You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
You need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Navigate to another section of the workspace.
B. stop the current kernel.
C. Reset the compute.
D. Change the current kernel.
E. Change the compute.
Show Answer
Correct Answer: A, B, D
Explanation: A notebook's in-memory state can be reset by leaving and reopening the notebook, stopping and restarting the kernel, or switching to a different kernel. Resetting or changing the compute affects the execution environment but is not required to reset the notebook state itself.
Sources:
https://www.freecram.net/question/Microsoft.DP-100.v2024-11-25.q182/you-create-a-workspace-by-using-azure-machine-learning-studio-you-must-run-a-python-sdk-v2-notebook-161
Question 12
DRAG DROP
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A data science team trains a model that depends on features that are stored in a managed feature store.
The model is registered in Azure Machine Learning and will be deployed to a real-time endpoint.
After deployment, the model must:
• Retrieve feature values dynamically at inference time.
• Use the same feature definitions that were used during training.
• Run without manual configuration changes across environments.
You need to define feature store entities so that feature retrieval behaves as expected when the model is deployed.
Which feature store entity should you select for each requirement? To answer, move the appropriate feature store entities to the correct requirements. You may use each feature store entity 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.
Show Answer
Correct Answer: Define how features are retrieved at inference → Feature retrieval specification
Ensure feature consistency between training and inference → Feature retrieval specification
Enable automated feature lookup in production → Feature retrieval specification
Explanation: The feature retrieval specification is packaged with the model and describes how features are looked up at inference, preserving the same feature definitions used during training and enabling automatic feature lookup across environments.
Question 13
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?
A. Blocklist applied to the model endpoint
B. Content filter configured in warning mode
C. Retrieval chunking strategy
D. GitHub Actions workflow that executes the runs
Show Answer
Correct Answer: D
Explanation: To automate generative AI evaluations for every pull request and fail the build when safety thresholds are exceeded, configure a GitHub Actions workflow that executes the evaluation runs as part of the CI pipeline. Blocklists and content filters are runtime protections, and retrieval chunking is unrelated to CI safety gating.
Question 14
A team provisions an Azure Machine Learning environment by triggering pull requests.
Deployments must be automated, auditable, and require approval before running.
You need to select a deployment automation tool.
Which tool should you use?
A. Azure Monitor
B. GitHub Actions
C. MLflow
D. Azure Machine Learning pipelines
Show Answer
Correct Answer: B
Explanation: GitHub Actions is the appropriate CI/CD automation tool for deployments triggered by pull requests. It supports automated workflows, auditability through repository history and workflow logs, and required approval gates via protected environments before deployment. Azure Monitor is for monitoring, MLflow is for experiment/model tracking, and Azure Machine Learning pipelines orchestrate ML workflows rather than PR-based deployment approvals.
Question 15
You manage an Azure Machine Learning workspace.
You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
Which parameter should you use?
A. conda_file
B. properties
C. build
D. image
Show Answer
Correct Answer: D
Explanation: In the Azure Machine Learning Python SDK v2, an Environment can be defined directly from an existing Docker image by specifying the image parameter. The conda_file parameter is for Conda dependencies layered onto a base image, build is used to build an image from a Docker build context/Dockerfile, and properties stores metadata.
Question 16
You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.
A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
What should you do?
A. Change the description parameter of the environment configuration.
B. Modify the conda_file to specify the new version of the Docker image.
C. Use the create_or_update method to change the tag of the image.
D. Use the Environment class to create a new version of the environment.
Show Answer
Correct Answer: D
Explanation: Azure Machine Learning environments are immutable and versioned. When the underlying Docker image changes, you define an Environment with the updated image reference and register it as a new environment version. Updating the description or conda file does not change the base image, and you do not modify an existing immutable environment in place by changing its image tag.
Question 17
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
• accounts for the performance of all previous runs when evaluating the current run
• avoids comparing the current run with only the best performing run to date
Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Bandit
B. Default
C. Median stopping
D. Truncation selection
Show Answer
Correct Answer: C, D
Explanation: Median stopping evaluates the current run against the median performance of all running averages from previous runs at the same interval, so it considers all prior runs rather than only the best. Truncation selection cancels the lowest-performing percentage of runs at evaluation intervals based on the distribution of all runs. Bandit compares against the best-performing run, and Default applies no early termination policy.
Question 18
DRAG DROP
-
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Load the model when the service starts: init()
Use the model to score new data: run()
Explanation: In Azure Machine Learning real-time inference entry scripts, init() runs once at container startup to load the model, while run(data) is invoked for each request to perform scoring.
Question 19
A team is validating a generative AI assistant for a company. The assistant generates responses by using internal knowledge sources.
The company requires assurance that responses are accurate, supported by sources, and related to the user prompts before enabling production access.
You need to implement quality metrics that confirm the assistant produces reliable and meaningful responses.
Which two evaluation metrics should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Groundedness
B. Relevance
C. Harmfulness
D. Tone
E. Fairness
Show Answer
Correct Answer: A, B
Explanation: Groundedness evaluates whether responses are supported by the provided knowledge sources, helping ensure factual accuracy and traceability. Relevance evaluates whether the response appropriately addresses the user's prompt. These directly match the requirement to verify responses are accurate, source-supported, and related to the prompt. Harmfulness, Tone, and Fairness assess safety, style, or bias rather than source support and prompt alignment.
Question 20
HOTSPOT
-
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation: Bandit stops runs that perform worse than the best run by more than a specified tolerance. A 25% performance tolerance relative to the best run is configured with slack_factor.
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