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| Section | Objectives |
|---|---|
| Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health |
| Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications |
| Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput |
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NEW QUESTION # 170
An organization operates a generative AI application in production by using Microsoft Foundry. The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve.
The application intermittently times out during production use, which requires ongoing visibility into runtime behavior.
The team must also validate model quality and safety before releasing new updates to avoid introducing regressions.
You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation.
Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism 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:
The two requirements target fundamentally different stages of the AI application lifecycle. Continuous runtime monitoring addresses production behavior - an application that intermittently times out needs real- time visibility into latency, error rates, and request volumes for every call in production. Tracing backed by Application Insights or OpenTelemetry exporters is correct here because it captures granular, per-request telemetry continuously without requiring manual triggering - it is always-on by design. Release-time validation addresses pre-release quality gates - before new prompts or model updates go live, you need to verify they have not introduced regressions. An evaluation pipeline in Microsoft Foundry ' s prompt flow is correct here: it runs the updated application against a predefined test dataset, scores outputs on quality and safety metrics, and passes or fails the release based on thresholds. Evaluation pipelines run on demand triggered by CI/CD, not continuously.
Microsoft Learn Reference Topic: Microsoft Foundry observability - Tracing for runtime monitoring vs.
Evaluation pipelines for release gates
NEW QUESTION # 171
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 # 172
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 in the review screen.
You train and register an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint and set the value of its auto_mode parameter to key. Deploy the model to the inline endpoint.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 173
You have an Azure Machine Learning workspace.
You plan to set up logging and tracking experiments by using MLflow Tracking.
You need to log the accuracy as a numerical value and the training loss as a plot.
How should you complete the commands? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 174
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.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
This action is best because it fulfills all of your operational requirements:
Consistency & Controlled Manner:
Infrastructure as Code (IaC) via Bicep ensures that dev, test, and prod environments are configured identically, eliminating configuration drift.
Source Control: Storing the Bicep template in a Git repository satisfies the requirement that each environment must be updated through source control.
Automated & Repeatable: Orchestrating the deployment using GitHub Actions fully automates the workflow, allowing it to be executed reliably every time a change is merged.
Auditable: Git commit histories combined with GitHub deployment logs provide a comprehensive, compliant audit trail of exactly who modified the infrastructure and when.
Incorrect:
* Clone an existing Azure Machine Learning workspace to create additional environments.
* Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning
NEW QUESTION # 175
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