PMLE Monitoring ML Solutions Practice Question

You are using Vertex AI Model Monitoring to detect prediction drift on a deployed model that serves online predictions. You want to ensure that the monitoring job can correctly compute drift metrics for numerical features. Which two configurations are required for the monitoring job to compute drift for a numerical feature? (Choose two.)

⚠ Common exam trap

The trap here is focusing on sampling rate or thresholds as prerequisites, while the fundamental requirements are the reference dataset and correct schema.

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

✓

Specify the feature's distribution as a numerical distribution and provide the training dataset as the reference.

To compute drift for numerical features, Vertex AI Model Monitoring needs a reference dataset to establish the baseline distribution and a correct input schema to parse the feature as numerical. These two configurations are essential; sampling rate, explainability, and thresholds are not required for the computation itself.

Answer analysis

Option-by-option breakdown

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

✓
Specify the feature's distribution as a numerical distribution and provide the training dataset as the reference.
Why this is correct
For numerical features, Vertex AI Model Monitoring requires a reference dataset to establish the baseline distribution. The monitoring job compares the live distribution to this baseline to compute drift. Without a reference, drift cannot be calculated. Thus, specifying the numerical distribution and providing the training dataset are essential.
✗
Enable explainability on the endpoint to generate feature attributions for drift analysis.
Why it's wrong here
Explainability provides feature attributions for individual predictions but is not used for drift detection. Drift detection relies on statistical comparisons of feature distributions between the reference and live data. Enabling explainability adds overhead and does not contribute to the computation of drift metrics.
✗
Configure the monitoring job to use a default threshold for drift detection, such as 0.3 for the Jensen-Shannon distance.
Why it's wrong here
While thresholds are used to trigger alerts, they are not required to compute drift metrics. The monitoring job will compute the drift value regardless of the threshold; the threshold only determines whether an alert is generated. Therefore, setting a threshold is not a prerequisite for computing drift.
✓
Ensure that the feature's data type is correctly specified in the model's input schema.
Why this is correct
The input schema tells Vertex AI Model Monitoring how to interpret each feature in the prediction requests. For numerical features, the schema must indicate that the feature is of type number or integer. If the schema is incorrect, the monitoring job may fail to parse the feature or treat it as categorical, preventing correct drift computation.
✗
Set the monitoring job's sampling rate to at least 0.5 to ensure sufficient data for statistical tests.
Why it's wrong here
While a higher sampling rate increases the amount of data analyzed and can improve statistical confidence, it is not a strict requirement for computing drift. Vertex AI can compute drift with lower sampling rates, though the results may be less stable. The essential requirement is the reference dataset, not a specific sampling rate.

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PMLE Monitoring ML Solutions Practice Question

Why each option matters

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