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1z0-1110-22 – Oracle Cloud Infrastructure Data Science 2022 Professional

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Showing 7–9 of 10 questions

Question 7

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?

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    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.
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    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.
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    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.
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    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.
Question 8

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?

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    sample()
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    suggeste_recoomendations()
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    auto_transform()
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    visualize_transforms()
Question 9

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?

Select an option, then click Submit answer.

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    Define the compute scaling strategy.
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    Configure the deployment infrastructure.
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    Define the inference server dependencies.
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    Execute the inference logic code