Oracle 1Z0-1110-22 Online Practice Questions and Exam Preparation

Oracle 1Z0-1110-22 Online Questions & Answers

• Question 41:

  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
  A. Console
  B. Accelerated Data Science (ADS) Software Development Kit (SDK)
  D. OCI Python SDK

• Question 42:

  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. Data Science Jots does not support automatic fog collection and storing.
  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. You can implement custom logging in your code by using the Data Science Jobs logging.
  D. 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.
  C. You can implement custom logging in your code by using the Data Science Jobs logging.

• Question 43:

  You have developed a model training code that regularly checks for new data in Object Storage and retrains the model. Which statement best describes the Oracle Cloud Infrastructure (OCI) services that can be accessed from Data Science Jobs?

  A. Data Science Jobs can access OCI resources only via the resource principal.
  B. Some OCI services require authorizations not supported by Data Science Jobs.
  C. Data Science Jobs cannot access all OCI services.
  D. Data Science Jobs can access all OCI services.
  D. Data Science Jobs can access all OCI services.

• Question 44:

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

• Question 45:

  While reviewing your data, you discover that your data set has a class imbalance. You are aware that the Accelerated Data Science (ADS) SDK provides multiple built-in automatic transformation tools for data set transformation. Which would be the right tool to correct any imbalance between the classes?

  A. sample()
  B. suggeste_recoomendations()
  C. auto_transform()
  D. visualize_transforms()
  C. auto_transform()

• Question 46:

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

• Question 47:

  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
  B. Model Catalog

• Question 48:

  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.
  C. Global behavior of a machine learning model may be complex, while the local behavior may be approximated with a simpler surrogate model.

• Question 49:

  You trained a model to predict housing prices for your city. Which two metrics from the Ac- celerated Data Science (ADS) Evaluation class can be used to evaluate the regression model you just trained?

  A. Mean Absolute Error
  B. Explained Variance Score
  C. Weighted Recall
  D. Weighted Precision
  E. F-1 Score
  A. Mean Absolute Error
  B. Explained Variance Score

• Question 50:

  You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

  A. Start with the strangest compute shape Jobs support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost.
  B. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job.
  C. Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training: If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized.
  D. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and re-run the job. Repeat the process until the processing time does not improve.
  D. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and re-run the job. Repeat the process until the processing time does not improve.