nerdexam
Google

PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #241

You are training models in Vertex AI by using data that spans across multiple Google Cloud projects. You need to find, track, and compare the performance of the different versions of your models. Whic

The correct answer is C. Dataplex, Vertex AI Experiments, and Vertex AI ML Metadata. To track and compare the performance of different model versions across projects, Vertex AI Experiments and Vertex AI ML Metadata are essential for logging and tracking, while Dataplex can manage distributed data.

Submitted by alyssa_d· Apr 18, 2026ML pipeline operationalization

Question

You are training models in Vertex AI by using data that spans across multiple Google Cloud projects. You need to find, track, and compare the performance of the different versions of your models. Which Google Cloud services should you include in your ML workflow?

Options

  • ADataplex, Vertex AI Feature Store, and Vertex AI TensorBoard
  • BVertex AI Pipelines, Vertex AI Feature Store, and Vertex AI Experiments
  • CDataplex, Vertex AI Experiments, and Vertex AI ML Metadata
  • DVertex AI Pipelines, Vertex AI Experiments, and Vertex AI Metadata

How the community answered

(64 responses)
  • A
    3% (2)
  • B
    6% (4)
  • C
    80% (51)
  • D
    11% (7)

Why each option

To track and compare the performance of different model versions across projects, Vertex AI Experiments and Vertex AI ML Metadata are essential for logging and tracking, while Dataplex can manage distributed data.

ADataplex, Vertex AI Feature Store, and Vertex AI TensorBoard

Vertex AI Feature Store manages features, not model performance tracking; Vertex AI TensorBoard provides visualization, but not the core tracking and comparison mechanism required.

BVertex AI Pipelines, Vertex AI Feature Store, and Vertex AI Experiments

Vertex AI Pipelines orchestrates ML workflows, but the core requirement is tracking and comparing model *performance* and *versions*, which is handled by Experiments and Metadata, not Pipelines or Feature Store directly.

CDataplex, Vertex AI Experiments, and Vertex AI ML MetadataCorrect

Vertex AI Experiments is used to track and compare model performance, while Vertex AI ML Metadata tracks artifacts and their lineage; Dataplex provides unified data management across multiple projects. This combination specifically addresses the requirements of finding, tracking, and comparing model versions with data spanning multiple projects.

DVertex AI Pipelines, Vertex AI Experiments, and Vertex AI Metadata

Vertex AI Pipelines orchestrates workflows, but is not the primary service for *tracking and comparing* model performance and versions; while Metadata is relevant, Vertex AI Experiments is key for comparison. Dataplex is needed for managing data across projects, which is missing here.

Concept tested: Model tracking, comparison, and metadata management

Source: https://cloud.google.com/vertex-ai/docs/experiments/track-experiments

Topics

#Vertex AI Experiments#Vertex AI ML Metadata#Data governance#Model versioning

Community Discussion

No community discussion yet for this question.

Sign up to join the discussion
Full PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice