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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Drag and Drop Question
You manage an Azure Machine Learning workspace. You train a model named model1.
You must identify the features to modify for a differing model prediction result.
You need to configure the Responsible AI (RAI) dashboard for model1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

2. You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slackjactor set to 0.2 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?

A) Set the value of max_concurrent_trials to 20.
B) Set the value of max_concurrent_trials to 4.
C) Set the value of slack_factor of early_termination policy to 0.1.
D) Set the value of slack_factor of early_termination policy to 0.9.

3. An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?

A) Create a shared Azure Machine Learning environment that includes the asset.
B) Publish the asset as a pipeline component.
C) Enable workspace-level Git integration and sync assets between repositories.
D) Publish the asset to an Azure Machine Learning registry.

4. Hotspot Question
You create an Azure Machine Learning workspace and install the MLflow library.
You need to log different types of data by using the MLflow library.
Which method should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

5. A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source.
You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Tune chunk size and overlap to match content structure.
B) Increase token limits for all requests.
C) Optimize the length of embedding vectors.
D) Implement an optimized re-ranker.

Solutions: