Oracle Cloud Infrastructure 1z0-1110-25 Exam Dumps

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: Address declining model performance and improve churn prediction.

Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.

Evaluate Options:

A. Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—correct.

B. Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.

C. Operational monitoring: Tracks system health (e.g., latency), not prediction quality.

D. Retrain the model: Updates model with new data—directly improves accuracy—correct.

E. Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective.

Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.

Conclusion: A and D are correct.

OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D 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: 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:

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.

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