A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?

• A.Enable observability metrics.
• B.Generate synthetic interaction data.
• C.Configure content filters.
• D.Apply a blocklist.

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A team accidentally deploys an outdated model version due to incorrect tagging. You need to enforce strict deployment governance and version control. What should you implement?

• A.Rename models
• B.Use version pinning and approval workflows
• C.Increase logging
• D.Store models locally

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

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Hotspot Question
You monitor an Azure Machine Learning classification training experiment named train_classification on Azure Notebooks.
You must store a table named table as an artifact in Azure Machine Learning Studio during model training.
You need to collect and list the metrics by using MLflow.
How should you complete the code segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Box 1: log_metrics
Log Metrics dictionary
The dictionary row1 contains key-value pairs representing metrics. The mlflow.log_metrics() function is used to log multiple metrics simultaneously.
Box 2: log_artifact
Save Table Artifact
The code writes a JSON file locally and needs to upload it to the experiment run. The mlflow.log_artifact() function logs a local file or directory as an artifact in Azure Machine Learning Studio.
Box 3: mlflow_run.info.run_id
Retrieve Run ID
To fetch the finalized run data using the MlflowClient, you need to pass the unique run ID string.
This identifier is accessed via the active run object using mlflow_run.info.run_id. Note that you also need to use the instantiated client object variable instead of the class name MlflowClient to call the method correctly.
Reference:
https://levelup.gitconnected.com/mlops-mastering-mlflow-unlocking-efficient-model-management-and-experiment-tracking-d9d0e71cc697

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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

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