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| Section | Objectives |
|---|---|
| Topic 1: 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 |
| Topic 2: Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
| Topic 3: Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints |
| Topic 4: Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 5: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering |
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NEW QUESTION # 41
A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.
The team requires a centralized way to track, version, and reuse prompt content across projects.
You need to recommend a solution to track and reuse prompt content.
Which approach should you recommend?
Answer: A
Explanation:
A Git repository provides all three capabilities required: version history for every change with author attribution and timestamp, branching for experimentation, tagging for stable releases, and a single source of truth accessible to all projects via clone or submodule reference. Azure ML datasets (option B) are designed for training data, not text configuration artifacts, and lack per-line diff tracking and review workflows.
Embedding prompts in application configuration files (option C) scatters prompt content across multiple applications with no unified view, no shared versioning, and no cross-project reuse. Unstructured Blob Storage with folder organization (option D) provides no version history, no diff tracking, no review workflow, and no native tooling for comparing prompt variants. Git is the definitive answer for centralized tracking, versioning, and reuse of prompt content across Microsoft Foundry projects.
Microsoft Learn Reference Topic: Prompt engineering and version control - Managing prompts in AI applications with Git
NEW QUESTION # 42
An organization operates a customer-facing generative AI chat service deployed by using Microsoft Foundry. The service processes a predictable, sustained volume of requests. The service must meet strict response time service-level agreements (SLAs) during peak business hours.
The organization requires that:
- Model responses remain consistent during sustained high traffic.
- Latency does not degrade during peak usage periods.
- Capacity planning avoids throttling and unpredictable performance.
You need to ensure that the deployed foundation model can reliably handle sustained, high- volume traffic while meeting performance and availability requirements.
What should you do?
Answer: C
Explanation:
Implement spillover traffic management for excess demand is the best action among the provided options to ensure the system gracefully maintains availability and handles sustained, high-volume traffic without degradation or unpredictable throttling under strict SLAs.
Under strict SLAs and predictable, sustained peak volumes, Azure AI Foundry utilizes provisioned throughput (PTUs) to guarantee latency and throughput. However, if traffic spikes beyond predictable levels, a spillover strategy redirects excess demand to standard pay-as-you- go slots or alternative regions. This prevents the primary deployment from throttling, dropping requests, or suffering from latency degradation.
Reference:
https://smartbridge.com/azure-openai-service-guide/
NEW QUESTION # 43
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 auth.mode parameter to aml.token. Deploy the model to the online endpoint.
Does the solution meet the goal?
Answer: A
NEW QUESTION # 44
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
Retrieved results frequently include duplicated content from the same document.
Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:
You need to reduce duplicated retrieval results and improve chunk relevance across policy sections.
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:
Two distinct RAG problems require two distinct solutions. When retrieved results frequently include duplicated content from the same document, the cause is overlapping chunks receiving similarly high similarity scores. The solution is Maximum Marginal Relevance reranking, which diversifies the result set by penalizing results that are semantically similar to already-selected results, reducing redundancy. When retrieved chunks sometimes span unrelated policy sections, the cause is fixed-size character chunking that splits content without regard for semantic boundaries. The solution is semantic chunking - splitting on natural sentence or section boundaries - ensuring each chunk is semantically coherent and does not straddle unrelated content. Both problems must be addressed together: reranking alone solves duplication but not relevance; semantic chunking alone solves relevance but not duplication.
Microsoft Learn Reference Topic: Optimize RAG retrieval in Azure AI Search - Chunking strategies and MMR reranking
NEW QUESTION # 45
A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.
The team requires a centralized way to track, version, and reuse prompt content across projects.
You need to recommend a solution to track and reuse prompt content.
Which approach should you recommend?
Answer: A
Explanation:
In Microsoft Foundry, centralizing prompt templates in a Git repository (such as GitHub) is a recommended GenAIOps practice to ensure consistency, versioning, and reuse across multiple AI applications.
Storing prompts as versioned files in a Git repository is an excellent approach for Microsoft Foundry applications. It treats prompts like code (PromptOps), enabling clear audit trails, peer reviews via Pull Requests, and easy rollbacks.
To make this centralized system effective, you should consider these three components:
Centralized Repository: Use a single "Prompt Bank" repo where templates are stored in a structured format like JSON or YAML. Include metadata like model version, temperature, and input variables.
Prompt Registry / SDK: Develop a small internal utility (or use a tool like Azure AI Studio's Prompt Flow) that allows applications to fetch the "latest" or a "tagged" version of a prompt via API or as a Git submodule.
CI/CD Integration: Automate testing so that when a prompt file is updated, it triggers a "prompt evaluation" pipeline to ensure the change doesn't degrade AI performance across the dependent projects.
Reference:
https://www.getmaxim.ai/articles/best-practices-for-prompt-management-in-ai-applications
NEW QUESTION # 46
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