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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Implement secure and scalable AI systems | - Security and governance
|
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NEW QUESTION # 13
Drag and Drop Question
A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK.
The evaluation dataset is registered in the Microsoft Foundry environment.
You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 14
You are preparing training data for a fine-tuning job in Microsoft Foundry.
Real production conversations cannot be used due to compliance requirements.
You need to generate synthetic interaction data that can be used for fine-tuning a generative model.
What should you do?
Answer: C
Explanation:
You can use a simulator or an LLM-as-a-judge pipeline to generate synthetic interaction data for fine-tuning. This technique is standard practice for maintaining strict data privacy while training models on specific business tasks.
Here is how to effectively structure and execute a synthetic data generation pipeline for Microsoft Azure AI Foundry (formerly Azure AI Studio).
Generation Methods
Persona-Driven Simulation: Prompt one LLM to act as a customer and another as your support agent to generate multi-turn dialogues.
Seed Data Expansion: Feed 10-20 hand-written, compliant examples into an LLM and instruct it to generate hundreds of diverse variations.
Schema-Based Evolution: Define variables (e.g., product types, user intents, sentiment levels) and programmatically combine them into prompt templates for an LLM to flesh out.
Reference:
https://www.digitaldividedata.com/blog/synthetic-data-generation-in-gen-ai
NEW QUESTION # 15
A company ' s platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
Answer: C,D
Explanation:
Microsoft ' s Azure AI Foundry documentation defines built-in roles scoped to the resource hierarchy. The Azure AI Administrator role grants permissions to manage the Azure AI Hub resource itself - including network settings, shared connections, quota, and governance configuration - which is appropriate for platform engineers who configure resource-level settings. The Azure AI Developer role grants permissions to create, update, and manage project-level assets such as deployments, prompt flows, fine-tuning jobs, and evaluations, without access to resource-level configuration. This precisely matches the developer requirement and enforces least privilege. Disabling Entra ID authentication (option B) violates security policy and removes the identity-based access control that makes RBAC possible. Sharing a single API key (option D) violates the least-privilege principle - all users would have identical, undifferentiated access with no audit trail.
Microsoft Learn Reference Topic: Manage access to Azure AI Foundry - Built-in roles: Azure AI Administrator and Azure AI Developer
NEW QUESTION # 16
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 for multi-turn chat.
Which file encoding method should you use?
Answer: A
Explanation:
UTF-8 is the universal encoding standard: it is backward-compatible with ASCII for standard Latin characters, supports every Unicode code point covering over 140,000 characters, and is the de facto standard for all modern APIs including Azure OpenAI. This matters for fine-tuning because training data often contains diverse characters from multiple languages, special punctuation, and domain-specific symbols. If they use ASCII encoding, any accented character or non-Latin script will be corrupted or lost entirely. UTF-
16 adds a Byte Order Mark and can cause parsing issues with tools that expect standard JSONL. ISO-8859-1 covers Western European characters only and fails immediately with CJK or Arabic scripts. The Azure OpenAI fine-tuning documentation explicitly states that training files must be UTF-8 encoded, making it the only safe choice.
Microsoft Learn Reference Topic: Azure OpenAI fine-tuning - File format and encoding requirements
NEW QUESTION # 17
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression.
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:
When evaluating a fine-tuned model against the base model using the same evaluation dataset, the interpretation of each metric requires careful analysis. A statement that the fine-tuned model improves on the target task is True if and only if the target metric such as task-specific accuracy, F1 score, or ROUGE score shows a statistically meaningful improvement. A statement about regression on a complementary metric is True if the fine-tuned model ' s score on that metric is meaningfully lower than the base model ' s. In Microsoft Foundry ' s evaluation framework, both pre-fine-tuning and post-fine-tuning results are stored against the same experiment, enabling direct side-by-side comparison. The core principle is that improvement on the primary task is not sufficient if fine-tuning causes degradation on safety or coherence - this is called catastrophic forgetting, and the evaluation dataset is designed to detect it.
Microsoft Learn Reference Topic: Evaluate fine-tuned models in Microsoft Foundry - Compare base and fine-tuned model metrics
NEW QUESTION # 18
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