Amazon Web Services AIP-C01 Dumps Questions Answers

Option A is the correct solution because hybrid search directly addresses the core retrieval failure modes while maintaining low latency and minimal operational overhead. In medical and scientific domains, exact terminology, abbreviations, and acronyms (for example, drug names, procedures, or conditions) are critical. Pure vector similarity search often underweights these exact matches, leading to missed results and excessive semantically related but irrelevant documents.

Amazon OpenSearch Service natively supports hybrid search, which combines keyword-based retrieval (such as BM25) with vector similarity search. Keyword search ensures precise matching for exact terms and acronyms, while vector search captures semantic meaning and contextual similarity. By blending these approaches, the retrieval system improves both precision and recall without introducing additional infrastructure.

Hybrid search operates within the same OpenSearch index and query path, which preserves low end-user latency even at large scale. This is especially important as the document collection grows to millions of documents. Because OpenSearch handles scoring and ranking internally, no additional orchestration layers or post-processing steps are required.

Option B increases computational cost and latency while failing to address exact-term recall. Option C introduces a new service and ingestion pipeline, increasing operational overhead and latency. Option D adds model hosting, re-ranking infrastructure, and complexity that is unnecessary when OpenSearch provides native hybrid retrieval.

Therefore, Option A delivers the best balance of retrieval quality, scalability, latency, and operational simplicity for medical RAG workloads.

Option B is the correct solution because Amazon Bedrock Prompt Management is purpose-built to manage, govern, and standardize prompt usage at scale across teams and Regions. It provides native version control, allowing teams to track prompt changes over time and ensure that only approved versions are used in production workflows.

Prompt Management supports approval workflows that align with enterprise governance requirements. Approval permissions can be enforced through IAM policies, ensuring that only authorized reviewers can approve or publish prompt versions. This removes the need for custom workflow engines or external storage systems, significantly reducing operational overhead.

Parameterized prompt templates enable consistent prompt structure while allowing controlled variation through defined variables. This ensures consistent quality standards and reduces prompt drift, which is critical when hundreds of prompts are reused across multiple applications and teams.

AWS CloudTrail integrates natively with Amazon Bedrock to provide immutable audit logs for prompt creation, updates, approvals, and usage. These detailed audit trails satisfy compliance requirements and allow security and governance teams to trace prompt activity across Regions and users.

Option A requires significant custom development to coordinate approvals and maintain state. Option C relies on general-purpose workflow services and manual versioning mechanisms that are error-prone and difficult to scale. Option D uses services not designed for large-scale GenAI prompt governance and introduces unnecessary complexity.

Therefore, Option B best meets the requirements for scalable, auditable, and low-overhead governance of AI-generated content using Amazon Bedrock.

Option B is the correct solution because Amazon Bedrock evaluation jobs are purpose-built to assess prompt effectiveness, model behavior, and response quality in a repeatable and automated manner. Evaluation jobs support both quantitative metrics and LLM-based judgment, making them suitable for detecting subtle response quality regressions that simple sentiment or latency metrics cannot capture.

By using custom prompt datasets, the company can consistently test multiple prompt templates and model configurations against the same inputs. This enables accurate comparison across updates and eliminates variability introduced by live traffic sampling. Amazon Bedrock evaluation jobs also support structured scoring outputs, which can be used to enforce objective quality thresholds.

Integrating evaluation jobs directly into AWS CodePipeline ensures that quality checks are automatically triggered whenever prompt templates or configurations change. This creates a gated deployment workflow in which only configurations that meet or exceed the predefined quality threshold are promoted. This directly satisfies the requirement to prevent low-quality configurations from being deployed.

Human reviewers can be incorporated by reviewing evaluation results and scores produced by the jobs, enabling informed feedback without manual data collection. Option A and D rely on custom frameworks and indirect quality signals, increasing complexity and reducing reliability. Option C focuses on operational health rather than response quality.

Therefore, Option B provides the most robust, scalable, and AWS-aligned quality assurance process for Amazon Bedrock–based applications.