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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

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

2. You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?

A) CSV
B) JSON
C) TSV
D) JSONL

3. Drag and Drop Question
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

4. You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?

A) Enable tracing for all inference calls in the evaluation pipeline.
B) Use prompt flow to generate multiple prompt templates for evaluation.
C) Deploy the base model to an online endpoint before starting fine-tuning.
D) Format the dataset as a JSONL file with prompt-completion pairs and upload the file.

5. A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?

A) One pipeline triggered by schedule and data drift alerts
B) Azure Function to retrain model manually
C) Notebook-based retraining process
D) Two independent pipelines with shared scripts

Solutions: