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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?

A) Retrain manually when failure occurs
B) Add a data validation component in pipeline
C) Use larger compute
D) Ignore schema differences

2. A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?

A) Coherence
B) Fluency
C) Textual similarity
D) Grounded ness

3. You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?

A) fairiearn.reductions.ExponentiatedGradient
B) fairlearn.reductions.GridSearch
C) fairlearn.postprocessing.ThresholdOptimizer
D) fairlearn.preprocessing.CorrelationRemover

4. Hotspot Question
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.

You need to verify whether the training script meets the experiment tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

5. A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
- Predictions must not disproportionately impact protected groups.
- Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Validate inference schema compatibility.
B) Measure endpoint latency under load.
C) Analyze error rates across the global cohort.
D) Evaluate feature importance for prediction transparency.
E) Analyze error rates across defined demographic cohorts.

Solutions: