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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
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NEW QUESTION # 92
You plan to build an app that will generate a list of tags for uploaded images. The app must:
* Generate tags in a user ' s preferred language.
* Support English, French, and Spanish.
* Minimize development effort.
Which Azure service endpoint should you use?
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Azure Vision Image Analysis provides a pretrained image-tagging capability that generates descriptive tags for visual content. The service can identify concepts such as objects, scenes, activities, and other image characteristics and return confidence values for the generated tags.
Because this is a pretrained capability, no custom training dataset, labeling process, training iteration, or prediction-resource deployment is required. That directly satisfies the requirement to minimize development effort.
Image Analysis also supports localized output for supported features and languages, allowing an application to request results appropriate for users who prefer English, French, or Spanish. The application therefore avoids implementing a separate tag-generation model followed by an additional translation workflow.
Azure Custom Vision image classification is intended for business-specific categories and would require labeled training images. Content Safety classifies harmful or inappropriate content rather than producing general-purpose descriptive image tags. Custom Translator can translate text but cannot inspect an image and determine which tags describe it.
The AI-103 Study Guide includes multimodal image analysis and extracting visual characteristics as part of the computer-vision skill domain. Microsoft Learn
NEW QUESTION # 93
A team wants to deploy a model from the Foundry catalogue with the fastest possible setup, pay only for the tokens they consume, and avoid provisioning any virtual machine (VM) quota. Which deployment option fits?
Answer: A
Explanation:
Serverless API deployment runs on Microsoft-managed infrastructure, bills per input and output token, and needs no VM quota, so it is the fastest path that matches token-based billing. It is the standard choice when you want pay-per-use economics without managing compute.
NEW QUESTION # 94
You need a connection method for a GPT realtime model that supports live client-side audio streaming and approximately 100-ms latency.
Answer: A
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use WebRTC . Microsoft ' s current Realtime API documentation explicitly recommends WebRTC for low- latency real-time audio streaming and lists it as the preferred connection type for client-side applications such as web and mobile apps. The documented latency is approximately 100 ms . Microsoft Learn WebRTC is designed specifically for interactive media and provides capabilities appropriate for continuous microphone input and audio playback, including low-latency media transport and handling of real-time network conditions.
Microsoft distinguishes this from WebSocket , which is positioned primarily for server-to-server/backend scenarios and has a higher typical latency of approximately 200 ms in the current guidance. SIP is appropriate when integrating telephony infrastructure such as call centers or IVR systems. RTMP is not the supported GPT Realtime interactive client protocol for this scenario. Microsoft Learn Therefore, the answer shown in the supplied file ( A, WebSocket ) conflicts with current Microsoft Learn documentation. For a browser/client application targeting approximately 100-ms audio latency, the verified answer is C, WebRTC .
Study Guide alignment: build multimodal/realtime generative applications and integrate speech as an agent modality . Microsoft Learn
NEW QUESTION # 95
You have a Python application that sends customer support transcripts to Azure Language in Foundry Tools by using the REST API. The transcripts are in English and can contain customer names, email addresses, and phone numbers.
You need to store a redacted transcript of each conversation. The support workflow must redact only customer names and leave other entity types unchanged.
How should you complete the analyze-text request body? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The operation must use PII Entity Recognition because the required output is a redacted version of the input based on personally identifiable information.
The " kind " property should therefore be set to " PiiEntityRecognition " . The piiCategories parameter restricts which sensitive categories participate in the analysis. Setting it to only [ " Person " ] ensures that recognized customer names are selected while other PII categories-such as PhoneNumber and email-related categories-are not included in the requested PII category set.
This distinction is important because the service supports numerous PII entity categories. Without category restriction, additional sensitive information could be recognized and redacted, which would violate the requirement to leave email addresses and phone numbers unchanged.
EntityRecognition is general Named Entity Recognition and is designed to identify broad entities such as Person, Organization, Location, and DateTime; it is not the specialized redaction operation required here.
LanguageDetection only identifies language.
The resulting PII response can provide redacted text and entity metadata that the application stores or audits.
Study Guide alignment: configure detection of sensitive content and implement solutions that extract entities and structured information from text . Microsoft Learn
NEW QUESTION # 96
You need to improve classification accuracy by showing the model how correct classifications look, without retraining the model or storing knowledge permanently.
Answer: C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Few-shot learning , more precisely few-shot prompting, supplies a small number of representative input
/output examples as part of the prompt. Those examples demonstrate the intended classification behavior before the model receives the new support ticket.
For example, the prompt might show one ticket classified as Billing, one as AccountAccess, and one as TechnicalIssue. The model can infer the expected labels, style, and output structure from the demonstrations and then apply the pattern to the incoming ticket.
The model ' s weights are not updated. The examples exist only in the prompt context for that invocation or conversation, so the approach satisfies both requirements: no retraining and no permanent knowledge storage.
Zero-shot prompting would supply only task instructions and no demonstrations. RAG retrieves external source information to ground model responses; it is not primarily a mechanism for showing examples of desired classification outputs. Chain-of-thought relates to reasoning behavior rather than labeled demonstration examples.
Few-shot prompting is therefore the direct method for increasing task accuracy by showing the model what correct results look like.
Study Guide alignment: Optimize generative AI systems through prompt engineering and adjust model behavior without retraining . Microsoft Learn
NEW QUESTION # 97
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