Online Access Free 1z0-1110-22 Practice Test

As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those pa-rameters with Oracle Data Science Jobs?

• A.Create a new job every time you need to run your code and pass the parameters as en-vironment variables.
• B.Create your code to expect different parameters as command line arguments, and create it new job every time you run the code.
• C.Create a new no by setting the required parameters in your code, and create a new job for mery code change.
• D.Create your code to expect different parameters either as environment variables or as command line arguments, which are set on every job run with different values.

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.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.
• B.Deactivate your notebook session, provision a new notebook session on larger compute shape, and re-create all your file changes.
• C.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.
• D.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.

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 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.
• B.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.
• 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 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.

You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?

• A.Global and local behaviors of machine learning models are similar.
• B.Local explanation techniques are model agnostic, while global explanation techniques are not.
• C.Global behavior of a machine learning model may be complex, while the local behavior may be approximated with a simpler surrogate model.
• D.Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models.

You have created a model, and you want to use the Accelerated Data Science (ADS) SDK to deploy this model. Where can you save the artifacts to deploy this model with ADS?

• A.Data Science Artifactory
• B.Model Depository
• C.Model Catalog
• D.OCI Vault