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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Drag and Drop Question
An organization operates a generative AI application in production by using Microsoft Foundry.
The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve.
The application intermittently times out during production use, which requires ongoing visibility into runtime behavior.
The team must also validate model quality and safety before releasing new updates to avoid introducing regressions.
You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation.
Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism 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.

2. You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?

A) Use batch endpoints
B) Define and reuse environment configurations
C) Use same compute for training and inference
D) Increase endpoint compute size

3. A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?

A) Set minimum instance count
B) Reduce logging
C) Switch to batch endpoint
D) Increase timeout settings

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

5. 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 train.py --training_data training_data
Does the solution meet the goal?

A) Yes
B) No

Solutions: