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| Section | Objectives |
|---|---|
| Topic 1: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Topic 2: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 3: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 4: Design and implement generative AI solutions | - Large language model integration
|
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NEW QUESTION # 162
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.
Answer:
Explanation:
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
NEW QUESTION # 163
A team runs training jobs by using multiple Azure Machine Learning pipelines.
The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
You need to configure the workspace so that runtime dependencies are consistent and reusable.
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.
Answer:
Explanation:
Explanation:
To ensure runtime dependencies are consistent and reusable, first create a conda.yaml or requirements.txt file that lists all Python packages and system libraries required by your training code - this file is the single source of truth for your runtime. Next, create an Environment object using the Azure ML Python SDK v2 with a name and reference to the conda.yaml file, specifying the base Docker image. Then register the Environment by calling ml_client.environments.create_or_update, which publishes it to the workspace registry with an auto-incremented version. Finally, reference the registered environment by name and version in all pipeline job steps. Azure ML will build or retrieve the cached Docker image and use it as the execution container. This approach means updating dependencies only requires modifying the conda.yaml and registering a new version - training code remains unchanged.
Microsoft Learn Reference Topic: Create and manage Azure Machine Learning environments - Reusable curated environments
NEW QUESTION # 164
A company ' s platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
Answer: A,D
Explanation:
Microsoft ' s Azure AI Foundry documentation defines built-in roles scoped to the resource hierarchy. The Azure AI Administrator role grants permissions to manage the Azure AI Hub resource itself - including network settings, shared connections, quota, and governance configuration - which is appropriate for platform engineers who configure resource-level settings. The Azure AI Developer role grants permissions to create, update, and manage project-level assets such as deployments, prompt flows, fine-tuning jobs, and evaluations, without access to resource-level configuration. This precisely matches the developer requirement and enforces least privilege. Disabling Entra ID authentication (option B) violates security policy and removes the identity-based access control that makes RBAC possible. Sharing a single API key (option D) violates the least-privilege principle - all users would have identical, undifferentiated access with no audit trail.
Microsoft Learn Reference Topic: Manage access to Azure AI Foundry - Built-in roles: Azure AI Administrator and Azure AI Developer
NEW QUESTION # 165
You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named .amlignore. The directory also contains subdirectories named ./outputs and ./logs.
There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named .gitignore in the root of the directory. You add the names of the 20 files to the .gitignore file. These 20 files continue to be copied to the compute targets.
You need to exclude the 20 files.
What should you do?
Answer: C
Explanation:
To exclude the 20 files from the training snapshot, you must add the file names to the .amlignore file located in the root of your training scripts directory.
Why the Files Are Still Copying
Azure Machine Learning training experiments use a specific order of precedence when creating a directory snapshot for compute targets:
.amlignore takes absolute priority: If an .amlignore file exists in the directory, Azure ML only respects the rules inside it. It completely ignores any .gitignore file present.
.gitignore is a fallback: Azure ML only respects .gitignore rules if an .amlignore file does not exist in the directory. Because you have both files, the .gitignore file is being completely bypassed.
Reference:
https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment
NEW QUESTION # 166
Drag and Drop Question
An organization is deploying generative AI solutions by using Microsoft Foundry to support multiple production workloads.
The organization has the following workload requirements:
- One workload must be real-time, latency-sensitive, and have
predictable global usage patterns that demand consistent performance.
- One workload must have variable performance and be optimized for
cost-efficient operation.
You need to select a global deployment type for each workload.
Which type of deployment should you use for each workload requirement? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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.
Answer:
Explanation:
NEW QUESTION # 167
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