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| Section | Objectives |
|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models - Prepare and manage datasets - Automate machine learning workflows and pipelines |
| Topic 2: Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
| Topic 3: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools |
| Topic 4: OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources |
| Topic 5: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management |
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NEW QUESTION # 73
Where do calls to stdout and stderr from score.py go in a model deployment?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate score.py output in OCI model deployment.
Understand Deployment: Logs are centralized in OCI Logging.
Evaluate Options:
A: VM file—Not default; requires custom config—incorrect.
B: Predict log in OCI Logging—Standard destination—correct.
C: Cloud Shell—Separate tool, not logs—incorrect.
D: Console—UI, not raw logs—incorrect.
Reasoning: B aligns with OCI’s logging integration.
Conclusion: B is correct.
OCI documentation states: “score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment.” A isn’t standard, C and D don’t receive logs—only B fits OCI’s logging setup.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".
NEW QUESTION # 74
Where are OCI secrets stored?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.
Evaluate Options:
A: Object Storage is for general data, not secure secret management.
B: Vault is OCI’s service for storing and managing secrets securely.
C: Autonomous Data Warehouse is for analytics, not secret storage.
D: Oracle Databases store data, not OCI-specific secrets.
Reasoning: Vault is purpose-built for secrets with encryption and access control.
Conclusion: B is correct.
OCI Vault “provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies.” Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes—only Vault (B) is designed for secrets per OCI’s security architecture.
1: Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".
NEW QUESTION # 75
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an ADSTuner strategy to minimize cost and assess model quality.
Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.
Evaluate Options:
A: Detailed—Exhaustive, high cost—incorrect.
B: No search space—False; tuning requires a space.
C: Perfunctory—Quick, low-cost assessment—correct.
D: Dictionary—Defines space but not a strategy.
Reasoning: Perfunctory balances cost and initial quality check.
Conclusion: C is correct.
OCI documentation states: “ADSTuner’s perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning.” Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy—only C fits the goal.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".
NEW QUESTION # 76
Which step is a part of the AutoML pipeline?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step in OCI’s AutoML pipeline.
Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.
Evaluate Options:
A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.
B: Saving to Model Catalog is post-AutoML, not a pipeline step.
C: Deployment is a separate action after AutoML—incorrect.
D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.
Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.
Conclusion: D is correct.
OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.
1: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
NEW QUESTION # 77
What is feature engineering in machine learning used for?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.
Evaluate Options:
A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.
D: Understanding features occurs during exploration, not engineering.
Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.
Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning 1with C. Other options (A, B, D) relate to different ML stages.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
NEW QUESTION # 78
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