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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| 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 # 32
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 # 33
You are building an agent that will retrieve the current time at a given location by using a custom API. You need to test the functionality of the custom API.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Endpoint selection: assistants
* Function declaration property: tools=[
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The command is creating/configuring an assistant that has access to a custom function named get_current_time, so the REST resource shown in the exhibit is assistants .
For the function definition, current Azure OpenAI/Foundry function-calling syntax uses the tools collection.
A function tool is represented by an item with " type " : " function " and a nested " function " object containing its name, description, JSON parameter schema, and required arguments.
Microsoft explicitly documents that the older functions and function_call parameters have been deprecated and that tools and tool_choice should be used instead. Microsoft Learn Therefore, although the embedded exhibit ' s answer image shows functions=[, the currently verified Microsoft syntax is tools=[ .
content represents message content rather than an agent tool definition. tool_resources is used for resources required by certain hosted tools and is not where a custom function schema is declared.
The AI-103 Study Guide requires candidates to define tool schemas and integrate agent tools, including APIs and custom functions . Microsoft Learn
NEW QUESTION # 34
You have a Microsoft Foundry agent that grounds responses from an Azure AI Search index containing:
* Searchable text fields for product names and product codes.
* A vector field containing embeddings for product descriptions.
You need users to query by exact product names or codes and by natural-language product descriptions.
Answer: D
Explanation:
Configure hybrid search , which executes full-text and vector queries within the same Azure AI Search request. The full-text component searches the product-name and product-code fields through the lexical index, providing the precision required for exact or near-exact identifiers. Microsoft specifically identifies product codes and other specialized terms as scenarios that frequently perform better with keyword search.
The vector component compares the embedding of the user's natural-language query with the embeddings stored for product descriptions. This retrieves semantically similar products even when the query and indexed description do not share the same literal words. Azure AI Search runs the full-text and vector searches in parallel and combines their result sets by using Reciprocal Rank Fusion, returning one unified ranking to the Foundry agent.
Keyword-only search would preserve exact matching but perform poorly for conceptual or paraphrased descriptions. Vector-only search supports semantic similarity but can miss precise product codes and rare identifiers. Semantic search alone reranks text-search results using language understanding; it does not replace the vector query required to use the existing embedding field.
Study Guide alignment: configure semantic, hybrid, and vector search for grounding, choose an appropriate retrieval method, and connect retrieval pipelines to agent tools .
NEW QUESTION # 35
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 # 36
s a Microsoft Foundry resource. You need to build an app that will suggest product names from a given product description. Which Foundry model should you use?
Answer: C
Explanation:
Use GPT-4 because suggesting product names from a textual description is a natural-language generation task. The application can supply the product's characteristics, intended audience, tone, and naming constraints in a prompt, and GPT-4 can generate candidate names that reflect those requirements. GPT models interpret natural-language instructions and generate corresponding textual completions, making them appropriate for ideation, summarization, content drafting, and conversational applications.
The other model categories serve different modalities. DALL-E is an image-generation model that creates visual content from natural-language prompts; it is not intended primarily to produce product-name lists.
Whisper is a speech-recognition model used to transcribe audio into text. Embedding models convert text into numerical vectors for similarity search, clustering, classification, and retrieval scenarios; they do not generate human-readable product names. Microsoft's Foundry model documentation distinguishes chat and generative models from image-generation, audio, and embedding model families.
The app should therefore deploy GPT-4, construct a prompt containing the product description and naming criteria, and consume the generated textual response through the model inference endpoint.
Study Guide alignment: Select and deploy appropriate generative models, integrate deployed models into applications, and implement prompt-driven content-generation workflows.
NEW QUESTION # 37
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