Google Cloud Professional Machine Learning Engineer — Question 56

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

Answer options

• A. Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.
• B. Use AI Platform Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
• C. Use AI Platform Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
• D. Use AI Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.

Correct answer: A

Explanation

The correct answer is A, as Kubeflow Pipelines is specifically designed for managing machine learning workflows, providing automated tracking and reporting of experiments. Options B and C involve using AI Platform Training but lack the streamlined experiment management and tracking that Kubeflow offers. Option D relies on manual data collection in Google Sheets, which increases manual effort and decreases efficiency.