As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?

• A. Ensure your files and environments are written to the block volume storage under the /home/datascience directory, deactivate the notebook session, and activate the notebook larger compute shape selected.
• B. Down your files and data to your local machine, delete your notebook session, provision tebook session on a larger compute shape, and upload your files from your local the new notebook session.
• C. Create a temporary bucket in Object Storage, write all your files and data to Object Storage, delete tur ctebook session, provision a new notebook session on a larger com-pute shape, and capy your flies and data from your temporary bucket onto your new notebook session.
• D. Deactivate your notebook session, provision a new notebook session on larger compute shape, and re-create all your file changes.

You are a data scientist designing an air traffic control model, and you choose to leverage Or-acle AutoML. You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?

• A. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning.
• B. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning.
• C. Algorithm selection, Adaptive sampling. Feature selection, Hyperparameter tuning.
• D. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning.

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 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.
• D. 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.

You are using a third-party Continuous Integration/Continuous Delivery (CI/CD) tool to create a pipeline for preparing and training models. How would you integrate a third-party tool outside Oracle Cloud Infrastructure (OCI) to access Data Science Jobs?

• A. Data Science Jobs does not accept code from third-party tools, therefore you need to run the pipeline externally.
• B. Data Science Jobs Data Science Jobs is not accessible from outside OCI.
• C. Third-party tools use authentication keys to create and run.
• D. Third-party software can access Data Science Jobs by using any of the OCI Software Development Kits (SDKs).