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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 2: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 4: Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Topic 5: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
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NEW QUESTION # 115
Hotspot Question
You manage an Azure OpenAI deployment of the gpt-4o base model.
You plan to fine-tune the deployed model.
You need to prepare a file that contains training data.
Which keys should you include in each line of the training data file? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For fine-tuning conversational models like gpt-4o on Azure OpenAI, the training file must be formatted in a JSON Lines (.jsonl) file, where each line represents a single training conversation.
Each line must strictly contain a messages key wrapping an array of message objects.
Box 1: role
role: Defines the sender of the message. The accepted strings for fine-tuning are "system",
"user", or "assistant".
Box 2: content
content: Contains the actual text string or multi-modal payload of the message.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/reinforcement-fine-tuning
NEW QUESTION # 116
A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution.
(Choose two.)
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
To achieve reliable, reusable, and adaptable prompts without retraining the model, you should implement Prompt Flow, use System Messages for tone/role definition, and enforce Output Parsing. These techniques standardize how the model behaves across all channels.
Here is the most effective approach to stabilize your customer support assistant:
[B]
1. Implement Prompt Flow
Instead of hardcoding raw prompts in your code, use Azure Machine Learning Prompt Flow.
Visual Workflows: Create executable flows that link LLMs, prompts, and Python tools.
Versioning: Easily track, iterate, and roll back prompt versions without touching application code.
Evaluation: Test prompt variants systematically against a set of baseline queries to objectively measure factual accuracy and tone before pushing to production.
[C]
2. Standardize System Messages
A well-defined system message serves as the "guardrails" for your assistant. It should dictate the persona, scope of knowledge, and safety rules.
Define the Persona: Instruct the model exactly how to behave (e.g., "You are a helpful, empathetic, and strictly technical support assistant for [Company Name].").Strict Guidelines:
Provide instructions on how to handle out-of-scope queries (e.g., "If you do not know the answer, do not guess. Apologize and route the user to [Support Email].").
Reference:
https://whitebeardstrategies.com/blog/5-best-practices-for-efficient-language-model-prompting/
NEW QUESTION # 117
You manage an Azure Machine Learning workspace.
You must log multiple metrics by using MLflow.
You need to maximize logging performance.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
NEW QUESTION # 118
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 # 119
You need to recommend an experiment-tracking strategy that ensures consistent experiment results.
What should you recommend?
Answer: D
Explanation:
MLflow is the industry-standard open-source platform for experiment tracking, and Azure Machine Learning has first-class native integration with it. When you use MLflow within an Azure ML job, parameters, metrics, and artifacts are automatically logged to the run history of the AML workspace, making every run reproducible and comparable. Option A (AML job output logs) only captures console output and lacks structured parameter and metric logging. Option C (Application Insights logs) is designed for application- level telemetry, not ML experiment metadata. Option D (Azure Monitor alerts) is a reactive notification tool, not a tracking system. MLflow ' s autologging capability means that for common frameworks such as scikit- learn, XGBoost, and PyTorch, parameters and metrics are captured without a single line of custom code, directly answering Fabrikam ' s requirement for consistent experiment tracking.
Microsoft Learn Reference Topic: Track ML experiments with MLflow - Azure Machine Learning MLflow integration
NEW QUESTION # 120
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