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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 2: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 3: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 4: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 5: Implement agentic solutions | 20-25% | - Build AI agents
|
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NEW QUESTION # 94
Drag and Drop Question
You have a Microsoft Foundry project that contains a deployed ticket-triage agent.
You discover that sometimes the agent responds without calling any tools, even when a tool is required.
You need to ensure that the agent calls a tool during execution.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 95
You need to recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1.
What should you include in the recommendation?
Answer: A
Explanation:
The correct recommendation is Azure AI Search. The case study states that the product detail sheets are stored as PDFs in storage1, and that Agent1 must be enabled to retrieve and use detailed product information from those sheets. It also specifies that the indexing pipeline must enable semantic and vector search, and that Agent1 must answer natural language questions about product details by using the product sheet information.
Azure AI Search is the Azure service designed to ingest content from sources such as Azure Blob Storage, create searchable indexes, and support keyword, semantic, hybrid, and vector retrieval for Retrieval Augmented Generation (RAG) solutions.
Microsoft's Azure AI Search guidance states that integrated vectorization can chunk content and generate embeddings during indexing, enabling vector search over source documents. It also states that Azure AI Search supports text and vector queries and can improve raw content for search-related scenarios through enrichment pipelines. Azure Translator is unrelated to retrieval. Document Intelligence can extract document structure, but it is not the retrieval index for Agent1. Grounding with Bing Search retrieves public web content, not Contoso's private PDFs in storage1. Reference topics: Azure AI Search, RAG, semantic search, vector search, Azure Blob Storage indexing, and agent grounding.
NEW QUESTION # 96
In Microsoft Foundry, you use the Chat playground with the GPT-35 Turbo model. You have a prompt that contains the following code.
You need the model to create an explanation of the code. The solution must minimize costs. What should you do?
Answer: B
Explanation:
Add the natural-language comment // what does function F do? after the code. The comment gives GPT-35 Turbo a direct and unambiguous instruction to analyze and explain function F. Because it uses syntax that naturally belongs beside source code, the model can distinguish the program being analyzed from the requested task and generate an explanatory completion. Microsoft's prompt-engineering guidance recommends clearly stating the required outcome and using cues that direct the model toward the desired response.
This approach retains the existing GPT-35 Turbo deployment and adds only a small number of prompt tokens, satisfying the cost-minimization requirement. Changing to GPT-4-32k would be unnecessary because the requirement is a straightforward code-explanation task and does not indicate that a substantially larger context window is required.
Adding function F(explanation) resembles a new function declaration or invocation rather than an instruction to explain existing code. The model could interpret it as source code that must be completed. Setting temperature to 1 changes output randomness but does not tell the model to explain the function; it can also make the answer less consistent.
Study Guide alignment: Use prompt-engineering techniques, construct clear model instructions, tune generation behavior, and select an appropriate model based on quality and cost requirements.
NEW QUESTION # 97
You have a Microsoft Foundry project that contains two agents named PolicyWriter and RiskReviewer.
PolicyWriter generates draft updates for customer policies, and RiskReviewer reviews the drafts. In the visual builder, you need to create a workflow that meets the following requirements:
* Finalizes low-risk updates without manual intervention
* Ensures predictable execution across the agents
Answer:
Explanation:
Explanation:
* Orchestration pattern: The sequential template that passes outputs node-by-node
* Approval checkpoints: Add a condition statement
Use the sequential orchestration template because the agents must execute in a fixed, predictable order.
PolicyWriter first generates the proposed policy update, and its output is then passed directly to RiskReviewer. Microsoft defines the sequential pattern as passing the result from one agent to the next in a defined order, making it appropriate for deterministic, multistage processing. The group-chat pattern would allow control to move dynamically between agents, which would reduce execution predictability.
After RiskReviewer returns a structured risk classification, add a condition statement that evaluates the result. The low-risk branch can proceed directly to the finalization action without requesting human input. A separate higher-risk branch can route to an approval step, such as an Ask a question node. Adding an Ask a question node without conditional branching would pause every workflow execution, including low-risk updates, and therefore would not satisfy the automation requirement. Microsoft Foundry workflows support if
/else branching and condition expressions for selecting the next action.
Study Guide alignment: implement orchestrated multi-agent solutions and build autonomous or semiautonomous workflows with safeguards and approval-flow controls .
NEW QUESTION # 98
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.
You need to provide scores for groundedness, relevance, and harmful content categories.
Which two evaluation categories can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
Explanation:
The two evaluation categories that would be of use are AI quality (AI assisted) metrics and risk and safety metrics Risk and safety metrics: Scoring harmful content categories (such as violence, sexual content, hate speech, and self-harm) is handled directly by the specialized safety evaluators contained within this category.
AI quality (AI assisted) metrics: In Microsoft Foundry, evaluating RAG-specific criteria like groundedness and relevance requires large language models (LLMs) acting as a judge to assess semantic context. These are explicitly categorized under AI-assisted quality metrics rather than standard natural language processing (NLP) rules.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/evaluation-evaluators/rag-evaluators
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/evaluate-sdk
NEW QUESTION # 99
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