Online Access Free 1z0-1110-23 Practice Test

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

You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations () tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?

• A.get_transformed_dataset ()
• B.fit_transform()
• C.auto_transform()
• D.visualize_transforms ()

As a data scientist, you create models for cancer prediction based on mammographic images.
The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix.
Model 1 has Test accuracy is 80% and recall is 70%.
* Model 2 has Test accuracy is 75% and recall is 85%.
Which model would you prefer and why?

• A.Model 2, because recall has more impact on predictions in this use se.
• B.Model 1, because recall has lesser impact on predictions in this use case
• C.Model 2, because recall is high.
• D.Model 1, because the test accuracy is high.

3. When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) Data Science model catalog, you create a score.py file. What is the purpose of the score.py file?

• A.Define the compute scaling strategy.
• B.Configure the deployment infrastructure.
• C.Execute the inference logic code.
• D.Define the inference server dependencies

During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most effi-cient way to do this with Data Science Jobs?

• A.Edit the job, change the size of the storage of your job, and start a new job run.
• B.On the job run, set the environment variable that helps increase the size of the storage.
• C.Your code using too much disk space. Refactor the code to identify the problem.
• D.Create a new job with increased storage size and then run the job.