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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 2: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 3: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 4: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 5: Implement computer vision solutions | 10-15% | - Analyze visual content
|
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NEW QUESTION # 100
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
Answer: A
Explanation:
The first step should be an Azure Content Understanding analyzer within Microsoft Foundry Tools.
This specific tool is designed to handle the complexities in this scenario--scanned text, complex tables, and multi-column layouts--by converting unstructured documents into a structured Markdown format. This preserves the document's original hierarchy and structural relationships, which is critical for accurate downstream reasoning by an agent.
Incorrect:
[Not A]
Azure Language is designed for text-based natural language processing (NLP) like sentiment analysis or PII redaction on raw text strings. It cannot parse scanned images, optical character recognition (OCR), tables, or complex multi-column visual layouts.
[Not C]
The first step in this specific Microsoft Foundry agent configuration should not be an Azure OpenAI Responses API call.While multimodal models can read images, they frequently hallucinate data, fail to capture complex multi-column reading orders, and degrade table structures when processing dense, mixed-format documents.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/elements
NEW QUESTION # 101
You have a Microsoft Foundry project that contains an agent. The agent uses two tools to perform the following actions:
* Use Azure AI Search to retrieve answers from a private product documentation index.
* Use the web search tool to retrieve public information on the internet.
You need to ensure that for a specific run, the agent deterministically retrieves information only from the internet. To what should you set tool_choice?
Answer: A
Explanation:
Set tool_choice to the named Grounding with Bing Search tool:
{ " type " : " bing_grounding " }
A named tool choice directs the model to invoke a specific tool rather than allowing it to select from all tools attached to the agent. Microsoft's SDK specification describes AgentsNamedToolChoice as the mechanism used to force the model to call a particular tool and lists bing_grounding and azure_ai_search as supported named tool types.
Grounding with Bing Search retrieves current public information from the internet and supplies the results to the model for response generation. Selecting bing_grounding therefore directs the run to the public web source rather than the private Azure AI Search index.
Setting tool_choice to " required " only establishes that a tool must be invoked; when multiple tools are available, it does not identify which one must be selected. " auto " delegates the decision entirely to the model. Selecting { " type " : " azure_ai_search " } would force retrieval from the private product- documentation index, which is the opposite of the requirement.
Study Guide alignment: configure agent tools, control tool selection, integrate web grounding, and connect agents to enterprise knowledge sources .
NEW QUESTION # 102
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search as the retriever.
You plan to ingest PDF into an Azure AI Search index to ensure that the agent can ground responses in texts in both documents and embedded images.
Users require citations that link to the source files.
You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill.
Which indexing approach should you use?
Answer: C
Explanation:
The best configuration step an indexer to extract image data into a normalized_images collection.
Cracking Foundation: In Azure AI Search, document cracking automatically separates textual data from visual content. To make embedded images available to image-processing skills like OCR, the indexer must be explicitly configured with an imageAction configuration property (such as generateNormalizedImages or generateNormalizedImagePerPage).
Input Structure for OCR: This indexer step extracts embedded images from the PDF and restructures them into an internal normalized_images array (accessed via the path
/document/normalized_images/*). The built-in OCR skill strictly requires this normalized image collection format as its input payload.
Incorrect:
[Not C]
Content Field Limitations: The /document/content field generated during document cracking holds only the raw text extracted from the file. It does not contain the binary data or structural properties of embedded images.
Targeting Errors: Pointing an OCR skill directly at the raw text content field would fail to analyze the actual images, leaving the agent unable to ground answers in visual elements like diagrams or embedded infographics.
Reference:
https://docs.azure.cn/en-us/search/tutorial-skillset
NEW QUESTION # 103
A copilot must answer multi-part questions that depend on earlier turns in the conversation. You want the system to break each complex question into focused subqueries, run them in parallel, and semantically rerank the results before composing an answer. Which Azure AI Search capability provides this?
Answer: D
Explanation:
Agentic retrieval in Azure AI Search uses a large language model to decompose a complex query into focused subqueries, runs them in parallel, semantically reranks each set of results, and merges them into a unified response, while taking conversation history into account. That is precisely the behaviour the scenario describes.
NEW QUESTION # 104
You have a Microsoft Foundry project that contains a support-ticket triage agent built by using the Foundry Agent Service.
The agent uses tool to classify the ticket type and sot the ticket priority.
Sometimes, the same support case continues across multiple sessions over several days.
You need to persist state by using a durable ID to ensure that the agent can automatically reuse the full interaction history. The solution must preserve previous user messages, tool calls and tool outputs across turns and sessions.
Which runtime component should you use?
Answer: A
Explanation:
To achieve state persistence and ensure that the agent automatically reuses the full multi-session interaction history (including previous user messages, tool calls, and tool outputs), you must include a conversation component.
References:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/runtime-components
NEW QUESTION # 105
......
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