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1z0-1110-22 test questions: Oracle Cloud Infrastructure Data Science 2022 Professional & 1z0-1110-22 pass-king dumps

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Oracle 1z0-1110-22 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Manage Models using Model Catalog
  • Deploy & Invoke a Cataloged Model
Topic 2
  • Create and Export a Dataset using OCI Data Labeling
  • Discuss general MLOps Architecture in OCI
Topic 3
  • Create and train models using OCI and Open source Libraries
  • Discuss Accelerated Data Science (ADS) SDK Capabilities
Topic 4
  • Create and Manage Spark Applications using Data Flow and OCI Data Science
  • Create and manage Conda environments
Topic 5
  • Explain core OCI Open Data Service concepts
  • Create & Manage Jobs for custom tasks
Topic 6
  • Obtain Global & Local Model Explanations
  • Access data from different sources

Oracle Cloud Infrastructure Data Science 2022 Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
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. Git CLI
  • B. ODSC CLI
  • C. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
  • D. OCI Python SDK
  • E. Console
  • F. Accelerated Data Science (ADS) Software Development Kit (SDK)

Answer: D,E,F


NEW QUESTION # 14
The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as ora-cle_automl and now you want an output with all the different trials performed by Oracle Au-toML. Which of the following command gives you the results of all the trials?

  • A. Oracle.automl.visualize_adaptive_sampling_trails()
  • B. Oracle.automl.print_trials()
  • C. Oracle.automl.visualize_algorith_selection_trails()
  • D. Oracle.automl.visualize_tuning_trails()

Answer: B


NEW QUESTION # 15
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. Accumulated Local Effects.
  • B. Local Interpretable Model-Agnostic Explanations.
  • C. Feature Dependence Explanations.
  • D. Feature Permutation Importance Explanations.

Answer: B


NEW QUESTION # 16
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. Execute the inference logic code
  • C. Define the inference server dependencies.
  • D. Configure the deployment infrastructure.

Answer: D


NEW QUESTION # 17
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 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.
  • 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 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.

Answer: A


NEW QUESTION # 18
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