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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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NEW QUESTION # 86
You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?
Answer: B
Explanation:
Reproducibility across environments depends on consistent versioning of datasets and environments. Differences in data or dependencies can lead to inconsistent results even if compute resources are identical. Proper asset versioning ensures experiments can be reliably reproduced in any workspace or region.
NEW QUESTION # 87
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: B,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 # 88
You create an Azure Machine Learning workspace and install the MLflow library.
You need to tog different types of data by using the MLflow library.
Which method should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 89
Your model requires access to external APIs using sensitive credentials during inference. You must ensure credentials are not exposed in code, logs, or environment variables. What should you implement?
Answer: D
Explanation:
Azure Key Vault with managed identity ensures secure access to sensitive credentials without exposing them in code or configuration. Managed identities eliminate the need for hardcoded secrets. Other approaches, such as config files or environment variables, increase the risk of accidental exposure.
NEW QUESTION # 90
Hotspot Question
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
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:
NEW QUESTION # 91
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