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| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 2: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Topic 3: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 4: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Topic 5: Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
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NEW QUESTION # 60
You have a Microsoft Foundry project. You need to deploy a model from the model catalog to support real- time inference. The solution must meet the following requirements:
* Use key-based authentication
* Support real-time REST API access
* Not consume the vCPU quota of the virtual machines in the Azure subscription Which type of deployment should you use?
Answer: D
Explanation:
A serverless API deployment exposes a model from the Microsoft Foundry model catalog as a managed inference endpoint without hosting the model on virtual-machine compute in the customer's subscription.
Consequently, it does not consume the subscription's VM-family vCPU quota. Capacity is managed by the service and controlled through deployment-level token and request rate limits rather than customer-managed compute instances.
The generated serverless endpoint uses key authentication. Foundry provides a target URI and associated primary or secondary credentials that the application uses to authorize inference requests. Serverless deployments support the Azure AI Model Inference API, enabling real-time application access through a consistent API interface suitable for REST-based prediction requests.
A self-hosted container requires customer-managed infrastructure and therefore consumes the compute capacity on which it runs. A compute-backed standard deployment generally requires provisioned hosting resources and applicable quota. A batch deployment is designed for asynchronous, high-volume processing rather than interactive real-time inference.
The serverless option therefore satisfies all three requirements simultaneously: key-based authorization, online API inference, and no dependency on the subscription's virtual-machine vCPU quota.
Study Guide alignment: Planning and managing Azure AI solutions - select model deployment options, deploy catalog models, configure endpoint authentication, and evaluate compute and quota requirements.
NEW QUESTION # 61
You have a Microsoft Foundry project that contains an agent.
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page- number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?
Answer: C
Explanation:
The correct configuration is advanced data parsing because the issue is not merely OCR; the ingestion job must preserve document structure for reliable RAG retrieval. Microsoft guidance for advanced parsing states that it automatically detects tables across all pages, including tables in scanned documents, merges tables that span multiple pages, restores column headers, and creates table chunks with metadata such as table index, shape, page numbers, section headings, and table previews. This directly satisfies the requirement to retrieve specific rows from multipage tables while retaining source-page context.
Basic parsing with fixed-size chunking would flatten the document into arbitrary text fragments, which is the current failure mode. OCR with page-level chunking improves text extraction from scanned PDFs, but it does not provide structure-aware chunks that preserve headings and table relationships across pages. Storing each page as a single chunk is too coarse for row-level retrieval and can bury relevant table rows in excessive context. Advanced data parsing is purpose-built for RAG ingestion because it produces semantically meaningful, retrievable chunks and enriches them with metadata needed for citations and grounding.
Reference topics: RAG ingestion, advanced parsing, OCR, table extraction, structure-aware chunking, page metadata, and Azure Blob Storage document ingestion.
NEW QUESTION # 62
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 # 63
You are building a voice agent for a pharmacy. It must accept spoken questions, reply with synthesised speech, and accurately recognise specialised medicine names that a general speech model often mishears. Which capability should you configure?
Answer: D
Explanation:
A custom speech model improves recognition of specialised vocabulary, such as medicine names, by adapting speech-to-text (STT) to your domain, and AI-103 covers integrating speech, including custom speech models, as an agent modality. This directly addresses the accuracy problem in the scenario.
NEW QUESTION # 64
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Prompt shields action: Set action to block.
Additional mitigation: Enable Spotlighting.
The correct configuration is to set the Prompt Shields document attack action to block and enable Spotlighting . Prompt Shields in Microsoft Foundry are designed to detect attempts to manipulate model behavior through adversarial input. Microsoft distinguishes document attacks from direct user prompt attacks:
document attacks are malicious instructions embedded in third-party content such as documents, webpages, emails, or other externally supplied material. In this scenario, the embedded text inside uploaded screenshots is third-party content and can attempt to override the agent's instructions. Setting the action to block prevents detected document-attack content from being processed normally, which is required because the goal is prevention rather than passive logging or annotation.
Spotlighting is the additional mitigation because it marks or transforms document content so the model treats it as lower trust than system and user instructions. Microsoft's Foundry guidance describes Spotlighting as a Prompt Shields subfeature that helps protect against indirect or embedded document attacks by tagging input documents with special formatting to indicate lower trust. A custom blocklist is insufficient for unknown attacks, and OCR alone only extracts the malicious text; it does not mitigate prompt injection. Reference topics: Prompt Shields, document attacks, guardrails, Spotlighting, multimodal safety, and prompt injection defense.
NEW QUESTION # 65
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