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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 3: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 4: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 5: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
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NEW QUESTION # 116
You are developing an app that will use the Azure Vision in foundry Tools API to analyze an image.
You need configure the request that will be used by the app to identify whether an image is explain or a line drawing.
How should you complete the request? To answer, select the appropriate options tn the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use an HTTP POST request to the Analyze Image endpoint and request the imageType visual feature.
The image-analysis operation receives image content or an image reference and evaluates the visual features specified in the request. imageType is the feature that supplies information indicating whether an image is clip art or a line drawing.
This is distinct from other feature values shown in the exhibit. description produces a textual image description. Objects performs object detection and returns detected objects and bounding boxes. tags produces semantic labels describing visual concepts. None of those provides the specific clip-art/line-drawing classification requested here.
The pretrained image-type capability eliminates the need to create and train a Custom Vision classifier to distinguish ordinary photographs from drawings. The application can simply examine the returned image-type properties and implement its classification policy.
The use of POST is consistent with submitting image data to the image-analysis endpoint.
The AI-103 Study Guide includes visual understanding workflows and selecting appropriate Foundry
/Vision capabilities for identifying visual characteristics. Microsoft Learn
NEW QUESTION # 117
You have a web app that uses Azure Al Search.
When reviewing activity, you see greater than expected search query volumes. You suspect that the query key is compromised.
You need to prevent unauthorized access to the search endpoint and ensure that users only have read only access to the documents collection. The solution must minimize app downtime.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Azure AI Search query keys provide read-only access to the documents collection for search, filter, and suggestion operations. Microsoft recommends using query keys rather than admin keys for client applications that require only document queries. Microsoft Learn To minimize downtime, first create a new query key while the current application continues functioning with the existing key.
Second, update the application configuration to use the newly created query key and deploy or refresh the application instances. At this point, legitimate traffic has moved away from the suspected compromised credential.
Finally, delete the compromised query key . Azure AI Search supports explicit query-key creation and deletion, so there is no need to expose an admin key to the application. Microsoft Learn Deleting the old key before updating the application would cause avoidable failures. Rotating the primary or secondary admin key would grant or manage a more privileged credential than the application requires and therefore violates least privilege.
The sequence simultaneously addresses credential compromise, read-only access, and service availability.
Study Guide alignment: Manage, monitor, and secure AI systems using least-privilege authentication and operational credential management . Microsoft Learn
NEW QUESTION # 118
Drag and Drop Question
You have a Microsoft Foundry project that contains a customer support agent grounded in internal documentation.
After a recent update, users report the following issues:
- Some answers are unsupported by retrieved documents.
- A small number of responses are flagged for policy violations.
You need to evaluate each issue.
Which observability signals should you use for each issue? To answer, drag the appropriate observability signals to the correct issues. Each observability signal may be used once, more than once, or not at all. You may need to drag the spit bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 119
You need to persist state by using a durable ID so that the agent automatically reuses the full interaction history across turns and sessions.
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use a conversation . A conversation is the durable runtime container that maintains the sequence of interactions associated with a particular case or user flow.
The application can persist the conversation ID together with the support-case ID. When the same case resumes later-even in another application session-the application reuses that conversation ID. The runtime can then continue with the prior interaction history rather than requiring the application to reconstruct it manually.
The stored history can include user messages, assistant responses, tool-call requests, and tool outputs, which is precisely what the question requires.
An agent defines reusable behavior such as the model, instructions, and tools. Many independent conversations can use the same agent, so the agent ID is not the state container for an individual support case.
A response represents an individual model/run result, while an output item is one item produced during response processing. Neither represents the complete durable interaction history.
This separation between durable conversational state and reusable agent configuration is central to Foundry ' s agent runtime model.
Study Guide alignment: define conversation-tracking approaches and build agents that integrate retrieval, function calling, and conversation memory . Microsoft Learn
NEW QUESTION # 120
You need to measure the public perception of your brand on social media by using natural language processing. Which Azure service should you use?
Answer: C
Explanation:
Azure Language in Foundry Tools provides sentiment analysis and opinion mining , which are the appropriate natural language processing capabilities for evaluating public perception. Sentiment analysis processes unstructured social-media text and assigns classifications such as positive, neutral, or negative, together with confidence scores. Opinion mining extends this analysis by associating expressed sentiment with particular aspects, attributes, products, or services mentioned in the text. This allows the application to determine not only the overall attitude toward the brand but also which specific brand characteristics are receiving favorable or unfavorable reactions.
Azure Document Intelligence is intended primarily for extracting text, fields, tables, and structures from documents. Content Safety detects potentially harmful material rather than measuring consumer opinion.
Azure Vision processes visual content and is not the primary service for sentiment classification of written social-media posts.
Azure Language can be accessed through REST APIs, client libraries, or supported containers. The application submits the social-media text and receives document-level and sentence-level sentiment results that can be aggregated into brand-perception metrics, dashboards, or alerts.
Study Guide alignment: Implement text analysis solutions - perform sentiment analysis and opinion mining, process unstructured text, and interpret confidence scores and sentiment classifications.
NEW QUESTION # 121
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