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| Section | Objectives |
|---|---|
| Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
| 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 |
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks |
| Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health |
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NEW QUESTION # 179
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
When evaluating a fine-tuned model against the base model using the same evaluation dataset, the interpretation of each metric requires careful analysis. A statement that the fine-tuned model improves on the target task is True if and only if the target metric such as task-specific accuracy, F1 score, or ROUGE score shows a statistically meaningful improvement. A statement about regression on a complementary metric is True if the fine-tuned model ' s score on that metric is meaningfully lower than the base model ' s. In Microsoft Foundry ' s evaluation framework, both pre-fine-tuning and post-fine-tuning results are stored against the same experiment, enabling direct side-by-side comparison. The core principle is that improvement on the primary task is not sufficient if fine-tuning causes degradation on safety or coherence - this is called catastrophic forgetting, and the evaluation dataset is designed to detect it.
Microsoft Learn Reference Topic: Evaluate fine-tuned models in Microsoft Foundry - Compare base and fine-tuned model metrics
NEW QUESTION # 180
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: Clone an existing Azure Machine Learning workspace to create additional environments.
Does the solution meet the goal?
Answer: A
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 # 181
A pipeline step fails intermittently due to transient compute issues. You need to improve reliability without modifying core logic or increasing cost significantly. What is the BEST approach?
Answer: D
Explanation:
Retry policies allow pipeline steps to automatically recover from transient failures, such as temporary compute or network issues. This improves reliability without modifying core logic or increasing infrastructure costs. Increasing compute resources does not address transient failure scenarios effectively.
NEW QUESTION # 182
A team manages prompts used by a generative AI application built on Microsoft Foundry. Prompt updates are frequent, and prior changes have caused unexpected behavior in production.
The team requires that new prompt versions can be tested and reviewed without affecting production behavior.
You need to implement a source control practice that allows safe prompt experimentation.
What should you do?
Answer: A
Explanation:
Creating a feature Git branch for each prompt change is an excellent and highly recommended action.
Prompts are code. Managing them through a standard software development lifecycle prevents production failures and allows for safe experimentation.
Isolation: Changes stay completely separate from the working production version.
Collaboration: Teams can review prompt tweaks together via Pull Requests (PRs).
Traceability: You can easily see who changed a prompt, when, and why.
Rollbacks: If a prompt performs poorly, you can instantly revert to a prior Git commit.
Reference:
https://mirascope.com/blog/prompt-management-system
NEW QUESTION # 183
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For tracking changes across contributors, Git integration is the answer: by connecting the Microsoft Foundry project to a Git repository, every prompt file change is tracked as a commit with author attribution, timestamp, and diff view, and pull requests enforce review before changes reach production. The Git history provides the complete audit trail and rollback capability needed for traceability. For allowing applications to consume updated prompts without requiring redeployment, Microsoft Foundry ' s prompt management feature allows prompts to be stored and versioned as named artifacts in the project. Applications reference prompts by name and load the latest approved version at inference time, rather than having prompt text hard-coded in the application deployment artifact. This decoupling means updating a prompt is a content operation - not a code deployment - so applications automatically pick up the new prompt without any redeployment.
Microsoft Learn Reference Topic: Prompt management in Microsoft Azure AI Foundry - Git integration and dynamic prompt versioning
NEW QUESTION # 184
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