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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Oracle Cloud Infrastructure Data Science Professional Sample Questions:

1. Where are OCI secrets stored?

A) OCI Object Storage
B) OCI Vault
C) Oracle Databases
D) Autonomous Data Warehouse

2. 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) fit_transform()
B) auto_transform()
C) get_transformed_dataset()
D) visualize_transforms()

3. Six months ago you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods would you employ to verify accuracy and lower customer churn?

A) Redeploy the model
B) Drift monitoring
C) Retrain the model
D) Validate the model using recent data
E) Operational monitoring

4. You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?

A) Create a pre-authenticated request (PAR) for the Object Storage bucket and use that in the job code
B) Store your personal OCI config file and keys in the Vault, and access the Vault through the job run resource principal
C) Package your personal OCI config file and keys in the job artifact
D) Use the resource principal of the job run as the signer in the job code, ensuring there is a dynamic group for this job run with appropriate access to Object Storage and the model catalog

5. What is the first step in the data science process?

A) Defining an analytical hypothesis that could provide business value
B) Working with data owners
C) Experimenting with and tuning different analytical models
D) Collecting data and preparing it for analysis

Solutions: