Detailed Answer in Step-by-Step Solution:
* Objective: Define LIME's core concept.
* Understand LIME: Explains individual predictions with local surrogate models.
* Evaluate Options:
* A: Complex global, simple local-Correct LIME principle.
* B: Agnosticism-True but not the key idea.
* C: Global/local similarity-False.
* D: Local vs. global agnosticism-Incorrect distinction.
* Reasoning: A captures LIME's local approximation focus.
* Conclusion: A is correct.
OCI documentation notes: "LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances." B, C, and D misalign-only A reflects LIME's foundational idea per OCI's interpretability tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".

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: 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:
* 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:
* 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".