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Monitoring ML SolutionsmediumMultiple ChoiceObjective-mapped

PMLE Monitoring ML Solutions Practice Question

A data scientist has deployed a model with Vertex AI Endpoints and enabled request/response logging to BigQuery. They want to compute a confusion matrix over time to monitor model quality. What should they do?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Upload ground truth labels to BigQuery and join with prediction logs, then compute confusion matrix in a scheduled query

To compute a confusion matrix, ground truth labels are needed. The team can upload ground truth labels to BigQuery and join with prediction logs to compare predictions vs actuals.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Use Vertex AI Model Monitoring to automatically generate confusion matrices

    Why it's wrong here

    Model Monitoring does not compute confusion matrices; it monitors skew and drift.

  • Use Cloud Monitoring to create a confusion matrix dashboard

    Why it's wrong here

    Cloud Monitoring does not have built-in confusion matrix generation.

  • Upload ground truth labels to BigQuery and join with prediction logs, then compute confusion matrix in a scheduled query

    Why this is correct

    Correct: This is the standard approach for model quality monitoring.

  • Enable Vertex AI Explainability to get confusion matrix

    Why it's wrong here

    Explainability provides feature attributions, not confusion matrices.

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