AIF-C01 AWS Certified AI Practitioner Exam Questions and Answers

Chain-of-thought prompting is a prompt engineering technique where you instruct the model to explain its reasoning step by step, which is particularly useful for tasks involving logic, math, or reasoning.

A is correct: Asking the model to "explain its reasoning step by step" directly invokes chain-of-thought prompting, as documented in AWS and generative AI literature.

B is unrelated (prompt injection is a security concern).

C (few-shot) provides examples, but doesn’t specifically require step-by-step reasoning.

D (templating) is about structuring the prompt format.

"Chain-of-thought prompting elicits step-by-step explanations from LLMs, which improves performance on complex reasoning tasks."

(Reference: Amazon Bedrock Prompt Engineering Guide, AWS Certified AI Practitioner Study Guide)

The company is building a chatbot using an LLM on Amazon Bedrock, and the chatbot needs to use content from previous customer messages to resolve requests. Adding previous messages to the model prompt (also known as providing conversation history) enables the LLM to maintain context across interactions, allowing it to respond coherently based on the ongoing conversation.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"To enable a large language model (LLM) to maintain context in a conversation, you can include previous messages in the model prompt. This approach, often referred to as providing conversation history, allows the LLM to generate responses that are contextually relevant toprior interactions."

(Source: AWS Bedrock User Guide, Building Conversational Applications)

Detailed Explanation:

Option A: Turn on model invocation logging to collect messages.Model invocation logging records interactions for auditing or debugging but does not provide the LLM with access to previous messages during inference to maintain conversation context.

Option B: Add messages to the model prompt.This is the correct answer. Including previous messages in the prompt gives the LLM the conversation history it needs to respond appropriately, a common practice for chatbots on Amazon Bedrock.

Option C: Use Amazon Personalize to save conversation history.Amazon Personalize is for building recommendation systems, not for managing conversation history in a chatbot. This option is irrelevant.

Option D: Use Provisioned Throughput for the LLM.Provisioned Throughput in Amazon Bedrock ensures consistent performance for model inference but does not address the need to use previous messages in the conversation.

AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.

Exact Extract from AWS AI Documents:

From the AWS Documentation on Responsible AI:

"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment."

(Source: AWS Documentation, Responsible AI Governance)

Detailed Explanation:

Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.

Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.

Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.

Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.

Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.

Data Wrangler is for data preparation and transformation.

Transcribe is for speech-to-text.

Macie is for sensitive data discovery, not labeling.

???? Reference:

AWS Documentation – SageMaker Ground Truth Plus

Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from various providers, enabling users to build and scale generative AI applications. It simplifies the process of integrating FMs into applications for tasks like text generation, chatbots, and more.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI providers available through a single API, enabling developers to build and scale generative AI applications with ease."

(Source: AWS Bedrock User Guide, Introduction to Amazon Bedrock)

Detailed Explanation:

Option A: Amazon Q DeveloperAmazon Q Developer is an AI-powered assistant for coding and AWS service guidance, not a service for hosting or providing foundation models.

Option B: Amazon BedrockThis is the correct answer. Amazon Bedrock provides access to foundation models, making it the primary service for building and scaling generative AI applications.

Option C: Amazon KendraAmazon Kendra is an intelligent search service powered by machine learning, not a service for providing foundation models or building generative AI applications.

Option D: Amazon ComprehendAmazon Comprehend is an NLP service for text analysis tasks like sentiment analysis, not for providing foundation models or supporting generative AI.

Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.

Option A (Correct): "Real-time inference": This is the correct answer because it supports low-latency requirements, which are essential for real-time applications where quick response times are needed.

Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.

Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.

Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.

AWS AI Practitioner References:

Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

Amazon Q Developer is an AI-powered coding assistant integrated into IDEs (e.g., VS Code, JetBrains). It can:

Generate unit tests.

Create documentation.

Suggest code completions.

This is the fastest and most effective solution for this scenario.

???? Reference:

Amazon Q Developer – AWS Documentation

Amazon SageMaker Ground Truth Plus is a managed data labeling service that includes human-in-the-loop (HITL) validation. This solution ensures high accuracy by involving human reviewers to validate the annotations and reduce the risk of incorrect annotations.

Amazon SageMaker Ground Truth Plus:

It allows for the creation of high-quality training datasets with human oversight, which minimizes errors in labeling and increases accuracy.

Human-in-the-loop workflows help verify the correctness of annotations, ensuring that generated images for protective eyewear meet high-quality standards.

Why Option A is Correct:

High Accuracy: Human-in-the-loop validation provides the ability to catch and correct errors in annotations, ensuring high-quality data.

Minimized Risk of Incorrect Annotations: Human review adds a layer of quality assurance, which is especially important in use cases like generating precise images for protective eyewear.

Why Other Options are Incorrect:

B. Amazon Bedrock: Does not offer a knowledge base for data augmentation; it focuses on running foundation models.

C. Amazon Rekognition: Provides image recognition and analysis, not a solution for minimizing annotation errors.

D. Amazon QuickSight: A data visualization tool, not relevant to image annotation or generation tasks.

Thus, A is the correct answer for generating high-accuracy images with minimized annotation risks.

Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements. References: AWS SageMaker Model Cards Documentation.

Amazon Transcribe is the AWS service that converts speech to text, enabling the generation of subtitles (closed captions) for audio and video content automatically.

C is correct:

“Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications.”

This feature supports creating subtitles and transcripts for media files.

(Reference: Amazon Transcribe Overview, AWS AI Practitioner Official Study Guide)

A (Comprehend) is for NLP/text analytics.

B (Polly) is text-to-speech.

D (Translate) translates text, but does not create subtitles from audio/video.

Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).

If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.

Temperature (A) and Top P (C) control randomness, not input size.

Fine-tuning (B) is irrelevant to input truncation failures.

???? Reference:

AWS Documentation – Amazon Bedrock Model Parameters (context size limits)

Guardrails for Amazon Bedrock provide mechanisms to filter and control the content generated by models to comply with privacy and policy requirements. Using guardrails ensures that sensitive or personal information is not included in the model's responses. Additionally, integrating Amazon CloudWatch alarms allows for real-time notification when a policy violation occurs.

Option C (Correct): "Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations": This is the correct answer because it directly addresses both the prevention of policy violations and the requirement to receive notifications when such violations occur.

Option A: "Use Amazon Macie to scan the model's output for sensitive data" is incorrect because Amazon Macie is designed to monitor data in S3, not to filter real-time model outputs.

Option B: "Configure AWS CloudTrail to monitor the model's responses" is incorrect because CloudTrail tracks API activity and is not suited for content moderation.

Option D: "Implement Amazon SageMaker Model Monitor to detect data drift" is incorrect because data drift detection does not address content moderation or privacy compliance.

AWS AI Practitioner References:

Guardrails in Amazon Bedrock: AWS provides guardrails to ensure AI models comply with content policies, and using CloudWatch for alerting integrates monitoring capabilities.

Building and training a generative AI model from scratch provides the company with the most ownership and control over security responsibilities. In this scenario, the company is responsible for all aspects of the security of the data, the model, and the infrastructure.

Option D (Correct): "Building and training a generative AI model from scratch by using specific data that a customer owns": This is the correct answer because it involves complete ownership of the model, data, and infrastructure, giving the company the highest level of responsibility for security.

Option A: "Using a third-party enterprise application that has embedded generative AI features" is incorrect as the company has minimal control over the security of the AI features embedded within a third-party application.

Option B: "Building an application using an existing third-party generative AI foundation model (FM)" is incorrect because security responsibilities are shared with the third-party model provider.

Option C: "Refining an existing third-party generative AI FM by fine-tuning the model with business-specific data" is incorrect as the foundation model and part of the security responsibilities are still managed by the third party.

AWS AI Practitioner References:

Generative AI Security Scoping Matrix on AWS: AWS provides a security responsibility matrix that outlines varying levels of control and responsibility depending on the approach to developing and using AI models.

Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. References: AWS Machine Learning Best Practices.

Comprehensive and Detailed Explanation From Exact Extract:

In a RAG (Retrieval Augmented Generation) architecture, there are steps that can be optimized using offline batch processing, particularly for operations that do not require real-time updates:

A. Generation of content embeddings:When new content is published, it can be processed in batches to generate embeddings (vector representations) offline. These embeddings are then used at query time for similarity search. As new documents come in daily, batch processing is ideal for generating embeddings for all new content together.

“Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time.”

(Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern)

C. Creation of the search index:After generating the content embeddings, these are indexed in a vector database or search service. This indexing is also typically performed in batch as part of the offline pipeline.

“Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository.”

(Reference: AWS RAG Pattern Whitepaper)

B, D, and E are real-time steps. Embeddings for user queries (B), retrieval of relevant content (D), and response generation (E) must be processed in real-time to provide an interactive experience.

Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.

Fine-Tuning Strategy:

To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.

This allows the model to learn the desired behavior or response style based on the provided examples.

Why Option A is Correct:

Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.

Customization: Ensures that the model is fine-tuned to the specific requirements of the company’s data and desired outputs.

Why Other Options are Incorrect:

B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.

C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.

D. Train on journals and textbooks: Lacks the specific format and labeling required for fine-tuning.

To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.

Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.

Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.

Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.

Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.

Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.

AWS AI Practitioner References:

Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.

Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.

The correct answer is D because Amazon Bedrock provides access to foundation models (FMs) from various providers for generative AI use cases, including text generation. It supports generating content in a consistent tone, voice, or writing style using prompts or few-shot examples.

From AWS documentation:

"Amazon Bedrock allows you to build and scale generative AI applications using foundation models from AI21 Labs, Anthropic, Cohere, Meta, Mistral, Stability AI, and Amazon. These models can generate text with controlled tone and style for applications like branding, content creation, and copywriting."

Explanation of other options:

A. Amazon Comprehend is for natural language understanding, such as sentiment analysis and entity recognition, not generation.

B. Amazon Personalize is for building recommendation systems, not content generation.

C. Amazon Rekognition is for image and video analysis, not text generation.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Generative AI Use Cases

AWS Certified Machine Learning Specialty Guide – Content Generation with FMs

The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. References: AWS Foundation Models Guide.

The company needs to automatically group similar customers and products based on their characteristics, which is a clustering task. Unsupervised learning is the ML strategy for grouping data without labeled outcomes, making it ideal for this requirement.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Unsupervised learning is used to identify patterns or groupings in data without labeled outcomes. Common applications include clustering, such as grouping similar customers or products based on their characteristics, using algorithms like K-means or hierarchical clustering."

(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies)

Detailed Explanation:

Option A: Unsupervised learningThis is the correct answer. Unsupervised learning, specifically clustering, is designed to group similar entities (e.g., customers or products) based on their characteristics without requiring labeled data.

Option B: Supervised learningSupervised learning requires labeled data to train a model for prediction or classification, which is not applicable here since the task involves grouping without predefined labels.

Option C: Reinforcement learningReinforcement learning involves training an agent to make decisions through rewards and penalties, not for grouping data. This option is irrelevant.

Option D: Semi-supervised learningSemi-supervised learning uses a mix of labeled and unlabeled data, but the task here does not involve any labeled data, making unsupervised learning more appropriate.

The business objective to evaluate the effect of an LLM chatbot aimed at reducing the actions required by call center employees should be average call duration.

Average Call Duration:

This metric measures the time taken to handle a customer call or query. A successful LLM chatbot should reduce the call duration by efficiently providing answers, minimizing the need for human intervention.

By decreasing the average call duration, the company can improve call center efficiency, reduce costs, and enhance the user experience.

Why Option B is Correct:

Direct Impact: The objective aligns directly with the goal of reducing the number of actions call center employees must take.

Operational Efficiency: Reducing call duration is a clear indicator of the chatbot's effectiveness in assisting customers without human help.

Why Other Options are Incorrect:

A. Website engagement rate: Is unrelated to call center operations.

C. Corporate social responsibility: Does not relate to call center efficiency.

D. Regulatory compliance: Is important but does not measure the effectiveness of a chatbot in reducing employee actions.

Amazon SageMaker is AWS’s fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.

A is correct:

"Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy."

(Reference: Amazon SageMaker Overview)

B (Amazon Personalize) is for recommendations, not general ML or waste reduction.

C (CloudWatch) is for monitoring, not ML training or deployment.

D (Rekognition) is for image/video analysis.

Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.

Option C (Correct): "Create an Amazon Bedrock knowledge base": This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.

Option A: "Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.

Option B: "Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.

Option D: "Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.

AWS AI Practitioner References:

Amazon Bedrock and Knowledge Integration: AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM’s relevance and accuracy.

This scenario describes a classic supervised learning problem. Supervised learning involves training a model on a labeled dataset where the correct output (also called the target variable or ground truth) is already known. In this case, the company has a dataset containing features such as age, cholesterol, and exercise habits, and a label indicating whether a patient has heart disease or not. This matches the definition of a binary classification task, which is a type of supervised learning. According to the AWS Machine Learning Specialty Study Guide, supervised learning is most effective when labels are available for all training examples, and the goal is to map input variables to a known output. This method enables the model to learn patterns and predict the presence or absence of heart disease. By contrast, unsupervised learning is used when labels are not available, and reinforcement learning involves agents learning via rewards and punishments, which is not applicable here. Semi-supervised learning is used when only some data is labeled.

Referenced AWS AI/ML Documents and Study Guides:

AWS Certified Machine Learning Specialty Guide – Supervised vs. Unsupervised Learning

Amazon SageMaker Documentation – Classification Models

Amazon Comprehend toxicity detection is a managed NLP service that can analyze text for harmful or toxic language using pre-trained models—no need for labeled data or custom training.

B is correct: Comprehend’s toxicity detection API is designed for this use case, works out-of-the-box, and requires no data labeling or model training.

A (Rekognition) is for image and video content moderation.

C would require labeled data for training.

D (Polly) is for text-to-speech, not content moderation.

“Amazon Comprehend can detect toxicity in text with pre-trained models, requiring no labeled training data.”

(Reference: Amazon Comprehend Toxicity Detection, AWS AI Practitioner Official Guide)

The correct answer is A because code translation tasks require a model with deep understanding of syntax, semantic meaning, and the ability to maintain functional equivalence in different programming languages. Some foundation models offered via Amazon Bedrock (like CodeWhisperer or Meta’s Code LLMs) are designed with these capabilities in mind.

From AWS documentation:

"Generative AI models used for code generation and translation must understand programming semantics and syntax to generate accurate and secure code. These models are trained to recognize language-specific patterns and preserve logic when converting between languages."

Explanation of other options:

B. Speed and error handling are secondary to correctness and comprehension in code translation tasks.

C. Creative content generation is not relevant for deterministic tasks like source code translation.

D. Model size may affect performance or latency but is not a primary selection criterion for translation accuracy.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Model Selection Guide – Code Generation and Translation

AWS Developer Tools – CodeWhisperer Capabilities

AWS ML Specialty Study Guide – LLM Use Cases in Software Engineering

Step 1: Define the business objective.

Step 2: Process the data.

Step 3: Develop and train the model.

Step 4: Deploy the model.

The correct order represents the machine learning lifecycle as defined by AWS in the Amazon SageMaker documentation and AWS Certified Machine Learning Specialty Study Guide. The lifecycle describes the sequence of tasks required to build, train, and deploy a custom ML model effectively.

From AWS documentation:

"The machine learning process begins with defining the business problem, followed by collecting and processing data, developing and training models, and finally deploying them into production for inference."

Step 1 – Define the business objective:

This step involves clearly identifying the business problem to be solved and determining the measurable outcomes expected from the ML model. This ensures alignment between business goals and ML outputs.

Step 2 – Process the data:

Data is collected, cleaned, transformed, and prepared for training. This includes handling missing values, normalizing data, and performing feature engineering — a crucial phase that influences model performance.

Step 3 – Develop and train the model:

The model is built and trained on the processed data using algorithms appropriate to the problem (e.g., regression, classification, clustering). Hyperparameters are tuned to optimize model accuracy.

Step 4 – Deploy the model:

Once validated, the model is deployed to a production environment (e.g., Amazon SageMaker endpoint) to make predictions on new data. Continuous monitoring and retraining ensure the model remains effective.

Referenced AWS AI/ML Documents and Study Guides:

Amazon SageMaker Developer Guide – Machine Learning Lifecycle

AWS Certified Machine Learning Specialty Study Guide – Model Development Lifecycle

AWS ML Best Practices Whitepaper – End-to-End ML Workflow

Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model’s expected input format and optimize its performance for your use case. Prompt engineering is critical to ensure the model understands the task and generates accurate outputs."

(Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models)

Detailed Explanation:

Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.

Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.

Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.

Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.

This question maps responsible AI principles to specific AI system practices as defined in AWS Responsible AI Guidelines and Amazon Bedrock Responsible AI documentation.

Scenario 1:

Encrypt the application data, and isolate the application on a private network.

→ Principle: Privacy and Security

From AWS documentation:

"Protecting user data through encryption, secure network isolation, and access control aligns with the Responsible AI principle of privacy and security. AWS recommends securing all data used by AI systems, both in transit and at rest, to maintain trust and regulatory compliance."

Scenario 2:

Evaluate how different population groups will be impacted.

→ Principle: Fairness

From AWS documentation:

"The fairness principle ensures that AI models do not discriminate or generate biased outcomes across population groups. Fairness assessment involves evaluating performance metrics across demographic segments and mitigating any bias detected."

Scenario 3:

Test the application with unexpected data to ensure the application will work in unique situations.

→ Principle: Robustness

From AWS documentation:

"Robustness refers to an AI system’s ability to maintain reliable performance under varied, noisy, or unexpected input conditions. Testing for robustness helps ensure the model generalizes well and behaves safely in edge cases."

Referenced AWS AI/ML Documents and Study Guides:

AWS Responsible AI Practices Whitepaper – Core Principles of Responsible AI

Amazon Bedrock Documentation – Responsible AI and Safety Controls

AWS Certified Machine Learning Specialty Guide – AI Governance and Model Evaluation

Comprehensive and Detailed Explanation from AWS AI Documents:

Amazon Bedrock ensures data privacy and security by not sharing customer inputs or outputs with third-party model providers.

The models are accessed via Bedrock’s API isolation layer, meaning that model providers do not see your data.

Customer data is not used for training or improving foundation models unless customers explicitly opt in.

From AWS Docs:

“Amazon Bedrock does not share your inputs and outputs with third-party model providers. Your data remains private, and is not used to improve the foundation models.”

This ensures full data privacy, especially for sensitive use cases like confidential documents.

???? Reference:

AWS Documentation – Data privacy in Amazon Bedrock

Amazon Bedrock Knowledge Bases enable Retrieval Augmented Generation (RAG) by letting you connect external company data sources to your foundation model in a serverless, cost-effective manner—without retraining or fine-tuning. This allows the model to answer questions and generate content grounded in your own documents or data.

A is correct:

“Knowledge Bases for Amazon Bedrock enable generative AI applications to retrieve and include your company’s information for more contextual responses, without the need for expensive retraining or custom models.”

(Reference: Amazon Bedrock Knowledge Bases)

B is not cost-effective or guaranteed to add your company’s context.

C (Agents) are for orchestrating workflows, not specifically RAG/context.

D (Custom model deployment) is costly and unnecessary for just adding context.

Summarization is a natural language processing (NLP) task that condenses long documents into concise, meaningful summaries while retaining the key information.

Regression predicts numerical values.

Clustering groups similar items.

Classification assigns data into predefined categories.

???? Reference:

AWS NLP Use Cases – Summarization

Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.

Option A (Correct): "On-Demand": This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.

Option B: "Model customization" is a feature, not a pricing model.

Option C: "Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.

Option D: "Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.

AWS AI Practitioner References:

AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage patterns.

The correct answer is B because K-means is an unsupervised learning algorithm used for clustering data points into groups (clusters) based on feature similarity. It is ideal for customer segmentation use cases where the goal is to discover natural groupings based on buying behavior and demographics without pre-labeled data.

From AWS documentation:

"K-means is a clustering algorithm that assigns data points to one of K groups based on feature similarity. It is commonly used in marketing and customer segmentation to group users with similar characteristics."

Explanation of other options:

A. K-nearest neighbors is a supervised classification algorithm, not intended for clustering.

C. Decision tree is also a supervised learning method used for classification or regression tasks.

D. Support vector machine is used for classification and regression, not unsupervised clustering.

Referenced AWS AI/ML Documents and Study Guides:

AWS ML Specialty Guide – Unsupervised Learning and Clustering

AWS ML Algorithm Selection Guide

Amazon SageMaker Built-in Algorithms – K-means Clustering

The correct answer is A because Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically detect sensitive data such as PII (personally identifiable information) in Amazon S3. It requires no custom development, and it can be configured to generate alerts when sensitive data is detected in newly uploaded objects.

From AWS documentation:

"Amazon Macie automatically discovers and classifies sensitive data in S3 buckets and generates alerts when it detects sensitive content, such as names, addresses, and credit card numbers."

Explanation of other options:

B. Deploying an LLM on SageMaker to perform redaction is custom and operationally intensive.

C. Regex-based detection is brittle and requires extensive manual work, with high maintenance overhead.

D. Asking customers to avoid sharing sensitive data is not enforceable and does not meet compliance or security standards.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Macie Documentation – Sensitive Data Discovery and Alerting

AWS Security Best Practices – Data Privacy and Governance

AWS ML Specialty Guide – Governance and Compliance Automation

A multi-modal embedding model is the correct type of foundation model (FM) for powering a search application that handles queries containing both text and images.

Multi-Modal Embedding Model:

Can process and integrate different types of data (e.g., text and images) into a common representation space, enabling a unified search capability.

Suitable for applications where queries or content involve multiple data modalities.

Why Option A is Correct:

Handles Multiple Modalities: Supports both text and image data, aligning with the application's requirement.

Improves Search Relevance: Allows for more accurate and relevant search results across different types of input data.

Why Other Options are Incorrect:

B. Text embedding model: Only handles text data, not images.

C. Multi-modal generation model: Focuses on generating outputs rather than embedding for search tasks.

D. Image generation model: Only handles image data, not suitable for text queries.

The evaluation stage of the generative AI model lifecycle involves testing the model to assess its performance, including accuracy, coherence, and other metrics. This stage ensures the model meets the desired quality standards before deployment.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"The evaluation phase in the machine learning lifecycle involves testing the model against validation or test datasets to measure its performance metrics, such as accuracy, precision, recall, or task-specific metrics for generative AI models."

(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle)

Detailed Explanation:

Option A: DeploymentDeployment involves making the model available for use in production. While monitoring occurs post-deployment, accuracy testing is performed earlier in the evaluation stage.

Option B: Data selectionData selection involves choosing and preparing data for training, not testing the model’s accuracy.

Option C: Fine-tuningFine-tuning adjusts a pre-trained model to improve performance for a specific task, but it is not the stage where accuracy is formally tested.

Option D: EvaluationThis is the correct answer. The evaluation stage is where tests are conducted to examine the model’s accuracy and other performance metrics, ensuring it meets requirements.

Comprehensive and Detailed Explanation From Exact Extract:

The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a particular demographic), the recommended, cost-effective approach is to collect additional data from underrepresented groups and retrain (fine-tune) the model with the improved dataset.

A. Include more diverse training data. Fine-tune the model again by using the new data:

“The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model.”

(Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation)

B (RAG) is unrelated to model fairness or bias mitigation; it’s for grounding LLMs with external knowledge.

C (AWS Trusted Advisor) is for AWS resource optimization/security—not for ML model bias detection or mitigation.

D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much more efficient.

The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.

Context Window:

The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.

For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.

Why Option B is Correct:

Determines Prompt Size: The context window size directly informs how much information (e.g., words or sentences) can fit in one prompt.

Model Capacity: The larger the context window, the more information the model can consider for generating outputs.

Why Other Options are Incorrect:

A. Temperature: Controls randomness in model outputs but does not affect the prompt size.

C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.

D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.

Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.

Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.

Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.

Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.

Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.

AWS AI Practitioner References:

Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.

Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key": This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.

Option B: "Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.

Option C: "Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.

Option D: "Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.

AWS AI Practitioner References:

Managing Access to Encrypted Data in AWS: AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.

The correct answer is C because Amazon Nova Reel is the AWS foundation model designed for generative video use cases, providing end-to-end video generation using generative AI, which significantly reduces video creation time and eliminates the need for manual assembly.

According to AWS Bedrock documentation:

"Amazon Nova Reel enables users to generate short-form video content directly from prompts, including the ability to define style, motion, scenes, and transitions — streamlining the generative content creation process."

This is the most operationally efficient choice as it does not require stitching together images or using external editing tools.

Explanation of other options:

A and B involve generating intermediate images and then manually creating videos using video editing tools — not operationally efficient.

D. Amazon Nova Pro is intended for high-end professional-grade image or 3D content generation, but not specifically optimized for video generation like Nova Reel.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Model Directory – Nova Models Overview

AWS GenAI Foundation Model Comparison Guide

AWS Generative AI for Creators Whitepaper (2024)

Sentiment analysis is the task of determining the emotional tone or intent behind a body of text (positive, negative, neutral).

This falls under Natural Language Processing (NLP) because it deals with understanding and processing human language.

Computer vision relates to images, robotics to autonomous machines, and time series forecasting to predicting values from sequential data.

???? Reference:

AWS ML Glossary – NLP

With Amazon Bedrock, AWS handles infrastructure security and patching (shared responsibility model).

Customers are responsible for securing their data (encryption, IAM, policies) both in transit and at rest.

Provisioning infrastructure (D) and platform patching (A, B) are AWS responsibilities.

???? Reference:

AWS Shared Responsibility Model

The correct answer is D because Few-shot prompting involves providing the LLM with a few examples of the expected input-output format. This helps the model learn and mimic the pattern or structure required in the response — such as generating product descriptions that follow a specific template.

From AWS documentation:

"Few-shot prompting helps guide the model to produce structured and domain-specific outputs by supplying a small number of example inputs and corresponding outputs."

Explanation of other options:

A. Zero-shot prompting provides no examples, which may lead to inconsistent formatting.

B. Chain-of-thought prompting is used to guide reasoning steps, not formatting.

C. One-shot prompting uses a single example, but few-shot typically yields better structure adherence.

Referenced AWS AI/ML Documents and Study Guides:

AWS Prompt Engineering Guide

Amazon Bedrock Developer Documentation – Prompting Techniques

AWS ML Specialty Study Guide – LLM Prompting Patterns

Moderation APIs, such as Amazon Rekognition’s Content Moderation API, can help filter and block inappropriate or unwanted images from being returned by a chatbot. These APIs are specifically designed to detect and manage undesirable content in images.

Option A (Correct): "Implement moderation APIs": This is the correct answer because moderation APIs are designed to identify and filter inappropriate content, ensuring the chatbot does not return unwanted images.

Option B: "Retrain the model with a general public dataset" is incorrect because retraining does not directly prevent inappropriate content from being returned.

Option C: "Perform model validation" is incorrect as it ensures model correctness, not content moderation.

Option D: "Automate user feedback integration" is incorrect because user feedback does not prevent inappropriate images in real-time.

AWS AI Practitioner References:

AWS Content Moderation Services: AWS provides moderation APIs for filtering unwanted content from applications.

The correct answer is AWS Artifact, which is a central resource for accessing AWS compliance documentation, audit reports, and certifications from both AWS and third-party providers, including ISVs. According to AWS documentation, Artifact supports notifications for newly available reports. Customers can subscribe to notification emails when specific compliance documents or updates are published. This feature is ideal for regulated industries or companies needing third-party verification of compliance and security postures. Artifact provides access to reports such as SOC 2, ISO, PCI, and more, and is commonly used for audit preparation and vendor risk management. In contrast, AWS Audit Manager helps create internal audit frameworks but is not focused on third-party report distribution. AWS Data Exchange is for marketplace data sharing, and Trusted Advisor provides performance and cost optimization recommendations, not compliance document alerts. Thus, AWS Artifact is the purpose-built service that meets this requirement.

Referenced AWS AI/ML Documents and Study Guides:

AWS Artifact Documentation – Compliance Reports and Subscriptions

AWS Security and Compliance Whitepaper – Using Artifact for Third-Party Assurance

Explainability refers to the ability of an AI system to make its decision-making process clear and understandable to humans.

A is correct:

"Explainability is crucial for human-centered AI, especially in healthcare, to ensure that doctors and patients understand the rationale behind AI-driven recommendations."

(Reference: AWS Responsible AI)

B relates to protecting data, not explanations.

C is about treating groups equally.

D is about managing data lifecycle, not providing rationales.

The scenario where a student copies content from generative AI to write essays represents the challenge of plagiarism in responsible AI use.

Plagiarism:

Occurs when someone uses content generated by AI (or any source) without proper attribution, claiming it as their own.

This is a key challenge with generative AI models, which can produce human-like text that might be misused for academic or other purposes.

Why Option C is Correct:

Represents Unauthorized Use: Copying content directly from AI without attribution is a clear case of plagiarism.

Ethical Concern: Highlights the ethical considerations around using AI-generated content responsibly.

Why Other Options are Incorrect:

A. Toxicity: Refers to harmful or offensive content generation, not content copying.

B. Hallucinations: When AI generates incorrect or nonsensical information, not plagiarism.

D. Privacy: Involves the misuse or exposure of personal information, not copying content.

The scenario involves analyzing customer feedback and automatically classifying it into categories such as product quality, customer service, and delivery experience. This task requires processing and understanding textual data, which is a core application of natural language processing (NLP). NLP encompasses techniques for analyzing, interpreting, and generating human language, including tasks like text classification, sentiment analysis, and topic modeling, all of which are relevant to this use case.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Natural Language Processing (NLP) enables machines to understand and process human language. Common NLP tasks include text classification, sentiment analysis, named entity recognition, and topic modeling. Services like Amazon Comprehend can be used to classify text into predefined categories based on content."

(Source: AWS AI Practitioner Learning Path, Module on AI and ML Concepts)

Detailed Explanation:

Option A: Computer visionComputer vision involves processing and analyzing visual data, such as images or videos. Since the scenario deals with textual customer feedback, computer vision is not applicable.

Option B: Natural language processing (NLP)This is the correct answer. The task of classifying customer feedback into categories requires understanding and processing text, which is an NLP task. AWS services like Amazon Comprehend are specifically designed for such text classification tasks.

Option C: Recommendation systemsRecommendation systems suggest items or content based on user preferences or behavior. The scenario does not involve recommending products or services but rather classifying feedback, so this option is incorrect.

Option D: Fraud detectionFraud detection involves identifying anomalous or fraudulent activities, typically in financial or transactional data. The scenario focuses on text classification, not anomaly detection, making this option irrelevant.

The best solution is Amazon Bedrock Knowledge Bases, which allows for the seamless integration of structured and unstructured internal documents—such as PDFs, CSVs, and extracted image text—into a retrieval-augmented generation (RAG) pipeline. According to AWS documentation, Bedrock Knowledge Bases offer a no-code or low-code setup to link your enterprise data with foundation models for context-aware responses, without needing to fine-tune or retrain models. The system indexes documents in an Amazon S3 bucket, creates embeddings, and stores them in a vector store. At inference time, the model retrieves relevant context and incorporates it into its response. This approach provides dynamic and up-to-date responses while maintaining data privacy, with minimal operational overhead. Unlike fine-tuning or building a model from scratch in SageMaker, which requires considerable compute resources and model management, Bedrock Knowledge Bases are serverless and easy to configure. It is designed exactly for internal knowledge AI assistants.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Knowledge Bases

AWS Generative AI Best Practices – RAG Patterns for Enterprise Search

AWS PrivateLink enables private connectivity between VPCs and AWS services without exposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.

Option A (Correct): "AWS PrivateLink": This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards.

Option B: "Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic.

Option C: "Amazon CloudFront" is incorrect because it is a content delivery network service and does not provide private network connectivity.

Option D: "Internet gateway" is incorrect as it enables internet access, which violates the VPC's no-internet-traffic policy.

AWS AI Practitioner References:

AWS PrivateLink Documentation: AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizations with strict regulatory requirements.

Amazon SageMaker Canvas is a visual, no-code tool for building and deploying ML models. According to the official SageMaker Canvas documentation:

“SageMaker Canvas provides a visual point-and-click interface that allows business analysts to generate accurate ML predictions without writing any code.”

Amazon Nova Pro is a multimodal foundation model in Amazon Bedrock that supports text, images, and multiple languages.

Nova Lite is optimized for lightweight, faster inference at lower cost.

Nova Canvas is a creative tool for visual design.

Nova Reel is optimized for video-related use cases.

???? Reference:

AWS Documentation – Amazon Nova Models

The correct answer is B – Enable model invocation logging in Amazon Bedrock. AWS Bedrock provides native functionality to log all model invocations, including input prompts, parameters, and generated outputs, to Amazon CloudWatch Logs or S3. According to AWS documentation, this feature helps developers monitor model usage, analyze errors, and audit for compliance. Unlike CloudTrail or Audit Manager, which record API events and compliance data respectively, invocation logging captures real inference transactions. EventBridge is used for event routing, not persistent log storage. Bedrock’s logging feature ensures complete traceability for debugging, usage analytics, and governance in accordance with Responsible AI principles.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Model Invocation Logging

AWS Responsible AI Documentation – Monitoring and Auditability

The correct answer is B because Amazon SageMaker JumpStart provides pre-built solutions, including training workflows for popular open-source LLMs such as Falcon, LLaMA, and others. It allows practitioners to quickly launch fine-tuning jobs using predefined templates, minimizing operational setup and code complexity.

From AWS documentation:

"Amazon SageMaker JumpStart enables you to fine-tune and deploy foundation models with minimal setup. It provides easy-to-use interfaces and pre-built configurations for training, which significantly reduces the operational overhead required to train models."

Explanation of other options:

A. PartyRock is designed for prototyping generative AI apps but does not support model training or fine-tuning.

C. Writing a custom script for SageMaker training is flexible but involves more operational effort, including handling infrastructure configuration.

D. Training on EC2 via a Jupyter notebook is fully manual and operationally intensive, including dependency setup, data handling, and resource scaling.

Referenced AWS AI/ML Documents and Study Guides:

Amazon SageMaker JumpStart Developer Guide – Fine-tuning Foundation Models

AWS Certified Machine Learning Specialty Guide – Model Customization and JumpStart

Amazon SageMaker Ground Truth lets you create data labeling jobs and can integrate with Amazon Mechanical Turk (a distributed human workforce) to label large unlabeled datasets.

A (batch inference) applies models to already-trained data, not labeling.

B (PyTorch Distributed) is for distributed training, not labeling.

D (OCR) applies only to text extraction from images, not transactions.

E is incorrect; Ground Truth is the service for labeling, not "AI labeling job."

???? Reference:

AWS Documentation – SageMaker Ground Truth

The requirement is to identify an AWS database service that supports the storage and querying of embeddings (from a generative AI model) as vectors. Embeddings are typically high-dimensional numerical representations of data (e.g., text, images) used in AI applications like conversational search. The database must support vector storage and efficient vector similarity searches. Let’s evaluate each option:

A. Amazon Athena: Amazon Athena is a serverless query service for analyzing data in Amazon S3 using SQL. It is designed for ad-hoc querying of structured data but does not natively support vector storage or vector similarity searches, making it unsuitable for this use case.

B. Amazon Aurora PostgreSQL: Amazon Aurora PostgreSQL is a fully managed relational database compatible with PostgreSQL. With the pgvector extension (available in PostgreSQL and supported by Aurora PostgreSQL), it can store and query vector embeddings efficiently. The pgvector extension enables vector similarity searches (e.g., using cosine similarity or Euclidean distance), which is critical for conversational search applications using embeddings from generative AI models.

C. Amazon Redshift: Amazon Redshift is a data warehousing service optimized for analytical queries on large datasets. While it supports machine learning features and can store numerical data, it does not have native support for vector embeddings or vector similarity searches as of May 17, 2025, making it less suitable for this use case.

D. Amazon EMR: Amazon EMR is a managed big data platform for processing large-scale data using frameworks like Apache Hadoop and Spark. It is not a database service and is not designed for storing or querying vector embeddings in the context of a conversational search application.

Exact Extract Reference: According to the AWS documentation, “Amazon Aurora PostgreSQL-Compatible Edition supports the pgvector extension, which enables efficient storage and similarity searches for vector embeddings. This makes it suitable for AI/ML workloads such as natural language processing and recommendation systems that rely on vector data.” (Source: AWS Aurora Documentation - Using pgvector with Aurora PostgreSQL, https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/PostgreSQLpgvector.html). Additionally, the pgvector extension supports operations like nearest-neighbor searches, which are essential for querying embeddings in a conversational search system.

Amazon Aurora PostgreSQL with the pgvector extension directly meets the requirement for storing and querying embeddings as vectors, making B the correct answer.

SageMaker Clarify provides model explainability, bias detection, and helps visualize how input features affect predictions.

Canvas is for low-code ML building.

Feature Store is for storing and managing ML features.

Ground Truth is for data labeling.

???? Reference:

AWS Documentation – Amazon SageMaker Clarify

Class imbalance in a training dataset can cause ML models to favor overrepresented groups, leading to biased predictions. The AWS AI Practitioner guide and SageMaker Clarify documentation emphasize the need to identify and mitigate class imbalance to ensure fairness and unbiased model outcomes.

D is correct: By measuring class imbalance and adapting the training process (e.g., through oversampling, undersampling, or using class weights), organizations can improve fairness and reduce bias across demographic groups.

A (reducing data size) could worsen bias by removing potentially useful diverse data.

B (consistency with historical results) might reinforce existing biases.

C (separate models) is not scalable and can introduce other fairness issues.

“To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented.”

(Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)

To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.

A. Include Fairness Metrics for Model Evaluation:

Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.

These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.

C. Modify the Training Data to Mitigate Bias:

Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.

Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.

Why Other Options are Incorrect:

B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.

D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.

E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.

Exact Extract from AWS AI Documents:

From the Amazon SageMaker Developer Guide:

"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations."

(Source: Amazon SageMaker Developer Guide, SageMaker Model Cards)

Detailed Explanation:

Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.

Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.

Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team’s requirements.

Option D: Create model training scripts. Commit the model training scripts to a Git repository.Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): "Number of tokens consumed": This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B: "Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.

Option C: "Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.

Option D: "Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner References:

Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.

The correct answer is A because model interpretability refers to the ability to understand and explain how an ML model arrives at a particular prediction or decision.

From AWS documentation:

"Model interpretability is the degree to which a human can understand the cause of a decision made by a machine learning model. Interpretability techniques help explain which features influenced a prediction and how much they contributed."

This is essential in areas like financial services, healthcare, or compliance-heavy industries, where decision transparency is critical.

Explanation of other options:

B. Model training refers to the process of teaching a model from data and doesn’t explain how predictions are made.

C. Model interoperability refers to the ability of systems or models to work across different platforms or environments.

D. Model performance refers to how accurate or effective the model is but doesn’t relate to the explanation of its decisions.

Referenced AWS AI/ML Documents and Study Guides:

Amazon SageMaker Clarify Documentation – Explainability and Bias Detection

AWS Machine Learning Specialty Study Guide – Responsible AI and Model Explainability

Amazon SageMaker Clarify provides built-in tools to detect bias in data and models, and to generate detailed explainability reports for model predictions, including SHAP values and feature importance.

A is correct:

“Amazon SageMaker Clarify provides bias detection, explainability for ML models, and comprehensive reports to satisfy regulatory and ethical requirements.”

(Reference: Amazon SageMaker Clarify Overview)

B (Data Wrangler) is for data preparation, not bias/explainability.

C (Model Cards) document models, but don’t detect bias or explain predictions.

D (AI Service Cards) provide transparency for AWS AI services, not custom model explainability.

The average response time is the correct metric for measuring the runtime efficiency of operating AI models.

Average Response Time:

Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.

A lower average response time indicates a more efficient model that can handle queries quickly.

Why Option C is Correct:

Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.

Performance Indicator: Helps identify potential bottlenecks and optimize model performance.

Why Other Options are Incorrect:

A. Customer satisfaction score (CSAT): Measures customer satisfaction, not model runtime efficiency.

B. Training time for each epoch: Measures training efficiency, not runtime efficiency during model operation.

D. Number of training instances: Refers to data used during training, not operational efficiency.

To manage the flow of data from Amazon S3 to SageMaker Studio notebooks securely, using a VPC with an S3 endpoint is the best solution.

Amazon SageMaker and S3 Integration:

Configuring SageMaker to use a Virtual Private Cloud (VPC) with an S3 endpoint allows the data flow between Amazon S3 and SageMaker Studio notebooks to occur over a private network.

This setup ensures that traffic between SageMaker and S3 does not traverse the public internet, enhancing security and performance.

Why Option C is Correct:

Secure Data Transfer: Ensures secure, private connectivity between SageMaker and S3, reducing exposure to potential security risks.

Direct Access to S3: Using an S3 endpoint in a VPC allows direct access to data in S3 without leaving the AWS network.

Why Other Options are Incorrect:

A. Amazon Inspector: Focuses on identifying security vulnerabilities, not managing data flow.

B. Amazon Macie: Monitors for sensitive data but does not manage data flow between S3 and SageMaker.

D. S3 Glacier Deep Archive: Is a storage class for archiving data, not for managing active data flow.

Step 1: Store meeting audio recordings in an Amazon S3 bucket.

Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.

Step 3: Summarize meeting text files by using Amazon Bedrock.

The company wants to create an application to summarize meeting audio recordings, which requires a sequence of steps involving storage, speech-to-text conversion, and text summarization. Amazon S3 is the recommended storage service for audio files, Amazon Transcribe converts audio to text, and Amazon Bedrock provides generative AI capabilities for summarization. These three steps, in this order, create an efficient workflow for the application.

Exact Extract from AWS AI Documents:

From the Amazon Transcribe Developer Guide:

"Amazon Transcribe uses deep learning to convert audio files into text, supporting applications such as meeting transcription. Audio files can be stored in Amazon S3, and Transcribe can process them directly from an S3 bucket."

From the AWS Bedrock User Guide:

"Amazon Bedrock provides foundation models that can perform text summarization, enabling developers to build applications that generate concise summaries from text data, such as meeting transcripts."

(Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe; AWS Bedrock User Guide, Text Generation and Summarization)

Detailed Explanation:

Step 1: Store meeting audio recordings in an Amazon S3 bucket.Amazon S3 is the standard storage service for audio files in AWS workflows, especially for integration with services like Amazon Transcribe. Storing the recordings in S3 allows Transcribe to access and process them efficiently. This is the first logical step.

Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.Amazon Transcribe is designed for automatic speech recognition (ASR), converting audio files (stored in S3) into text. This step is necessary to transform the meeting recordings into a format that can be summarized.

Step 3: Summarize meeting text files by using Amazon Bedrock.Amazon Bedrock provides foundation models capable of generative AI tasks like text summarization. Once the audio is converted to text, Bedrock can summarize the meeting transcripts, completing the application’s requirements.

Unused Options Analysis:

Convert meeting audio recordings to meeting text files by using Amazon Polly.Amazon Polly is a text-to-speech service, not for converting audio to text. This option is incorrect and not used.

Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.Amazon EBS is for block storage, typically used for compute instances, not for storing files for processing by services like Transcribe. S3 is the better choice, so this option is not used.

Summarize meeting text files by using Amazon Lex.Amazon Lex is for building conversational interfaces (chatbots), not for text summarization. Bedrock is the appropriate service for summarization, so this option is not used.

Hotspot Selection Analysis:

The task requires selecting and ordering three steps from the list, with each step used exactly once or not at all. The selected steps—storing in S3, converting with Transcribe, and summarizing with Bedrock—form a complete and logical workflow for the application.

The correct answer is Amazon Bedrock, AWS’s fully managed service for building and scaling generative AI applications using foundation models (FMs). Bedrock gives developers access to models from leading providers such as Anthropic (Claude), Meta (Llama), Mistral, Cohere, and Amazon Titan. Users can invoke these models via API without managing infrastructure or model training. According to AWS documentation, Bedrock supports tasks such as text generation, summarization, question answering, image generation, and RAG workflows with minimal setup. It supports both on-demand and provisioned throughput modes and integrates with features like Guardrails, Knowledge Bases, and Agents for secure, enterprise-grade applications. Amazon Q Developer is a generative AI tool for developers, but it doesn’t host or scale models. Amazon Kendra is an intelligent search engine, and Amazon Comprehend is used for NLP tasks like entity extraction—not foundation model access.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Foundation Models and Use Cases

AWS Certified ML Specialty Guide – Generative AI on AWS

A prompt template defines how the model is structured and guided (system prompts, roles, guardrails).

An attack that reveals or leaks this prompt template is known as a prompt extraction attack.

The other options (persona switching, exploiting friendliness, ignoring prompts) describe adversarial techniques but do not directly expose the internal configured behavior.

???? Reference:

AWS Responsible AI – Prompt Injection & Extraction Attacks

Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to answer queries based on context provided in product manuals. To achieve this cost-effectively, the company should avoid unnecessary use of resources.

Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock": This is the most cost-effective solution. By using prompt engineering, only the relevant content from one PDF file is added as context to each query. This approach minimizes the amount of data processed, which helps in reducing costs associated with LLMs' computational requirements.

Option B: "Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs significantly due to the large context size processed by the model.

Option C: "Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine-tuning a model is more expensive than using prompt engineering, especially if done for multiple documents.

Option D: "Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying PDF documents.

AWS AI Practitioner References:

Prompt Engineering for Cost-Effective AI: AWS emphasizes the importance of using prompt engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context, users can reduce the amount of data processed and save on expenses.

Measuring the model's accuracy against a predefined benchmark dataset is the correct strategy to evaluate the accuracy of a foundation model (FM) used in image classification tasks.

Model Accuracy Evaluation:

In image classification, the accuracy of a model is typically evaluated by comparing the predicted labels with the true labels in a benchmark dataset that is representative of the real-world data the model will encounter.

This approach provides a quantifiable measure of how well the model performs on known data and is a standard practice in machine learning.

Why Option B is Correct:

Benchmarking Accuracy: Using a predefined dataset allows for consistent and reliable evaluation of model performance.

Standard Practice: It is a widely accepted method for assessing the effectiveness of image classification models.

Why Other Options are Incorrect:

A. Total cost of resources: Does not measure model accuracy but rather the cost of operation.

C. Number of layers in the neural network: Does not directly correlate with the accuracy or performance of the model.

D. Color accuracy of images processed by the model: Is unrelated to the model’s classification accuracy.

Fine-tuning requires paired input–output examples to teach the model how to respond to inputs with desired styled outputs.

Single inputs (A) or outputs (B) are insufficient.

Separate, unpaired samples (D) don’t establish the input-output mapping.

???? Reference:

AWS Documentation – Preparing data for fine-tuning FMs

The correct answer is B because Amazon CloudWatch provides monitoring capabilities that include tracking usage metrics for Amazon Comprehend endpoints, such as invocation counts. You can configure CloudWatch to collect these metrics and create custom dashboards or alarms to report when an endpoint has zero usage over a period (e.g., 15 days).

From AWS documentation:

"Amazon CloudWatch enables you to collect and track metrics for Comprehend endpoints, create alarms, and automatically react to changes in your AWS resources."

This allows automated reporting and alerting for underused or idle endpoints.

Explanation of other options:

A. AWS Trusted Advisor focuses on general AWS resource optimization, security, and limits but does not track endpoint usage.

C. AWS CloudTrail tracks API calls but does not provide time-based monitoring or usage analysis over time.

D. AWS Config is used to track configuration changes and compliance, not endpoint usage metrics.

Referenced AWS AI/ML Documents and Study Guides:

Amazon CloudWatch Metrics for Amazon Comprehend

AWS Certified Machine Learning Specialty Guide – Monitoring and Logging Section

AWS Cloud Operations Guide – Resource Utilization Monitoring

Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks.

Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms": This is the correct answer because fine-tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance.

Option A: "Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms.

Option C: "Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology.

Option D: "Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution.

AWS AI Practitioner References:

Domain Adaptation in Amazon Bedrock: AWS recommends fine-tuning models with domain-specific data to improve their performance on specialized tasks involving unique terminology.

To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer resources and havefaster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.

Option A (Correct): "Deploy optimized small language models (SLMs) on edge devices": This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.

Option B: "Deploy optimized large language models (LLMs) on edge devices" is incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.

Option C: "Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.

Option D: "Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.

AWS AI Practitioner References:

Optimizing AI Models for Edge Devices on AWS: AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.

Jailbreaking an LLM refers to deliberately crafting prompts to bypass its safety guardrails and produce restricted or harmful content.

Fuzzing is input testing for software.

DoS is a service availability attack.

Pen testing (C) is allowed with AWS authorization but doesn’t describe bypassing model safeguards.

???? Reference:

AWS Responsible AI – Prompt Injection and Jailbreak Risks