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| Section | Objectives |
|---|---|
| Topic 1: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Topic 2: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Topic 3: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 4: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Topic 5: Develop Generative AI Applications and Agents | - AI agents architecture
|
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NEW QUESTION # 136
You are building a voice agent for a pharmacy. It must accept spoken questions, reply with synthesised speech, and accurately recognise specialised medicine names that a general speech model often mishears. Which capability should you configure?
Answer: B
Explanation:
A custom speech model improves recognition of specialised vocabulary, such as medicine names, by adapting speech-to-text (STT) to your domain, and AI-103 covers integrating speech, including custom speech models, as an agent modality. This directly addresses the accuracy problem in the scenario.
NEW QUESTION # 137
You are building a customer support web app named App1 in Microsoft Foundry that uses a GPT realtime model.
App1 must support:
- Live, low-latency voice conversations that use Azure OpenAI
- Streaming audio input from users and playback audio responses
You need to configure a connection method that supports real-time audio streaming in client application and targets approximately 100 ms latency.
Which connection method should you use?
Answer: D
Explanation:
WebSockets is the best connection method for this application because it enables full-duplex, bi- directional streaming over a single TCP connection, meeting the strict ~100 ms latency requirement for real-time audio.
Reference:
https://www.chat-data.com/blog/implement-openai-realtime-api-for-chatgpt-voice
NEW QUESTION # 138
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: A
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 # 139
You have a large collection of image files and PDF documents stored in an Azure Storage account. The documents contain tabular data.
You need to extract the tables into a structured format that can be imported into a database. The solution must minimize development effort. What should you use?
Answer: B
Explanation:
To extract tables from numerous images and PDF documents in Azure Storage into a structured database with low development, you should utilize Azure AI Document Intelligence (formerly Form Recognizer) within the Azure AI Foundry ecosystem. The Layout Model is best for extracting tables, rows, columns, and headers.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/tools/document-intelligence
NEW QUESTION # 140
You have a Microsoft Foundry project that uses Azure Content Understanding in Foundry Tools to analyze marketing videos.
Video segmentation is enabled.
You need to configure an analyzer to output a generated JSON field that describes the color scheme of each video segment.
How should you configure the analyzer? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Field method: generate
* Field value type: string
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The field should use the generate method because "describe the color scheme" asks Content Understanding to infer visual characteristics from each segment and generate a new semantic description. The desired value is not simply literal text that can be extracted from the media.
The field value type is string because a color-scheme description is textual-for example, " warm red and orange palette " or " predominantly cool blue and gray tones " .
Microsoft ' s Content Understanding video documentation explains that after segmentation, the generative- model layer can interpret scenes and populate developer-defined fields for every segment. Field schemas define a field ' s name, type, and description, and generated fields can capture semantic information derived from the source content. Microsoft Learn Therefore, the two values shown in the supplied answer image are reversed. The technically correct configuration is:
method = generate
type = string
This directly aligns with AI-103 ' s computer-vision objectives to implement visual understanding by configuring Azure Content Understanding to extract visual characteristics and process and interpret video segments . Microsoft Learn
NEW QUESTION # 141
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