You want to make your model more parsimonious to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heat map that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?

• A. Pearson_plot{}
• B. Correlation_ratio_plot{}
• C. Cramersv_plot{}
• D. Corr{}

You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

• A. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and re-run the job. Repeat the process until the processing time does not improve.
• B. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job.
• C. Start with the strangest compute shape Jobs support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost.
• D. Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training: If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized.

You are a data scientist working for a manufacturing company, you have developed a fore-casting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact.?

• A. Requirement.txt
• B. Model_artifact_validate.py
• C. Score.py
• D. Runtime.yaml

What preparation steps are required to access an Oracle AI service SDK from a Data Science notebook session?

• A. Create and upload execute.py and runtime.yaml
• B. Call the Accented Data Science (ADS) command to enable Al integration
• C. Import the REST API
• D. Create and upload the API signing key and config file

You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail?

• A. Reduce the load balancer bandwidth limit so that fewer requests come in.
• B. Update the deployment to use fewer instances.
• C. Delete the deployment.
• D. Update the deployment to use a larger virtual machine (mare CPUs/memory).
• E. Update the deployment to add more instances.