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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
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NEW QUESTION # 55
You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
- Multiple client applications must use the same search configuration.
- A security policy must prevent key-based authentication.
- Administrative effort must be minimized.
What should you do?
Answer: C
Explanation:
To meet your security and architecture requirements, you must add the Azure AI Search instance as a Connection within your Azure AI Foundry project and configure Managed Identities for role- based access control (RBAC).
To securely unify your search configuration without API keys, add the Azure AI Search instance as a shared Connection in your Azure AI Foundry project, disable key authentication on the search service, and authorize your applications using Azure RBAC and Managed Identities.
Note:
*-> 1. Create a Project Connection
Connect Azure AI Search directly inside the Azure AI Foundry hub or project.
*-> Share the same search service configuration across all connected client applications automatically.
Centralize your search endpoint details to reduce administrative overhead.
2. Disable Key Authentication
3. Enable Managed Identities
4. Update the Web App Code
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/tutorials/copilot-sdk-create-resources
NEW QUESTION # 56
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed line items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
* Extracts the invoice number, invoice date, vendor name, and total amount across varying templates
* Returns confidence scores so that results with confidence below 0.80 can be routed for supervisor review What should you use?
Answer: A
Explanation:
The correct answer is C because the requirement is structured field extraction from invoices across varying templates, not only OCR or layout preservation. Azure Content Understanding analyzers are reusable configurations that combine content extraction, AI-powered analysis, and structured data output, and Microsoft states that custom analyzers can be created for specific extraction needs. In this case, the analyzer schema should define fields such as invoice number, invoice date, vendor name, and total amount so the output can be returned as structured JSON for downstream RAG grounding.
The confidence-routing requirement also points to Content Understanding field confidence scores. Microsoft documentation states that every field can include a confidence score from 0 to 1, and that confidence scores can be used to automate high-confidence results while routing low-confidence results for human review. A threshold such as 0.80 is therefore an application routing rule based on the returned field confidence. The prebuilt-layout analyzer preserves layout but does not define invoice-specific business fields. Groundedness guardrails evaluate generated answers, not invoice field extraction. Azure AI Search search.score measures retrieval relevance, not extraction confidence. Reference topics: Content Understanding custom analyzers, document field extraction, structured JSON output, confidence scoring, and RAG grounding.
NEW QUESTION # 57
Hotspot Question
You have a Microsoft Foundry project that contains a workflow for a customer support triage process.
You have an Ask a question node that stores user responses in a local variable named Var01.
You need to create the following Power Fx expressions:
- An if/else condition expression that ensures that Var01 contains a
value
- A Send message expression that returns the stored user response in
uppercase
How should you configure the expressions? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 58
Drag and Drop Question
You are developing an application that will detect faulty components produced on a factory production line. The components are specific to your business.
You need to use the Azure Custom Vision API to help detect common faults.
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:
Explanation:
Step 1: Create a project
You must first set up a new project in the Custom Vision portal or via the SDK to store your data and configuration.
Step 2: Upload and tag images
Next, you need to upload photos of your specific factory components and draw bounding boxes around the faults to tag them.
Step 3: Train the object detection model
Because you need to identify and locate specific, localizable faults on a component, you must train an object detection model rather than a general classifier model.
Reference:
https://thegroundtruth.blog/tag/computervision/
NEW QUESTION # 59
You have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses.
Users report the following issues:
* Some requests take more than 15 seconds to complete.
* Some responses are incorrect, even when the knowledge API returns the expected data.
You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls, tool invocations, and timing information.
Which observability capability should you use?
Answer: A
Explanation:
The correct capability is tracing because the requirement is to inspect the execution path of an individual agent run. Microsoft Foundry tracing captures detailed telemetry for agent behavior, including LLM calls, tool invocations, agent decision flows, inputs, outputs, tool results, token consumption, duration, and latency.
This is the appropriate observability mechanism when you need to determine which step introduced a delay, whether the agent called the internal knowledge API, what data the tool returned, and how the model used that data before producing the final response. Microsoft's Foundry observability guidance describes distributed tracing as the mechanism that provides visibility into LLM calls, tool invocations, agent decisions, and inter-service dependencies.
Token usage is useful for cost analysis and prompt optimization, but it does not show ordered run steps or tool-call sequencing. Safety metrics evaluate risk-related output behavior, not latency or tool execution.
General monitoring provides aggregate health, latency, success-rate, and dashboard views, but the question asks for per-run sequence inspection and timing breakdowns. Foundry agent tracing specifically supports debugging unexpected behavior and monitoring latency across requests. Reference topics: Microsoft Foundry observability, agent tracing, OpenTelemetry-based traces, tool invocations, LLM call inspection, and latency diagnostics.
NEW QUESTION # 60
......
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