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| Section | Weight | Objectives |
|---|---|---|
| Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
| Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking |
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Model development, training, and evaluation |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
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NEW QUESTION # 98
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an explanation technique for feature-target relationships with correlated features.
Evaluate Options:
A: Permutation—Breaks with high correlation.
B: LIME—Local, not global relationships.
C: Dependence—Not a standard term; vague.
D: ALE—Handles correlation, shows feature effects—correct.
Reasoning: ALE is robust to correlated features, ideal here.
Conclusion: D is correct.
OCI documentation states: “Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation.” B is local, C isn’t defined—only D fits per OCI’s explanation tools.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".
NEW QUESTION # 99
Which activity is NOT a part of the machine learning life cycle?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify which activity isn’t part of the ML lifecycle.
Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
Evaluate Options:
A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
B: Model Deployment (e.g., serving predictions) is a key ML phase—correctly included.
C: Modeling (e.g., training) is the core of ML—correctly included.
D: Data Access (e.g., retrieving data) is the first ML step—correctly included.
Reasoning: Database management supports infrastructure, not the ML process directly.
Conclusion: A is the outlier.
The OCI Data Science lifecycle includes “data access, exploration, feature engineering, modeling, deployment, and monitoring,” per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI’s ML pipeline.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".
NEW QUESTION # 100
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
Answer: B,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
NEW QUESTION # 101
Where do calls to stdout and stderr from score.py go in a model deployment?
Answer: A
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 # 102
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine minimum log storage duration before archiving in Logging Analytics.
Understand Logging Analytics: Logs are active before archival.
Evaluate Options:
A: 60 days—Too long for minimum.
B: 10 days—Too short.
C: 30 days—Standard minimum—correct.
D: 15 days—Not OCI’s default.
Reasoning: 30 days is OCI’s documented minimum active period.
Conclusion: C is correct.
OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.
1: Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".
NEW QUESTION # 103
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