1z0-1110-25 Exam Dumps : Oracle Cloud Infrastructure 2025 Data Science Professional

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: Troubleshoot a 404 auth error when saving a model with resource principal.

Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials.

Analyze 404 Error: Indicates permission failure—likely IAM misconfiguration.

Evaluate Options:

A: Block volume storage—Irrelevant to auth; it’s about saving locally—incorrect.

B: Dynamic group matching—Ensures notebook is recognized—correct.

C: User group policy—Not used with resource principal—incorrect.

D: Dynamic group policy—Grants catalog access—correct.

E: Service Gateway—Network-related, not auth-specific—incorrect.

Reasoning: Resource principal needs B (group inclusion) and D (policy perms)—404 points to these.

Conclusion: B and D are correct.

OCI documentation states: “For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type ='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D).” A is storage, C is user-based, E is network—only B and D fix the auth issue per OCI’s IAM setup.

Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".

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".