MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning engineer manages a SageMaker Model Monitor schedule for a real-time endpoint. The monitor's baseline was computed from a training dataset with a categorical feature named region. In production, a new category value appears that was never seen in training, and the monitor begins reporting violations. The engineer wants the monitor to flag only the appearance of unknown categories without treating normal distribution shifts in known categories as violations. Which approach should the engineer take?
⚠ Common exam trap
The trap here is assuming that distribution comparison automatically detects brand-new categorical values, when the explicit allowed-value check that catches them is the constraint check.
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
✓
Enable the monitor's constraint on the categorical feature so that only values present in the baseline are allowed, and keep distribution comparison thresholds relaxed for that feature.
Model Monitor separates two kinds of checks. Constraints, derived from the baseline statistics, enumerate allowed values for categorical features, so a value never seen in training violates the constraint and is flagged as unknown. Distribution comparison measures frequency drift among values that exist in the baseline. Keeping constraints enabled while relaxing distribution thresholds isolates unknown-category detection from ordinary frequency shifts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Recreate the baseline with a larger sample that includes the new category, then set the monitor's comparison threshold to zero for all features.
Why it's wrong here
Including the new category in the baseline would treat it as expected, so it would no longer be flagged as unknown, which defeats the stated goal. Setting the comparison threshold to zero for all features makes the monitor maximally sensitive and would flag ordinary distribution shifts in known categories as violations, the opposite of what the engineer wants.
- ✗
Disable the constraint checks for the feature and rely solely on the monitor's distribution comparison to detect the new category.
Why it's wrong here
Distribution comparison measures how the frequency of observed values diverges from the baseline distribution; a brand-new category has no baseline frequency, so the comparison may or may not surface it depending on how the statistic treats unseen values, and disabling constraints removes the explicit allowed-value check. This makes unknown-category detection unreliable.
- ✓
Enable the monitor's constraint on the categorical feature so that only values present in the baseline are allowed, and keep distribution comparison thresholds relaxed for that feature.
Why this is correct
Model Monitor emits constraints from the baseline that list allowed categorical values, so a value absent from the baseline violates the constraint and is flagged as an unknown category. Keeping the distribution comparison threshold relaxed for that feature prevents normal frequency shifts among known categories from generating violations, which is exactly the separation the engineer is asking for.
- ✗
Increase the monitor's sampling percentage to one hundred percent and lower the KMS key rotation period so the monitor can read all incoming records.
Why it's wrong here
Sampling percentage controls how many captured records are analyzed; raising it to one hundred percent improves coverage but does not change whether unknown categories are flagged. KMS key rotation is unrelated to whether the monitor can read captured data, since access depends on the execution role's kms:Decrypt permission and the key policy, not on rotation frequency.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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