Online Access Free 1z0-1110-22 Exam Questions

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

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

The feature type TechJob has the following registered validators: Tech-Job.validator.register(name='is_tech_job', handler=is_tech_job_default_handler) Tech-Job.validator.register(name='is_tech_job', handler= is_tech_job_open_handler, condi-tion=('job_family',)) TechJob.validator.register(name='is_tech_job', handler= is_tech_job_closed_handler, condition=('job_family': 'IT')) When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?

• A.Throw an error because the system cannot determine which handler to run.
• B.Execute the is_tech_job_open_handler handler.
• C.Execute the is_tech_job_closed_handler handler.
• D.Execute the is_tech_job_default_handler sales handler.

You want to ensure that all stdout and stderr from your code are automatically collected and logged, without implementing additional logging in your code. How would you achieve this with Data Science Jobs?

• A.You can implement custom logging in your code by using the Data Science Jobs logging.
• B.On job creation, enable logging and select a log group. Then, select either log or the op-tion to enable automatic log creation.
• C.Make sure that your code is using the standard logging library and then store all the logs to Check Storage at the end of the job.
• D.Data Science Jots does not support automatic fog collection and storing.

Which of the following TWO non-open source JupyterLab extensions has Oracle Cloud In-frastructure (OCI) Data Science developed and added to the notebook session experience?

• A.Command Palette
• B.Environment Explorer
• C.Table of Contents
• D.Notebook Examples
• E.Terminal

You want to evaluate the relationship between feature values and model predictions. You sus-pect that some of the features are correlated. Which model explanation technique would you recommend?

• A.Feature Permutation Importance Explanations.
• B.Local Interpretable Model-Agnostic Explanations.
• C.Accumulated Local Effects.
• D.Feature Dependence Explanations.