Hotspot Question
You are using hyperparameter tuning in Azure Machine Learning Python SDK v2 to train a model.
You configure the hyperparameter tuning experiment by running the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Box 1: No
No - By defining sampling in this manner, every possible combination of the parameters will be tested.
No, every possible combination will not be tested.
Continuous Spaces: Normal and Uniform distributions define continuous search spaces, creating an infinite number of possible decimal values.
Sampling Limits: The sweep job will only try a finite number of combinations based on your configuration.
Termination Rules: The total number of trials evaluated is strictly determined by the max_total_trials parameter and the chosen sampling_algorithm (e.g., Random, Grid, or Bayesian) that you apply to the sweep.
Box 2: Yes
Yes - Random values of the learning_rate parameter will be selected from a normal distribution with a mean of 10 and a standard deviation of 3.
The Normal(mu, sigma) distribution sampler in the Azure Machine Learning Python SDK v2 selects random values from a normal distribution defined by a mean (mu) and a standard deviation (sigma). In the code, Normal(10, 3) explicitly sets the mean to 10 and the standard deviation to 3.
Box 3: No
No - The keep_probabilily parameter value will always be either 0.05 or 0.1.
Because it is defined using Uniform(0.05, 0.1), Azure Machine Learning treats it as a continuous hyperparameter. This means the sweep job will sample any real number (floating-point value) distributed uniformly between 0.05 and 0.1 inclusive (for example, 0.063, 0.087, or 0.091), rather than choosing strictly between the two boundaries.
Box 4: No
No - Random values for the number_of_hidden_layers parameter will be selected from a normal distribution with a mean of 3 and standard deviation of 5.
The values for number_of_hidden_layers will be selected uniformly from a discrete list of choices, not from a normal distribution.
The Choice function: The Choice(range(3,5)) expression creates a discrete set of options: [3, 4].
Reference:
https://learn.microsoft.com/en-us/Azure/machine-Learning/how-to-tune-hyperparameters
https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/lightgbm/iris/lightgbm-iris-sweep.ipynb

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Hotspot Question
You review the following Azure CLI command and the relevant Bicep excerpt.

(Non-relevant sections are omitted.)
You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Correct Answer:

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An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?

• A.Azure Firewall rules
• B.Private endpoints
• C.Network security groups
• D.Service endpoints

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Drag and Drop Question
You manage an Microsoft Foundry project.
You deploy a large language model from the model catalog.
You need to manually evaluate the model, collect the statistics, and be able to review the results later.
Which four 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.

Correct Answer:

Explanation:
Step 1: Import Data in CSV Format
Supported formats: Azure AI Studio natively accepts .csv and .jsonl files for evaluation datasets.
Requirement: Your file must contain the input columns (e.g., user prompts) that you want to test against the model.
Step 2: Evaluate the Solution on 50 input Rows
Sample size: 50 rows is an excellent size for a manual, qualitative test baseline.
Execution: You will upload this dataset into the Evaluation blade of your project and map your data fields to the model's inputs.
Step 3: Provide thumbs up or down ratings to model responses
Manual UI: The platform features a manual review interface (often called human-in-the-loop evaluation).
Feedback: You can view the model's generated response for each of the 50 rows side-by-side with the input and log your binary (thumbs up/down) or detailed feedback.
Step 4: Save the evaluation results
Persistence: Once completed, the session is saved to your project's run history.
Review: You can return to the dashboard later to view aggregate statistics, check pass/fail rates, and export the annotated data for your records.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-perform-azure-migrate-at-scale-using-csv-import-for-large-datacenter-inventories/view

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You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.

You need to verify whether the training script meets the experiment tracking requirement.
For each of the following statements, select Yes if the statement is true. Otherwise, select No . NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
In Azure ML command jobs, MLflow automatically creates a run context, so explicit start_run is optional.
The training script must call mlflow.log_param to record each hyperparameter for the run. If the script calls this for each hyperparameter, the requirement for capturing parameters per run is met. The script must also call mlflow.log_metric to record numeric metrics such as accuracy or loss, optionally indexed by training step. Metrics and parameters logged inside an Azure ML command job are automatically associated with the correct experiment and run - no manual specification of experiment or run ID is required. If all three elements (run context, parameter logging, metric logging) are present in the script, all experiment tracking statements are True. If any element is absent, the corresponding statement is False and the tracking requirement is not met.
Microsoft Learn Reference Topic: Log metrics, parameters, and artifacts during Azure Machine Learning training runs with MLflow

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