Google Cloud Professional Machine Learning Engineer — Question 259

You are developing an ML model in a Vertex AI Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?

Answer options

• A. 1. Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution. 2. After a successful experiment create a Vertex AI pipeline.
• B. 1. Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket, and upload the models to Vertex AI Model Registry. 2. After a successful experiment, create a Vertex AI pipeline.
• C. 1. Create a Vertex AI pipeline with parameters you want to track as arguments to your PipelineJob. Use the Metrics, Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline. 2. Associate the pipeline with your experiment when you submit the job.
• D. 1. Create a Vertex AI pipeline. Use the Dataset and Model artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline. 2. In your training component, use the Vertex AI SDK to create an experiment run. Configure the log_params and log_metrics functions to track parameters and metrics of your experiment.

Correct answer: A

Explanation

The correct answer, A, is effective because it emphasizes the initialization of the Vertex SDK and logging of parameters and metrics, which are essential for tracking experiments, followed by creating a Vertex AI pipeline for successful experiments. Option B includes saving the dataset to Cloud Storage, which adds unnecessary complexity. Option C focuses on creating a pipeline first instead of logging experiments, and option D lacks the initial logging step, making them less suitable for the goal of transitioning experiments to production efficiently.