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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #252

You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions…

The correct answer is C. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex. To enable easy collaboration and comparison of metrics from different pipeline executions both visually and programmatically, you should log metrics to Vertex ML Metadata and use the Vertex AI console for visualization.

Submitted by joshua94· Apr 18, 2026ML pipeline operationalization

Question

You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?

Options

  • AAdd a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table
  • BAdd a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table
  • CAdd a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex
  • DAdd a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the

How the community answered

(29 responses)
  • A
    17% (5)
  • B
    7% (2)
  • C
    72% (21)
  • D
    3% (1)

Why each option

To enable easy collaboration and comparison of metrics from different pipeline executions both visually and programmatically, you should log metrics to Vertex ML Metadata and use the Vertex AI console for visualization.

AAdd a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table

While BigQuery can store metrics, it requires custom querying and visualization tools, which is less convenient for direct collaboration and visual comparison within the Vertex AI ecosystem.

BAdd a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table

Storing metrics in BigQuery and loading them into a custom dashboard requires significant development effort for collaboration and comparison, which is less integrated than using Vertex ML Metadata and the Vertex AI console.

CAdd a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use VertexCorrect

Logging metrics to Vertex ML Metadata ensures that all experiment-related information, including metrics, parameters, and lineage, is centrally stored and tracked. The Vertex AI console provides built-in visualizations and comparison tools for experiment runs, making it easy for teams to collaborate, view, and programmatically access these metrics.

DAdd a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the

Logging metrics to Vertex ML Metadata is correct, but loading them into a custom dashboard adds unnecessary complexity for collaboration and visualization, as the Vertex AI console already offers these capabilities.

Concept tested: Vertex ML Metadata and Vertex AI Experiments for tracking and comparison

Source: https://cloud.google.com/vertex-ai/docs/ml-pipelines/visualize-runs#visualize_run_metrics

Topics

#Vertex AI Pipelines#ML Metadata#Experiment Tracking#Metric Comparison

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