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| Section | Objectives |
|---|---|
| Topic 1: Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows - Implement security, governance, and compliance for MLOps |
| Topic 2: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates |
| Topic 3: Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
| Topic 4: Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health |
| Topic 5: 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 - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails |
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NEW QUESTION # 170
Drag and Drop Question
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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 # 171
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: Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
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 # 172
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.
Answer: B,C
Explanation:
Groundedness and Relevance directly measure the two quality characteristics specified in the requirement.
Groundedness evaluates whether the generated response is supported by the supplied knowledge context rather than introducing unsupported or fabricated information. Microsoft Foundry defines the Groundedness evaluator as measuring how well a generated response aligns with the provided context without fabricating content. For an assistant that relies on internal knowledge sources, this is the primary metric for determining whether its claims are traceable to retrieved evidence.
Relevance evaluates whether the generated response actually addresses the user ' s query. Microsoft defines the Relevance evaluator as measuring how relevant the response is to the query, including whether it appropriately and accurately responds to the user ' s request. This detects responses that may be factually grounded yet tangential or insufficiently focused.
Harmfulness and Fairness are safety-oriented dimensions rather than measures of factual support or query alignment. Tone assesses communication characteristics but does not establish whether a response is supported by source material or relevant to the prompt.
Therefore, the required pair is Groundedness + Relevance .
Study Guide Reference: Implement generative AI quality assurance and observability - Foundry quality evaluators, groundedness, relevance, RAG evaluation, and pre-production validation.
NEW QUESTION # 173
You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 174
You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?
Answer: A
Explanation:
An appropriate base model for fine-tuning with text and image inputs in the Microsoft Foundry portal is GPT-4o (specifically versions like gpt-4o-2024-05-13 or newer). This model supports multi-modal training data, allowing you to improve performance on tasks requiring both visual understanding and textual response within Azure OpenAI Service.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-new-fine-tuning- capabilities-with-images-on-azure-openai-service/4303695
NEW QUESTION # 175
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