Objective 6.2
PMLEMonitoring, testing, and troubleshooting AI solutions
Objective 6.2 sits in Monitoring AI solutions, which carries 13% of the ML Engineer exam. The questions below are original, written from the official objective title above, and each explanation cites the Google Cloud page it rests on.
Objective title verbatim from the official objectives. Google Cloud exam page ↗
A worked example
Shown solved, with the whole explanation open: this is what every question here carries.
A team no longer holds the original training data and wants to catch changes in production inputs over time. Which detection applies?
Correct.
Concept
A distribution comparison needs two sides. Which comparison you are able to run is decided by which reference you still hold, so the absence of the training set picks the method for you.
Why C
Training-serving skew occurs when the feature data distribution in production deviates from the distribution used to train the model, and skew detection needs the original training data. Inference drift occurs when the production distribution changes significantly over time, and drift detection is used when the training data is unavailable.
Source
Introduction to Model Monitoring, Agent Platform documentation, checked August 2026Training-serving skew occurs when the feature data distribution in production deviates from the feature data distribution used to train the model. If the original training data is available, you can enable skew detection to monitor your models for training-serving skew. Inference drift occurs when feature data distribution in production changes significantly over time. If the original training data isn't available, you can enable drift detection to monitor the input data for changes over time.
Now you: objective 6.2 questions
No account needed. The explanation opens when you answer.
Sample question 1 of 3
An engineer monitors one numerical feature and one categorical feature and asks which distance measure each uses. What is the pairing?
Sample question 2 of 3
A team changes nothing but expects the skew baseline to refresh as production data accumulates. When is that baseline recalculated?
Sample question 3 of 3
A team enables monitoring on a model and names neither features nor thresholds. What does the default configuration cover?
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Read the sources
These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.