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| Section | Weight | Objectives |
|---|---|---|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
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NEW QUESTION # 11
You are creating an enrichment pipeline that will use Azure Al Search. The knowledge store contains unstructured JSON data and the text from scanned PDF documents.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use an object projection for the unstructured JSON data. Object projections store a JSON representation of an enrichment tree node in an Azure Blob Storage container. They preserve hierarchical fields and complex structures, making them appropriate when enriched document content must remain available as a complete JSON object rather than being decomposed into relational rows. Microsoft describes object projections as JSON representations that can be sourced from nodes in the enrichment tree.
For the scanned PDF content, use a file projection . During document cracking and OCR processing, scanned pages are represented through the /document/normalized_images/* collection. File projections write these binary normalized images to Blob Storage, preserving the page assets from which the text is extracted.
Microsoft specifies that file projections operate only on normalized images and contain binary data rather than JSON.
A table projection is intended for row-and-column structures used by analytical tools such as Power BI. It is not the appropriate choice for preserving hierarchical JSON or scanned-document image files.
Study Guide alignment: configure Azure AI Search enrichment pipelines, skillsets, knowledge stores, and table, object, and file projections .
NEW QUESTION # 12
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:
Explanation:
" tool_choice " : " required "
The correct completion is " tool_choice " : " required " . In Microsoft Foundry Agent Service, tool_choice controls whether the model can answer directly or must invoke a tool during a run. The official Foundry tool guidance states that tool_choice provides deterministic control over tool calling: auto allows the model to decide whether to call tools, none prevents tool use, and required forces the model to call one or more tools.
This directly addresses the issue where the ticket-triage agent sometimes responds without invoking a required tool.
The completed payload should therefore add the tool_choice property beside assistant_id, with the value " required " . The value " auto " is incorrect because it preserves the current nondeterministic behavior. The values " tools " and " type " do not force execution-time tool invocation in this payload; they are used for tool definitions or typed objects in other contexts. response_format controls output formatting, not tool execution.
Reference topics: Microsoft Foundry Agent Service, tool calling reliability, run payload configuration, tool_choice, agent execution, and deterministic tool invocation.
NEW QUESTION # 13
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
Answer: D
Explanation:
To enhance response completeness in your Microsoft Foundry agent, you must intercept the retrieved documents and the generated summary within your backend application logic before returning the payload to the user.
1. Implement Completeness Verification Logic
Add a verification step in your orchestration code (e.g., in your Python/Semantic Kernel or LangChain pipeline) that compares the generated summary against the retrieved chunks.' Map Key Assertions: Extract main policy rules from retrieved text.Cross-Reference Entities: Verify all key entities are in the summary.
Check Scope Coverage: Ensure every retrieved document is represented.
Scan for Gaps: Identify critical missing constraints or exceptions.
2. Apply Application-Level Mitigation Strategies
If the verification step detects that the summary is incomplete, use your code to correct it before the final response leaves your system.
Reference:
https://dev.to/moonrunnerkc/how-i-built-a-verification-layer-for-copilot-clis-multi-agent-output-4b7h
NEW QUESTION # 14
You are developing an app that will use the Azure Vision in foundry Tools API to analyze an image.
You need configure the request that will be used by the app to identify whether an image is explain or a line drawing.
How should you complete the request? To answer, select the appropriate options tn the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use an HTTP POST request to the Analyze Image endpoint and request the imageType visual feature.
The image-analysis operation receives image content or an image reference and evaluates the visual features specified in the request. imageType is the feature that supplies information indicating whether an image is clip art or a line drawing.
This is distinct from other feature values shown in the exhibit. description produces a textual image description. Objects performs object detection and returns detected objects and bounding boxes. tags produces semantic labels describing visual concepts. None of those provides the specific clip-art/line-drawing classification requested here.
The pretrained image-type capability eliminates the need to create and train a Custom Vision classifier to distinguish ordinary photographs from drawings. The application can simply examine the returned image-type properties and implement its classification policy.
The use of POST is consistent with submitting image data to the image-analysis endpoint.
The AI-103 Study Guide includes visual understanding workflows and selecting appropriate Foundry
/Vision capabilities for identifying visual characteristics. Microsoft Learn
NEW QUESTION # 15
You have an Azure subscription that contains an Azure App Service app named App1.
You provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
Answer: B
Explanation:
To configure the App Service app to access the Microsoft Foundry Service (part of Azure AI Services) with low administration, you need to configure the app with the endpoint URI and a subscription key.
These credentials can be found in the Azure Portal under the Keys & Endpoint section of your provisioned Foundry resource, allowing the app to seamlessly authenticate API calls.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/authentication
NEW QUESTION # 16
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