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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 2: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 4: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 5: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
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NEW QUESTION # 134
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: C
Explanation:
You should use Real-Time Speech-to-Text (Streaming STT) for this project.
Why Real-Time STT is Required
Low Latency: It transcribes audio within seconds.
Continuous Input: It processes live, ongoing audio streams.
Live Monitoring: Supervisors need immediate text to flag issues.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/get-started-speech-to-text
NEW QUESTION # 135
You need to extract prebuilt entity categories including Person, Organization, Location, and DateTime from each email without training a model.
Answer: C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
TextAnalyticsClient.recognize_entities invokes Azure Language ' s pretrained Named Entity Recognition (NER) capability. NER identifies recognized concepts in unstructured text and assigns supported categories such as Person, Organization, Location, DateTime, Quantity, and related entity classes.
No training dataset or custom model is required, which directly satisfies the question.
recognize_pii_entities is designed for personally identifiable and sensitive information. Although there is some overlap in certain entity classes, its purpose is privacy detection and redaction rather than general NER.
NamedEntityRecognitionSkill is an Azure AI Search enrichment skill. It would be appropriate if the text were being processed as part of an AI Search indexing pipeline, but the question states that the application already uses TextAnalyticsClient, so adding Search would unnecessarily increase complexity.
Custom Entity Recognition is required when an organization needs domain-specific entities not adequately covered by pretrained categories. It requires labeling training data and training a custom Language model, directly contradicting the requirement to avoid training.
The correct method is therefore A, recognize_entities .
Study Guide alignment: Implement text analysis solutions # extract entities, topics, summaries, and structured information using Foundry Tools .
NEW QUESTION # 136
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 # 137
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 # 138
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: A
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 # 139
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