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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 2: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 3: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Topic 4: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 5: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
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質問 # 10
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
- Ensure that background objects can be removed by applying a mask-
based inpainting edit.
- Preserve the original lighting and style of the edited images.
- Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
正解：C
解説：
You should enable mask_inpainting and supply both the input image and the mask to meet all the criteria for your Microsoft Foundry image-editing workflow.
By utilizing Microsoft Foundry's built-in Image Generation Tool parameters, configuring the workflow this way ensures the desired edits are perfectly executed.
Workflow Configuration Requirements
Mask-Based Object Removal: Passing the mask parameter explicitly flags the exact background object regions targeted for removal, replacing them seamlessly.
Preserving Style and Lighting: Enabling mask_inpainting prompts the underlying built-in model (such as gpt-image-2) to inherit and maintain the exact lighting, textures, and style of the surrounding unmasked environment.
Built-In Controls Only: This is entirely handled natively via the Foundry Agent Service API variables without deploying a single line of custom code or third-party model checkpoints.
Targeted Area Enforcement: The system relies on the provided mask array to ensure pixels outside the marked coordinates are kept completely untouched and protected from VAE degradation.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/image-generation
質問 # 11
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?
正解：D
解説：
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
質問 # 12
An application must translate a high volume of standard product descriptions into 20 languages quickly, consistently, and at the lowest cost. The text is straightforward, with no domain-specific nuance. Which approach best fits?
正解：B
解説：
Azure Translator in Foundry Tools provides deterministic, broad-language translation at scale and at lower cost, which suits high-volume, straightforward content.
質問 # 13
Hotspot Question
You are building a model to detect objects in images.
The performance of the model based on training data is shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: 0
The percentage of false positives is 0%.
Because the model made zero incorrect positive predictions, the count and percentage of false positives must be exactly zero.
Box 2: 25
The value for the number of true positives divided by the total number of true positives and false negatives is 25% (or 0.25).
High Precision (100%): Every single object your model detects is correct; it generates zero false alarms.
Low Recall (25%): Your model misses 75% of the actual objects it was supposed to find.
'
Reference:
https://medium.com/grabngoinfo/how-to-evaluate-the-performance-of-a-binary-classification-model-6e7193dcbbf9
質問 # 14
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Does this meet the goal?
正解：A
解説：
The solution does not meet the goal. A completeness evaluation flow is useful for detecting incomplete responses, but detection and blocking do not improve the response itself. Microsoft Foundry RAG evaluators define Response Completeness as a metric that measures whether a response covers all critical information from the expected response or ground truth. It is a system evaluation signal used to assess response quality and produce pass/fail or scored results.
In this scenario, the issue is that the agent omits required regulatory clauses even though the clauses are present in retrieved content. Blocking low-scoring responses would prevent incomplete answers from being returned, but it would not revise the summary, add the missing clauses, or improve the generation process.
The appropriate improvement is to add a response-generation control such as a reflection or verification pass that checks the draft summary against the retrieved policy content and regenerates or amends the answer before returning it. Evaluation can support the quality gate, but by itself it is an assessment mechanism, not a completeness-enhancement mechanism. Reference topics: Microsoft Foundry RAG evaluators, response completeness, grounded generation, reflection, and response quality optimization.
質問 # 15
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