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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
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NEW QUESTION # 141
You have a Microsoft Foundry project named Project1 that contains an agent. The agent uses an OpenAPI 3.0 specification to call an external weather service.
The weather service requires a key to be passed in an HTTP header. The key value is stored as a connection in Project1.
You need to ensure that the key value from the connection is included automatically whenever the OpenAPI tool is invoked.
What should you configure in the OpenAPI specification?
Answer: C
Explanation:
To ensure Microsoft Foundry automatically injects the API key from your project connection whenever the OpenAPI tool is invoked, your OpenAPI 3.0 specification must explicitly include a securitySchemes component mapping to the exact header name, and a global or operation-level security requirement referencing that scheme.The orchestrator matches the name field in the specification against the key stored inside your project's custom connection.
1. Required OpenAPI 3.0 Configuration
You must add both the components.securitySchemes block and the security block to your specification file:
openapi: 3.0.0
info:
title: External Weather Service
version: 1.0.0
paths:
/weather:
get:
operationId: getWeather
responses:
'200':
description: Successful weather retrieval
# 1. Define the security scheme in the components section
components:
*-> securitySchemes:
weatherApiKey: # Arbitrary logical identifier for this scheme
*-> type: apiKey
in: header
name: X-Weather-API-Key # MUST match the "key" name configured in your Foundry Connection
# 2. Apply the security requirement globally (or inside individual operations) security:
- weatherApiKey: [] # Instructs Foundry to enforce this scheme on the API requests in: header: Explicitly instructs the Foundry proxy layer to attach the credential value to the HTTP request headers (rather than as a query parameter).name: This string value is the exact HTTP header key (e.g., X-Weather-API-Key or Authorization). Crucially, this value must identically match the "Key" property given to the secret in your Microsoft Foundry Custom Connection.
security: Actively triggers the authentication workflow for the tool's endpoints. Without this block, Microsoft Foundry treats the API call as anonymous and strips out connection values.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/openapi
NEW QUESTION # 142
Hotspot Question
You need to create a new resource that will be used to perform sentiment analysis and optical character recognition (OCR). The solution must meet the following requirements:
- Use a single key and endpoint to access multiple services.
- Consolidate billing for future services that you might use.
- Support the use of Azure Vision in Foundry Tools in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: PUT
Need to create a Cognitive Services Multi-Service resource using the PUT method. This specific resource type provides a single endpoint and key for multiple AI services, consolidates billing, and supports Azure Vision.
Box 2: CognitiveServices
Using the CognitiveServices kind creates a multi-service resource. This fulfills all the requirements by providing a single key and endpoint for multiple services, consolidating billing, and enabling access to features like Azure AI Vision and Text Analytics under one roof.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/sdk-overview
NEW QUESTION # 143
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Semantic Kernel provides support for the following template formats:
semantic-kernel - Built-in Semantic Kernel format.
handlebars - Handlebars template format.
liquid - Liquid template format
The Semantic Kernel prompt template language is a simple way to define and compose AI functions using plain text. You can use it to create natural language prompts, generate responses, extract information, invoke other prompts or perform any other task that can be expressed with text.
Reference:
https://learn.microsoft.com/en-us/semantic-kernel/concepts/prompts/prompt-template-syntax
NEW QUESTION # 144
You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?
Answer: D
Explanation:
The correct capability is token usage . In Microsoft Foundry observability, token consumption is the primary signal for diagnosing model-cost changes when request volume is unchanged. Token usage lets you distinguish whether costs increased because prompts became larger, retrieved or tool-provided context expanded, responses became longer, or agent execution added more model calls. Microsoft Foundry monitoring dashboards track operational metrics such as token consumption, latency, error rates, and quality scores, and the agent monitoring dashboard is specifically intended to help analyze token usage, latency, success rates, and evaluation outcomes for production traffic.
This directly matches the scenario because the issue is not more traffic, but changed request or response characteristics. Input tokens reveal whether the prompt, chat history, grounding data, or tool outputs being sent to the model increased. Output tokens reveal whether the model is generating longer completions.
Expanded tool usage can also increase cost indirectly by adding more tool results, intermediate calls, and context into subsequent model requests; Foundry tracing and observability capture tool usage and token consumption for agent runs.
Evaluation metrics assess response quality and safety, not cost drivers. Latency identifies performance delays, and run success rate measures reliability. Reference topics: Microsoft Foundry observability, agent monitoring dashboard, token consumption, cost analysis, tool usage, and production monitoring.
NEW QUESTION # 145
You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?
Answer: A
Explanation:
The correct answer is B. Use real-time speech to text to process streaming audio input . The scenario requires live transcription from a continuous telephony stream, with transcript text appearing within a few seconds while the call is still in progress. Azure Speech in Foundry Tools real-time speech recognition is specifically intended for immediate transcription scenarios such as call center assistance, dictation, and live meeting captioning. Microsoft's Speech guidance describes real-time speech to text as processing audio input and returning transcriptions in real time, which matches the supervisor monitoring requirement.
Batch transcription is inappropriate because it processes stored audio files after recording, not an active live stream. Speech translation is used when the primary goal is translating speech into another language, not simply producing live same-language call transcripts. Text to speech performs the reverse operation by generating spoken audio from text and does not transcribe inbound calls. Real-time speech to text provides the low-latency streaming recognition path required for live operational monitoring. Reference topics: Azure Speech in Foundry Tools, real-time speech recognition, streaming audio input, call center transcription, and live captions.
NEW QUESTION # 146
......
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