PMLE Monitoring ML Solutions Practice Question
A data science team is configuring Vertex AI Model Monitoring for a deployed model. They want to detect both feature skew and feature drift. Which TWO configurations must they set?
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
✓
Configure the monitoring frequency (e.g., hourly).
To detect feature skew, they need to specify a training dataset or statistics as a reference. To detect drift over time, they need to set a monitoring frequency. Sampling rate is optional but recommended.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable request/response logging.
Why it's wrong here
Logging is separate from monitoring configuration.
- ✗
Select the Jensen-Shannon divergence algorithm.
Why it's wrong here
Algorithm is chosen per feature, not a global config.
- ✓
Configure the monitoring frequency (e.g., hourly).
Why this is correct
Required to define how often drift is computed.
- ✗
Set the sampling rate to 1.0 (100%).
Why it's wrong here
Not required; 100% sampling is not mandatory.
- ✓
Specify a training dataset or statistics for baseline.
Why this is correct
Required for skew detection.
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