1Z0-1110-22 Online Practice Questions and Answers

Which feature of the Oracle Cloud Infrastructure (OCI) Vision service helps you generate in-dexing tags for a collection of marketing photographs?

A. Document classification

B. Image classification

C. Text recognition

D. Key Value extraction

Select two reasons why it is important to rotate encryption keys when using Oracle Cloud In-frastructure (OCI) Vault to store credentials or other secrets.?

A. Key rotation allows you to encrypt no more than five keys at a time.

B. Key rotation reduces risk if a key is ever compromised.

C. Key rotation improves encryption efficiency.

D. Periodically rotating keys make it easier to reuse key.

E. Periodically rotating keys limits the amount of data encrypted by one key version.

You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS supported data formats?

A. DOCX

B. Pandas DataFram

C. JSON

D. Raw Images

E. XML

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. Package your personal OC file and keys in the job artifact.

B. 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.

C. Store your personal OCI config file and kays in the Vault, and access the Vault through the job nun resource principal

D. Create a pre-authenticated request (PAA) for the Object Storage bucket, and use that in the job code.

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. Model Depository

B. Model Catalog

C. OCI Vault

D. Data Science Artifactory

You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?

A. Adding labels to image using semantic segmentation, by drawing multiple bounding boxes to an image.

B. Adding a single label to an image.

C. Adding labels to an image by drawing bounding box to an image, is not supported by Data Labeling

D. Adding labels to an image using object detection, by drawing bounding boxes to an im- age.

E. Adding multiple labels to an image.

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 customer churn would you employ to verify the accuracy of the model?

A. Redeploy the model

B. Retrain the model

C. Operational monitoring

D. Validate the model using recent data

E. Drift monitoring

Where do calls to stdout and stderr from score.py go in a model deployment?

A. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment.

B. The OCI Cloud Shell, which can be accessed from the console.

C. The file that was defined for them on the Virtual stachine (VM).

D. The OCI console.

You are a data scientist working for a utilities company. You have developed an algorith that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces would you use to save the model artifact into the model catalog?

A. Console

B. Accelerated Data Science (ADS) Software Development Kit (SDK)

C. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)

D. OCI Python SDK

E. Git CLI

F. ODSC CLI

You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many subfolder and classes. What is the best way to run this project as a job?

A. ZIP the entire code project folder, upload it as a Job artifact on job creation and set JOB_RUN_ENTRYPOINT to point to the main executable file.

B. ZIP the entire code project folder and upload it as a Job artifact on job creation, Jobs identities the main executable file automatically.

C. Rewrite your code so that a single executable Python or Bash/Shell script file.

D. ZIP the entire code project folder and upload it as a Job artifact Jobs automatically identifies That main top level where the code is run.