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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?

A) Git repositories
B) Blob storage containers
C) Data lake
D) Model registry

2. You must ensure full reproducibility of experiments including dataset, code, and environment across multiple runs and workspaces. Which combination of practices is MOST appropriate?

A) Logging metrics only
B) Dataset versioning only
C) Environment + dataset + code versioning
D) Git only

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.preprocessing.CorrelationRemover
D) fairlearn.postprocessing.ThresholdOptimizer

4. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?

A) Yes
B) No

5. You need to run large-scale inference jobs on millions of records periodically. Jobs are not latency-sensitive but must be cost-efficient and scalable. Which deployment option is MOST appropriate?

A) Batch endpoint
B) Managed online endpoint
C) Local endpoint
D) Notebook execution

Solutions: