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| Section | Objectives |
|---|---|
| Topic 1: Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - 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 |
| Topic 2: Design and implement an MLOps infrastructure | - 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 - Implement security, governance, and compliance for MLOps |
| Topic 3: Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
| Topic 4: Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks |
| Topic 5: 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 |
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NEW QUESTION # 85
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 # 86
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
Answer: A
Explanation:
The team cannot rely on live user traffic for evaluation because it would make results non-reproducible and could expose users to untested prompts. Synthetic data generation within Microsoft Foundry allows the team to produce diverse, representative input examples that simulate real user queries without any live traffic risk, giving a stable, consistent test set evaluated under identical conditions. Option B (content filters) and Option C (blocklists) are safety controls applied at inference time, not evaluation inputs. Option D (observability metrics) is a monitoring capability for production systems. With synthetic data, the team can construct edge cases, varied phrasings, and domain-specific scenarios that would take months to accumulate organically from real users, directly enabling the controlled evaluation the question requires without any dependency on live traffic.
Microsoft Learn Reference Topic: Evaluate generative AI apps with synthetic data - Microsoft Foundry prompt evaluation
NEW QUESTION # 87
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
Answer: B
Explanation:
To fine-tune a model in the Azure OpenAI Service, your training data must be in JSONL (JSON Lines) format.
File Requirements
Format: Each line of the file must be a single, valid JSON object.
Encoding: The file must be encoded in UTF-8.
Special Requirement: For some older tools or specific Azure OpenAI use cases, the file may need to include a byte-order mark (BOM).
Size Limits: Individual files typically have a maximum size (e.g., 100 MB per file in some contexts), but you can often upload multiple files.
Reference:
https://dev.to/icebeam7/fine-tuning-a-model-with-azure-open-ai-studio-39p7
NEW QUESTION # 88
A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team ' s organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?
Answer: A
Explanation:
Data Zone Standard deployment routes requests to multiple datacenters within a defined geographic zone such as the US data zone, providing lower latency by using zone-level load balancing while maintaining data within a broad compliance boundary. This makes it suitable for enterprise internal applications where multiple business units need low-latency access without strict single-region data residency requirements. Developer deployment (option A) is a low-quota, unguaranteed tier designed for prototype testing, not production workloads serving multiple business units. Data Zone Batch (option B) is designed for high-volume asynchronous batch processing, not interactive low-latency real-time requests. Global Batch (option D) is optimized for scheduled bulk inference across all global regions but adds latency and is inappropriate for synchronous interactive use cases.
Microsoft Learn Reference Topic: Foundation model deployment types in Microsoft Foundry - Data Zone Standard for low-latency internal use
NEW QUESTION # 89
A team runs training and inference jobs in Azure Machine Learning.
The team experiences inconsistent runtime dependencies that cause variation in results.
You need to ensure that all jobs use the same execution dependencies.
Which asset should you define?
Answer: C
Explanation:
To guarantee consistent runtime dependencies in Azure Machine Learning, you must use Azure ML Environments configured with custom Docker images or pinned Conda dependencies.
An Environment asset encapsulates the exact Python packages, environment variables, and software settings for your training and inference workloads.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets
NEW QUESTION # 90
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