Oracle Oracle Certifications 1Z0-1110-22 Questions & Answers

• Question 51:

  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. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models.

  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. Global and local behaviors of machine learning models are similar.

  Correct Answer: C

• Question 52:

  You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your condo environment. After running this command, you get a network timeout error. What might be missing from your networking configuration?

  A. Service Gateway with private subnet access.

  B. NAT Gateway with public internet access.

  C. FastConnect to an on-premises network.

  D. Primary Virtual Network Interface Card (VNIC).

  Correct Answer: B

• Question 53:

  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. Environment Explorer

  B. Table of Contents

  C. Command Palette

  D. Notebook Examples

  E. Terminal

  Correct Answer: AD

• Question 54:

  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 fie?

  A. Define the compute scaling strategy.

  B. Configure the deployment infrastructure.

  C. Define the inference server dependencies.

  D. Execute the inference logic code

  Correct Answer: B

• Question 55:

  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.

  Correct Answer: A