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| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Topic 2: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 3: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 4: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Topic 5: Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
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NEW QUESTION # 10
You have a Microsoft Foundry project named Project1 that contains the following:
- An OpenAPI tool that calls an external API
- A project connection named Connection1 that stores the API key of the external API When an agent calls the OpenAPI tool, the API returns a 401 unauthorized error, and traces show that the API key header is NOT being sent.
You need to ensure that the OpenAPI tool automatically includes the API key from Connection1 on all requests.
What should you do?
Answer: A
Explanation:
To make sure that the OpenAPI tool automatically includes the API key from the connection on all requests, you should explicitly define the security schemes and security requirements in your OpenAPI specification file.
Incorrect:
[Not D]
Even if you successfully link the tool to the custom connection in the portal UI, the Microsoft Foundry agent service will intentionally redact or withhold the API key if it cannot find a corresponding matching structure in the tool's code definition.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/openapi
NEW QUESTION # 11
You have an Azure subscription that contains 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 # 12
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
* Authenticates by using a Microsoft Entra managed identity
* Sends prompts to a deployed model by using the Azure OpenAI Responses API How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
credential = DefaultAzureCredential
response = openai_client.responses.create
The correct authentication class is DefaultAzureCredential because the requirement is Microsoft Entra-based authentication, and this credential supports managed identity in hosted Azure environments. The Microsoft Foundry quickstart for Python shows the project client being created with AIProjectClient (endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential()), which enables the Foundry SDK to authenticate without using API keys or embedded secrets. The same guidance shows creating an OpenAI- compatible client from the project by calling project.get_openai_client().
The correct Responses API method is create because the application must send a new prompt to the deployed model and receive generated output. Microsoft's Foundry quickstart demonstrates the exact pattern: response
= openai.responses.create(model= " gpt-5-mini " , input= " ... " ), followed by reading response.output_text.
The retrieve operation is used to fetch an existing response, not submit a new inference request, and compact is not the correct method for generating a model response. AzureKeyCredential would violate the Microsoft Entra managed identity requirement, while ClientSecretCredential uses an application secret rather than managed identity. Reference topics: Microsoft Foundry SDK, AIProjectClient, Microsoft Entra authentication, DefaultAzureCredential, and Azure OpenAI Responses API.
NEW QUESTION # 13
You have an agent named Agent1 that uses Model Context Protocol (MCP) calls to retrieve external data.
You need to implement guardrails to ensure that Agent1 cannot send any content tagged as Violence to the MCP server. Which intervention point should you use?
Answer: A
Explanation:
The correct intervention point is tool call because the prohibited content must be inspected before Agent1 transmits the MCP request to the external server. Microsoft Foundry defines a tool call as "the action and data the agent proposes to send to a tool." This includes the selected MCP operation and its arguments or payload.
A guardrail configured at this stage evaluates the outbound content immediately before execution. When the Violence risk is detected and the response action is configured as Annotate and block , the tool invocation is not executed, preventing the restricted data from leaving the agent boundary.
The other intervention points operate at different stages. User input scans the original user prompt, but harmful content could subsequently be generated within the tool arguments. Tool response scans information returned by the MCP server, which occurs after the outbound content has already been transmitted. Output scans the final answer delivered to the user and therefore does not control agent-to-tool communication.
This aligns with the AI-103 Study Guide topics Configure safety filters, guardrails, risk detection, and content moderation and Govern agent behavior with oversight modes, constraints, and tool-access controls under responsible AI and agentic systems.
NEW QUESTION # 14
You have an application that uses a prebuilt Azure Language model.
You need to generate a summary that meets the following requirements:
- Identify each presenter in the video and attribute each text file to
sentences
- Preserves the original sentence order
- Returns exactly three sentences
Which Language service feature should you use?
Answer: B
Explanation:
The Azure AI Language service's extractive summarization feature is the appropriate choice to summarize video transcripts while preserving original sentence structure and order. Using the REST API or client library, you can set sentenceCount to exactly three to generate a concise summary from key phrases.
This approach avoids generating new, abstract text, fulfilling the constraint to keep original text, unlike abstractive summarization.
Key Implementation Details:
Service: Azure AI Language, specifically the Summarization feature.
Method: Extractive summarization selects the most relevant sentences directly from the source text, which is ideal for maintaining the original context, speaker attribution, and sentence structure.
Configuration: The API call allows setting the sentenceCount parameter to three, ensuring the output is limited to that exact number.
Workflow: This model works best on conversational transcripts processed via speech-to-text (e.g., Azure AI Speech), where speakers are identified, allowing the summarization tool to aggregate sentences while preserving the speaker-to-text relationship.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/text-summarization
NEW QUESTION # 15
......
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