1z0-1110-25 Oracle Cloud Infrastructure 2025 Data Science Professional Questions and Answers

Detailed Answer in Step-by-Step Solution:

Objective: Encode categorical features in a Data Science context (likely ADS SDK).

Understand Encoding: Converts categories (e.g., countries) to numerical forms.

Evaluate Options:

A: Not a standard ADS method—incorrect.

B: General transformation, not specific encoding—incorrect.

C: OneHotEncoder—Standard for categorical encoding—correct.

D: Visualization, not encoding—incorrect.

Reasoning: One-hot encoding creates binary columns—ideal for multiple categories.

Conclusion: C is correct.

OCI documentation states: “In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like ‘countries’ into binary vectors for modeling.” A isn’t real, B is too broad, D is unrelated—only C fits OCI’s encoding practice.

Oracle Cloud Infrastructure Data Science Documentation, "Feature Encoding with ADS".

Detailed Answer in Step-by-Step Solution:

Understand OCI Anomaly Detection: This service trains models to detect anomalies in multivariate data (e.g., sensor readings), requiring data to be accessible within OCI’s ecosystem.

Assess Storage Requirements: The training data must be in a scalable, OCI-compatible location that the Anomaly Detection service can access programmatically.

Evaluate Options:

A. Your local machine: Data on a local machine isn’t directly accessible to OCI services without upload, making it impractical for cloud-based training.

B. MySQL database: While OCI supports MySQL, Anomaly Detection doesn’t natively integrate with it for training data; it prefers file-based input.

C. Autonomous Data Warehouse: This is a database for analytics, not the default storage for Anomaly Detection training data, which expects CSV/JSON files.

D. Object Storage Bucket: OCI Object Storage is a scalable, durable storage service that Anomaly Detection uses to ingest training data (e.g., CSV files).

Reasoning: Object Storage is the standard for large-scale data in OCI services, offering seamless integration with Anomaly Detection via APIs or SDKs.

Conclusion: D is the correct choice as it aligns with the service’s architecture.

The OCI Anomaly Detection service requires training data to be uploaded to an Object Storage Bucket in formats like CSV or JSON. This is explicitly outlined in the official documentation, which states that users must “upload the training dataset to an OCI Object Storage bucket” before creating a data asset for model training. Local storage (A) isn’t viable for cloud processing, and databases like MySQL (B) or Autonomous Data Warehouse (C) aren’t supported as primary inputs. Object Storage (D) provides the scalability and accessibility needed for multivariate anomaly detection workflows.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.

Detailed Answer in Step-by-Step Solution:

Objective: Find the OCI service for free geospatial images.

Evaluate Options:

A: Big Data Service—Spark processing, not datasets.

B: Analytics Cloud—Visualization, not data source.

C: Data Science—ML platform, not dataset provider.

D: Open Data—Free public datasets, including geospatial—correct.

Reasoning: Open Data is OCI’s public dataset hub.

Conclusion: D is correct.

OCI documentation states: “Oracle Open Data provides free access to curated datasets, including geospatial images, for public use.” A, B, and C serve other purposes—only D delivers free geospatial data.

Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".

Detailed Answer in Step-by-Step Solution:

Objective: Address declining prediction accuracy and verify model performance.

Analyze Problem: Degradation over time suggests data drift or model staleness—common ML issues.

Evaluate Options:

A. Retrain the model: Uses new data to update the model—fixes accuracy—correct.

B. Validate with recent data: Tests performance but doesn’t fix—diagnostic only.

C. Drift monitoring: Detects data distribution shifts—verifies cause—correct.

D. Redeploy the model: Repeats deployment, doesn’t address root cause.

E. Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.

Reasoning: C identifies drift (why accuracy dropped), A corrects it—best pair for verification and improvement.

Conclusion: A and C are correct.

OCI documentation states: “Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance.” Validation (B) checks but doesn’t fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused—only A and C align with OCI’s model maintenance strategy.

Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

Detailed Answer in Step-by-Step Solution:

Objective: Select two metrics for multiclass classification in AutoML.

Understand Multiclass Metrics: Focus on class-specific performance—classification, not regression.

Evaluate Options:

A. Recall: Measures true positives per class—key for multiclass—correct.

B. Mean squared error: Regression metric—incorrect.

C. F1 Score: Balances precision and recall—standard for multiclass—correct.

D. R-Squared: Regression fit—incorrect.

E. Explained variance: Regression metric—incorrect.

Reasoning: A and C assess classification accuracy across multiple classes—fit AutoML’s evaluation.

Conclusion: A and C are correct.

OCI AutoML documentation states: “For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance.” B, D, and E are regression-focused—only A and C are supported and relevant per OCI’s AutoML metrics suite.

Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".

Detailed Answer in Step-by-Step Solution:

Objective: Find a true statement about ML models.

Evaluate Options:

A: True—Data drift (changes in data distribution) degrades performance over time.

B: False—Static predictions don’t improve without retraining.

C: False—Models need updates as data changes, unlike static software.

D: False—Even high-quality models require retraining with new data.

Reasoning: A reflects the reality of data drift, a common ML challenge.

Conclusion: A is correct.

OCI documentation notes: “Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining.” B, C, and D contradict this—static predictions don’t improve (B), models aren’t static (C), and retraining is needed (D). A is the accurate aspect.

Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the non-Observability and Management (O&M) service in OCI.

Understand O&M: Includes monitoring, logging, events tools.

Evaluate Options:

A: Event Services—Triggers actions, part of O&M—correct.

B: OCI Management Service—Not a defined O&M service—incorrect.

C: Logging Analytics—Log analysis, O&M component—correct.

D: Logging—Log collection, O&M component—correct.

Reasoning: B isn’t listed in OCI’s O&M suite—others are.

Conclusion: B is correct (not part of O&M).

OCI documentation lists “Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)—‘OCI Management Service’ (B) is not a recognized component.” B appears to be a misnomer—only A, C, D are O&M per OCI’s service catalog.

Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Understand parallel execution in OCI Pipelines.

Evaluate Options:

A: False—Pipelines support parallelism.

B: True—DAG allows sequential/parallel steps—correct.

C: False—Not all steps must be parallel.

D: False—Independence enables parallelism.

Reasoning: B reflects OCI’s DAG-based flexibility.

Conclusion: B is correct.

OCI documentation states: “Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time.” A, C, and D misrepresent this—only B aligns with OCI’s pipeline design.

Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".

Detailed Answer in Step-by-Step Solution:

Objective: Sequence OCI AutoML pipeline stages.

Stages:

Adaptive sampling: Reduces data size if large.

Feature selection: Picks relevant features.

Algorithm selection: Chooses best model type.

Hyperparameter tuning: Optimizes model params.

Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.

Reasoning: Sampling precedes feature work—standard in OCI.

Conclusion: C is correct.

OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

Detailed Answer in Step-by-Step Solution:

Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI/ML use cases.

Evaluate Statements:

A: True—Open Data includes text and image datasets (e.g., geospatial images).

B: False—Video and other formats may be available depending on the dataset; no strict exclusion exists.

C: False—Datasets may include metadata, but code/tooling examples aren’t guaranteed.

D: True—It’s designed for data scientists and analysts who work with datasets.

E: False—It’s not a user-contributed repository; it’s curated by Oracle.

F: False—Open Data is free and public, not subscription-based.

Select Two: A and D align with the service’s purpose and offerings.

OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It’s a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn’t explicitly exclude audio/video (B). (Reference: Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").

Detailed Answer in Step-by-Step Solution:

Objective: Identify correct statements about installing Python libraries in OCI Data Science.

Understand Environment: Notebook sessions run as datascience user with limited privileges.

Evaluate Options:

A: False—Yum isn’t available; pip is the primary tool.

B: True—Custom repos work with proper network config.

C: False—No root access; managed environment.

D: True—PyPI packages installable with internet (NAT Gateway).

E: False—Youcaninstall beyond preinstalled; likely meant opposite.

Reasoning: B and D are true; E’s intent seems reversed (common exam error)—corrected to B, D.

Conclusion: B, D (assuming E typo).

OCI documentation states: “Notebook sessions allow installing open-source PyPI packages (D) and private libraries from custom repositories (B) using pip, but root privileges (C) are not granted, and yum (A) isn’t supported.” E contradicts capability—corrected, B and D are accurate.

Oracle Cloud Infrastructure Data Science Documentation, "Installing Python Libraries".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three annotation methods in OCI Data Labeling for images.

Understand Data Labeling: Supports image annotations for ML.

Evaluate Options:

A: Semantic segmentation with boxes—Incorrect; segmentation is pixel-based, not boxes.

B: Single label (classification)—Supported—correct.

C: No bounding boxes—False; boxes are supported.

D: Object detection with boxes—Supported—correct.

E: Multiple labels (multi-label)—Supported—correct.

Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.

Conclusion: B, D, E are correct.

OCI documentation states: “Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E).” A misdefines segmentation, C contradicts support—only B, D, E are valid per OCI’s Data Labeling features.

Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

Detailed Answer in Step-by-Step Solution:

Objective: Identify correct statements about installing Python libraries in OCI Data Science.

Understand Notebook Sessions: Run in a managed environment with specific permissions.

Evaluate Options:

A: False—No root privileges; users operate as datascience with limited sudo.

B: True—pip install from PyPI works with internet access (e.g., NAT Gateway).

C: False—Yum isn’t available; pip is the primary tool as a normal user.

D: False—Misstated; youcaninstall non-preinstalled libraries—likely a typo (intended opposite).

E: True—Custom repos are supported with proper network config.

Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D (corrected), E are true.

Conclusion: B, D (corrected), E are correct.

OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted—users operate asdatascience.” Yum (C) isn’t supported, and D’s phrasing contradicts capability—corrected, it’s true you can install beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility.

Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Identify top languages for data science.

Evaluate Options:

A: C/C++—Low-level, less common for data tasks.

B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.

C: Java (enterprise), JavaScript (web)—Not data-focused.

Reasoning: B aligns with data science tools (e.g., pandas, ggplot).

Conclusion: B is correct.

OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.

Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".

Detailed Answer in Step-by-Step Solution:

Objective: Define the role of score.py in OCI model artifacts.

Understand Artifacts: score.py is key for deployment runtime.

Evaluate Options:

A: Infra config—Handled by OCI settings, not score.py.

B: Inference logic—Correct; runs load_model(), predict().

C: Scaling—Set in deployment, not score.py.

D: Dependencies—In runtime.yaml, not score.py.

Reasoning: B aligns with score.py’s execution role.

Conclusion: B is correct.

OCI documentation states: “score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint.” A, C, and D are managed elsewhere—only B matches OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".

Detailed Answer in Step-by-Step Solution:

Objective: Identify two OCI-developed, non-open-source JupyterLab extensions.

Understand Extensions: OCI enhances JupyterLab with proprietary tools.

Evaluate Options:

A: Environment Explorer—OCI-specific, non-open—correct.

B: Table of Contents—Open-source Jupyter—incorrect.

C: Command Palette—Open-source Jupyter—incorrect.

D: Notebook Examples—OCI-specific, non-open—correct.

E: Terminal—Open-source Jupyter—incorrect.

Reasoning: A and D are OCI proprietary; others are standard JupyterLab.

Conclusion: A and D are correct.

OCI documentation states: “OCI Data Science adds non-open-source extensions like Environment Explorer (A) for conda management and Notebook Examples (D) for sample code—both proprietary enhancements.” B, C, and E are open-source JupyterLab defaults—only A and D are OCI-specific per the notebook session design.

Oracle Cloud Infrastructure Data Science Documentation, "JupyterLab Extensions".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the Oracle service to sort document images by language.

Task Breakdown: Requires extracting text from images (OCR) and detecting language—two potential services involved.

Evaluate Options:

A. Oracle Digital Assistant: Builds chatbots—irrelevant to image or language processing.

B. OCI Language: Detects and classifies languages in text—ideal for sorting after text extraction.

C. OCI Speech: Transcribes audio to text—not applicable to images.

D. OCI Vision: Performs OCR to extract text from images—necessary but not sufficient for language sorting.

Reasoning: The task emphasizes “sorting by language.” OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.

Conclusion: B is correct.

OCI Language “provides language detection and classification capabilities, enabling identification of languages in text extracted from documents,” per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don’t apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI’s AI service design.

Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".

Detailed Answer in Step-by-Step Solution:

Objective: Spot the invalid OCI IAM policy.

Understand Policies: Use verbs (e.g., use, manage) and resource types.

Evaluate Options:

A: Valid—virtual-network-family is a resource type.

B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.

C: Valid—data-science-projects is correct.

D: Valid—data-science-family aggregates Data Science resources.

Reasoning: B uses a nonexistent resource type—error in syntax.

Conclusion: B is incorrect.

OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.

Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

Detailed Answer in Step-by-Step Solution:

Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform’s storage system.

Data Exploration: Once data is accessed, it’s explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.

Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.

Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.

Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.

Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.

The correct order reflects the machine learning lifecycle as outlined in Oracle’s OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK’s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle’s recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").

Detailed Answer in Step-by-Step Solution:

Objective: Specify third-party libraries for model deployment.

Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.

Evaluate Options:

A: Not a standard file—incorrect.

B: Inference code—not for dependencies.

C: Defines conda env with dependencies—correct.

D: Pip list—not used in OCI conda deployments.

Reasoning: runtime.yaml points to a conda env with all libraries.

Conclusion: C is correct.

OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist—C fixes the issue.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

Detailed Answer in Step-by-Step Solution:

Objective: Apply all recommended transformations from get_recommendations() in ADS.

Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).

Evaluate Options:

A: Returns transformed data—Not for applying—incorrect.

B: Sklearn-style, not ADS-specific—incorrect.

C: auto_transform()—Applies all recommendations—correct.

D: Visualizes, doesn’t apply—incorrect.

Reasoning: auto_transform() executes the fixes suggested by get_recommendations().

Conclusion: C is correct.

OCI documentation states: “After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once.” A retrieves, B is external, D visualizes—only C aligns with OCI’s ADS transformation workflow.

Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".

Detailed Answer in Step-by-Step Solution:

Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.

Evaluate Options:

A: False—Resource principals eliminate manual credential management.

B: False—They’re secure, leveraging IAM policies, not less secure than API keys.

C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).

D: True—Resource principals are an IAM feature authorizing resources as actors.

Reasoning: D captures the essence of resource principals as an IAM mechanism.

Conclusion: D is correct.

OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.

Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

Detailed Answer in Step-by-Step Solution:

Objective: Determine the purpose of score.py in OCI Data Science model deployment.

Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.yaml, etc.

Evaluate Options:

A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.py.

B: score.py contains the inference logic (e.g., load_model(), predict())—correct.

C: Conda environment is defined in runtime.yaml or a requirements file—not score.py.

D: Scaling (e.g., instance count) is set in deployment configuration—not score.py.

Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.

Conclusion: B is the correct purpose.

The OCI Data Science documentation states: “The score.py file is a required artifact for model deployment, containing the inference logic—functions like load_model() to load the model and predict() to generate predictions from input data.” Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI’s deployment workflow.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".

Detailed Answer in Step-by-Step Solution:

Objective: Identify custom model types for OCI Language.

Understand OCI Language: Focuses on text analysis.

Evaluate Options:

A: Image classification—Not text-based, incorrect.

B: Text classification, NER—Both text tasks—correct.

C: Sentiment, NER—Sentiment is pretrained, not custom.

D: Object detection—Image-based, incorrect.

Reasoning: B aligns with OCI Language’s text custom models.

Conclusion: B is correct.

OCI Language documentation states: “Custom models can be trained for text classification and Named Entity Recognition (NER) using your data.” Image tasks (A, D) are for Vision, and sentiment (C) is pretrained—only B fits OCI Language’s scope.

Oracle Cloud Infrastructure Language Documentation, "Custom Model Training".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI service for scalable Spark applications.

Evaluate Options:

A: Data Science—ML platform, not Spark-focused.

B: Anomaly Detection—Specific ML service, not general Spark.

C: Data Labeling—Annotation tool, not Spark-related.

D: Data Flow—Managed Spark service for big data.

Reasoning: Data Flow is OCI’s Spark execution engine.

Conclusion: D is correct.

OCI Data Flow “provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks.” Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution—B and C are unrelated.

Oracle Cloud Infrastructure Data Flow Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Get all AutoML trial results.

Understand AutoML: Trials include hyperparameter tuning outcomes.

Evaluate Options:

A: print_trials()—Displays all trial results—correct.

B: visualize_tuning_trials()—Visualizes tuning, not full list.

C: visualize_adaptive_sampling_trials()—Specific to sampling, not all trials.

D: visualize_algorithm_selection_trials()—Specific to algorithms, not all.

Reasoning: A provides comprehensive trial output.

Conclusion: A is correct.

OCI AutoML documentation states: “print_trials() outputs a table of all trials performed, including hyperparameters and scores.” Visualization methods (B, C, D) focus on specific aspects—only A gives the full list.

Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".

Detailed Answer in Step-by-Step Solution:

Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).

Evaluate Options:

A: Access control—Possible but not primary collaboration.

B: Version control (e.g., Git)—Standard for code sharing—correct.

C: Shared instance—Not supported; sessions are single-user.

D: Chat/video—Not a feature of OCI Data Science.

Reasoning: B leverages Git for team collaboration—OCI’s recommended method.

Conclusion: B is correct.

OCI documentation states: “Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks.” A is limited, C isn’t feasible, and D isn’t available—only B matches OCI’s collaboration approach.

Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".

Detailed Answer in Step-by-Step Solution:

Objective: Sequence steps for a PySpark app in OCI Data Science.

Evaluate Steps:

Launch notebook: First—provides the environment.

Install PySpark conda: Second—sets up Spark libraries.

Configure core-site.xml: Third—connects to data (e.g., Object Storage).

Develop app: Fourth—writes the PySpark code.

Data Flow: Fifth—optional scaling, post-development.

Check Options: D (1, 2, 3, 4, 5) matches this logical flow.

Reasoning: Notebook first, then setup, coding, and scaling.

Conclusion: D is correct.

OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this—others (A, B, C) misorder critical steps like launching the notebook.

Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".

Detailed Answer in Step-by-Step Solution:

Objective: Diagnose a network timeout during pip install in a notebook session.

Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.

Analyze Timeout: Indicates failure to reach the public PyPI repository—likely no internet outbound route.

Evaluate Options:

A: NAT Gateway—Provides internet access for private subnets—correct fix.

B: Service Gateway—Accesses OCI services privately, not public internet.

C: FastConnect—Links to on-premises, not public internet.

D: VNIC—Essential but present by default; doesn’t solve internet access.

Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.

Conclusion: A is correct.

OCI documentation notes: “Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.” Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard—only A resolves the issue.

Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI service for restricted resource access.

Evaluate Options:

A: Bastion—Secure, temporary access to resources—correct.

B: Internet Gateway—Public access, not restricted.

C: Load Balancer—Distributes traffic, not access control.

D: SSL Certificate—Secures comms, not access.

Reasoning: Bastion limits access (e.g., SSH) to specific targets.

Conclusion: A is correct.

OCI documentation states: “OCI Bastion (A) provides restricted, audited access to target resources like instances, typically via SSH.” B, C, and D don’t restrict—only A fits per OCI’s security services.

Oracle Cloud Infrastructure Bastion Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify protection for data at rest/transit with customer control.

Evaluate Options:

A: Controls—Broad, not specific to encryption.

B: Isolation—Separates tenants, not crypto-focused.

C: Encryption—Secures data, allows key management—correct.

D: Federation—Auth sharing, not data protection.

Reasoning: C provides crypto control (e.g., Vault keys).

Conclusion: C is correct.

OCI documentation states: “Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs.” A and B are broader, D is unrelated—only C fits per OCI’s security model.

Oracle Cloud Infrastructure Security Documentation, "Data Encryption".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a true statement about OCI Jobs logging.

Understand Logging: Jobs can log stdout/stderr to OCI Logging service.

Evaluate Options:

A: False—Each run has its own log, not a single job log.

B: False—Logging is optional, not mandatory.

C: True—When enabled, stdout/stderr are auto-captured.

D: False—Logs persist unless explicitly deleted.

Reasoning: C matches OCI’s automatic logging feature.

Conclusion: C is correct.

OCI documentation states: “When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.” A is incorrect (per-run logs), B is optional, and D contradicts log retention—only C is accurate.

Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the Data Safe feature that reduces personal data exposure.

Understand Data Safe: Secures sensitive data in OCI databases.

Evaluate Options:

A: Encryption—Protects data, doesn’t minimize it.

B: Assessment—Identifies risks, doesn’t alter data.

C: Masking—Obfuscates personal data (e.g., SSNs)—correct.

D: Discovery—Locates sensitive data, doesn’t reduce it.

E: Auditing—Tracks access, doesn’t minimize data.

Reasoning: Masking replaces sensitive data, reducing risk for teams—fits goal.

Conclusion: C is correct.

OCI documentation states: “Data masking (C) in Data Safe transforms sensitive data into anonymized versions, minimizing exposure for test, dev, and analytics use.” A protects, B assesses, D finds, E audits—only C reduces data per OCI’s Data Safe features.

Oracle Cloud Infrastructure Data Safe Documentation, "Data Masking Overview".

Detailed Answer in Step-by-Step Solution:

Understand OCI IAM Policies: Policies control access using verbs like “inspect,” “read,” “use,” and “manage.”

Define Requirement: Consultants need view-only access without modification rights.

Evaluate Verbs:

A. Use: Allows interaction (e.g., running jobs)—too permissive.

B. Inspect: Grants view-only access to resource details—matches requirement.

C. Manage: Full control (create, update, delete)—too permissive.

D. Read: Includes viewing content (e.g., data), not just metadata—slightly broader than needed.

Conclusion: “Inspect” (B) is the precise verb for view-only access to resource details.

In OCI Identity and Access Management (IAM), the “inspect” verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Reference: Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").

Detailed Answer in Step-by-Step Solution:

Define Conda: Conda is a widely used tool for managing packages and environments in data science.

Evaluate Options:

A: Partially true—Conda manages dependencies, but it’s broader (an environment system).

B: Incorrect—Kernels (e.g., Jupyter) are separate; Conda manages environments.

C: Correct—Conda is an open-source tool for creating isolated environments with specific packages.

D: Incorrect—Not specific to Oracle AI; it’s a general tool.

Reasoning: C captures Conda’s full scope as an open-source system, beyond just dependency management (A).

Conclusion: C is the most accurate.

OCI documentation describes Conda as “an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries.” A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda’s official definition and OCI’s usage.

Oracle Cloud Infrastructure Data Science Documentation, "Conda Environments Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify true statements about published conda environments in OCI.

Understand Published Conda: Custom envs shared via Object Storage.

Evaluate Options:

A: False—Users create them, not OCI-curated.

B: True—odsc conda init sets up access to published envs.

C: False—Object Storage, not notebook, is the source.

D: False—Can create from scratch, not just modifications.

E: True—Conda env vars extend job configs.

Reasoning: B configures access, E enhances flexibility—match OCI behavior.

Conclusion: B and E are correct.

OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.

Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".

Detailed Answer in Step-by-Step Solution:

Objective: Identify an open model format for cross-platform ML model execution.

Evaluate Options:

A. PySpark: A big data framework, not a model format.

B. PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.

C. TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.

D. ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.

Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.

Conclusion: D is the correct choice.

ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.

Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".

Detailed Answer in Step-by-Step Solution:

Objective: Identify OCI’s managed Kubernetes service.

Evaluate Options:

A: Container Registry—Stores images, not Kubernetes.

B: Load Balancing—Network service, not Kubernetes.

C: Container Engine (OKE)—Managed Kubernetes—correct.

D: Streaming—Data streaming, not containers.

Reasoning: C is OCI’s Kubernetes offering—OKE.

Conclusion: C is correct.

OCI documentation states: “OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes.” A, B, and D serve other purposes—only C matches per OCI’s container services.

Oracle Cloud Infrastructure OKE Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Prevent deployment failure due to high utilization.

Evaluate Options:

A: More instances—Scales capacity—correct.

B: Delete—Stops service, not a solution.

C: Fewer instances—Worsens utilization.

D: Larger VM—Increases resource capacity—correct.

E: Reduce bandwidth—Limits load—correct.

Reasoning: A and D boost capacity, E controls demand—proactive fixes.

Conclusion: A, D, E are correct.

OCI documentation advises: “To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume.” B stops service, C reduces capacity—only A, D, E prevent failure per OCI’s scaling options.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".

Detailed Answer in Step-by-Step Solution:

Objective: Identify two OCI Language AI capabilities.

Understand OCI Language: Focuses on text analysis tasks.

Evaluate Options:

A: Table extraction—Vision, not Language—incorrect.

B: Punctuation correction—Not offered—incorrect.

C: Sentence diagramming—Not supported—incorrect.

D: Topic classification—Supported (custom/pretrained)—correct.

E: Sentiment analysis—Supported (pretrained)—correct.

Reasoning: D and E are core text analysis features of OCI Language.

Conclusion: D and E are correct.

OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.

Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".

Detailed Answer in Step-by-Step Solution:

Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.

Understand FAP: Controls false positive rate—threshold for anomaly flagging.

Evaluate Options:

A: Disable reporting—Incorrect; FAP sets sensitivity, not on/off.

B: Changes sensitivity—Correct; lower FAP = fewer false positives—correct.

C: Count-based error—Incorrect; not a counter.

D: Score per signal—Incorrect; FAP is a global setting.

Reasoning: FAP adjusts detection threshold—direct impact on sensitivity.

Conclusion: B is correct.

OCI documentation states: “False Alarm Probability (FAP) (B) adjusts the model’s sensitivity in Anomaly Detection—lower values increase specificity, reducing false positives.” A, C, and D misinterpret FAP’s role—only B aligns with OCI’s anomaly detection tuning.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.

Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.

Key Concepts:

Entity: Represents the instance (e.g., Linux VM).

Source: Defines log locations (e.g., file paths).

Log Group: Organizes logs for analysis.

Evaluate Options:

A: Log-Log Group—Groups logs, not collection setup.

B: Entity-Log—Links instance to logs, but not source-specific.

C: Source-Entity—Maps log sources to the instance—correct.

D: Log Group-Source—Post-collection organization, not ingestion.

Reasoning: C establishes the link between the instance and its log sources—key for ingestion.

Conclusion: C is correct.

OCI documentation states: “To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths).” A and D organize logs post-collection, B is less specific—only C is required for ingestion per OCI’s Logging Analytics setup.

Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".

Detailed Answer in Step-by-Step Solution:

Objective: Configure conda env for a pipeline step.

Evaluate Options:

A: Shape—Infra, not env config.

B: Volume—Storage, not env.

C: Command-line—Step args, not env.

D: Env variables—Sets conda path—correct.

Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).

Conclusion: D is correct.

OCI documentation states: “Configure a pipeline step’s conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.” A, B, and C address other aspects—only D fits env config.

Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

Detailed Answer in Step-by-Step Solution:

Objective: Find the OCI service for accessing large public datasets.

Evaluate Options:

A: Oracle Databases—General-purpose, not dataset-focused.

B: ADW—Analytics warehouse, not a dataset repository.

C: OCI Data Science—ML platform, not a dataset provider.

D: Oracle Open Data—Free, public datasets (e.g., geospatial).

Reasoning: Open Data provides pre-existing large datasets for research.

Conclusion: D is correct.

OCI documentation states: “Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers.” Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML—not datasets—only Open Data (D) fits.

Oracle Cloud Infrastructure Open Data Documentation, "Overview".