Detailed Answer in Step-by-Step Solution:
* Objective: Identify OCI's managed Kubernetes service.
* Evaluate Options:
* A: Container Registry-Stores images, not Kubernetes.
* B: Load Balancing-Network service, not Kubernetes.
* C: Container Engine (OKE)-Managed Kubernetes-correct.
* D: Streaming-Data streaming, not containers.
* Reasoning: C is OCI's Kubernetes offering-OKE.
* Conclusion: C is correct.
OCI documentation states: "OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes." A, B, and D serve other purposes- only C matches per OCI's container services.
Oracle Cloud Infrastructure OKE Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:
* Objective: Define data science's main goal.
* Evaluate Options:
* A: Archiving-Not the focus; too narrow.
* B: Analyze for insights/business value-Core purpose-correct.
* C: Prep for analytics-Means, not the end goal.
* D: Output-focused-Vague, incomplete.
* Reasoning: B captures the actionable insight generation central to data science.
* Conclusion: B is correct.
OCI documentation defines data science as "mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value." A is storage-focused, C is preparatory, and D is unclear-only B reflects the principal goal per OCI's mission.
Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".

Detailed Answer in Step-by-Step Solution:
* Prepare Model: Build and train the time series model using historical data.
* Verify: Validate the model's accuracy (e.g., using metrics like MAE or RMSE).
* Save: Store the trained model (e.g., in the OCI Model Catalog).
* Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
* Predict: Generate revenue forecasts for the upcoming quarter.
* Evaluate Options: D follows this logical flow; others (e.g., A starts with "verify" before preparation) don't.
In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").

Detailed Answer in Step-by-Step Solution:
* Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
* Evaluate Options:
* A: OCI CLI-Supports Data Science tasks-possible but not primary.
* B: ADS SDK-Designed for model catalog ops-correct.
* C: ODSC CLI-Not standard; likely typo for OCI CLI.
* D: Console-GUI for catalog uploads-correct.
* E: OCI Python SDK-Programmatic catalog access-correct.
* F: Git CLI-Version control, not catalog-related.
* Reasoning: B, D, E are OCI's primary interfaces; A is valid but less emphasized.
* Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists "ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog." OCI CLI (A) works but isn't highlighted, C isn't real, and F is unrelated- B, D, E are the standard trio.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".

Detailed Answer in Step-by-Step Solution:
* Objective: Scale up notebook without losing work.
* Evaluate Options:
* A: Object Storage-Extra steps, inefficient.
* B: Block volume-Persists data, seamless scale-correct.
* C: Local machine-Risky, cumbersome.
* D: Recreate-Loses work, impractical.
* Reasoning: B uses OCI's built-in persistence.
* Conclusion: B is correct.
OCI documentation states: "Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss." A, C, and D add complexity or risk-only B is optimal per OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".