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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 | - RAG (Retrieval Augmented Generation) solutions
|
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NEW QUESTION # 183
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupiter notebook in the workspace. The experiment must log string metrics.
You need to implement the method to log the string metrics.
Which method should you use?
Answer: A
NEW QUESTION # 184
You manage an Azure Machine learning workspace named workspace1.
You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the Compute instance class.
You need to add the instantiated compute instance to workspace 1.
What should you use?
Answer: B
NEW QUESTION # 185
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.
Answer: C,D
Explanation:
Median stopping and Truncation selection both evaluate a run relative to the broader population of training runs rather than comparing it only against the single best run.
The Median Stopping Policy calculates running averages of the primary metric across all runs . At an evaluation interval, a run can be terminated when its best primary-metric performance is worse than the median of the running averages across the population. Microsoft explicitly defines this policy as being based on running averages of the primary metric of all runs.
The Truncation Selection Policy periodically ranks runs by their primary metric and terminates a configured percentage of the lowest-performing runs . For example, a 20-percent truncation policy terminates runs falling within the lowest 20 percent at the applicable evaluation interval. It therefore compares each candidate with the population of comparable runs rather than only the current leader.
Bandit is specifically unsuitable because it uses an allowable slack relative to the best-performing run , which directly violates the requirement. The Default behavior applies no early termination policy and allows runs to execute to completion.
Study Guide Reference: Implement machine learning model lifecycle and operations - automated hyperparameter tuning, early termination policies, Median Stopping Policy, Truncation Selection Policy, and efficient experiment execution.
NEW QUESTION # 186
You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?
Answer: B
Explanation:
Traffic splitting enables controlled rollout of a new model version by directing a percentage of requests to it. Combined with monitoring alerts, it supports automated rollback when performance degrades. Separate endpoints lack built-in traffic management and do not provide seamless or automated rollback capabilities.
NEW QUESTION # 187
A team manages prompts that are used by a generative AI application built on Microsoft Foundry. Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
The team requires that:
Prompt changes are reviewed before being applied to the version in production.
Previous prompt versions can be restored if issues occur.
Prompt updates follow the same governance practices as the application code.
You need to implement a controlled process for managing and updating prompts in production.
How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each action 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:
Explanation:
All three requirements point to Git as the underlying mechanism, but each uses a different Git capability. For reviewing changes before production: a branch-based workflow where prompt changes are made on feature branches and merged to the main branch only after pull request approval enforces the review gate. For restoring previous versions: Git ' s commit history and tag system provide a precise, immutable record of every prompt state, and a git revert or checkout to a specific commit SHA restores any prior version instantly.
For governance parity with application code: by storing prompts in the same Git repository as application code, all the same CI/CD, branch protection, code review, and audit trail policies apply automatically. The alternatives such as Blob Storage or embedded configuration files lack native review workflows, branch protection, and full audit history.
Microsoft Learn Reference Topic: Prompt management and versioning with Git integration in Microsoft Foundry
NEW QUESTION # 188
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