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…

Question

Options

• AUse Vertex Al Pipelines to execute the experiments. Query the results stored in MetadataStore
• BUse Vertex Al Training to execute the experiments. Write the accuracy metrics to BigQuery, and
• CUse Vertex Al Training to execute the experiments. Write the accuracy metrics to Cloud
• DUse Vertex Al Workbench user-managed notebooks to execute the experiments. Collect the

• A
  82% (37)
• B
  2% (1)
• C
  11% (5)
• D
  4% (2)

Explanation

Vertex AI Pipelines with MetadataStore is the purpose-built GCP solution for this workflow. Pipelines orchestrate and execute experiments automatically, while Vertex AI MetadataStore records artifacts, parameters, and metrics from each run. MetadataStore exposes a first-class API that allows querying and comparing metrics across experiments over time-exactly what the question requires-with no manual instrumentation beyond pipeline configuration. Option B (writing to BigQuery) and Option C (writing to Cloud Monitoring) require custom code to log and retrieve metrics, adding manual effort. Option D (Workbench notebooks) is an interactive environment suitable for exploration but not for automated, reproducible experimentation at scale with API-queryable results.

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