As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series…

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").