COMPREHENSIVE AND UP-TO-DATE MICROSOFT AI-102 PRACTICE EXAM QUESTIONS

Comprehensive and Up-to-Date Microsoft AI-102 Practice Exam Questions

Comprehensive and Up-to-Date Microsoft AI-102 Practice Exam Questions

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Microsoft AI-102 certification exam is a challenging exam that requires a lot of preparation and hands-on experience with Azure services. Candidates who pass AI-102 exam demonstrate their skills in designing and implementing AI solutions using Azure services, which is highly valued in the IT industry. Designing and Implementing a Microsoft Azure AI Solution certification is an excellent way for professionals to demonstrate their expertise in AI and advance their career in this field.

Microsoft AI-102 exam is designed for individuals who want to demonstrate their expertise in designing and implementing AI solutions on the Microsoft Azure platform. AI-102 exam is suitable for professionals who have experience in developing and deploying AI solutions and want to validate their skills through a recognized certification. AI-102 Exam covers topics such as developing AI solutions using Azure Cognitive Services, implementing computer vision solutions, natural language processing, and speech-to-text and text-to-speech conversion.

Microsoft AI-102: Designing and Implementing a Microsoft Azure AI Solution Exam is a certification exam designed to test your knowledge of designing and implementing AI solutions using Microsoft Azure technologies. AI-102 exam is intended for the individuals who have a strong understanding of Microsoft Azure AI services and want to demonstrate their expertise in designing and implementing AI solutions.

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Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q179-Q184):

NEW QUESTION # 179
You are creating an enrichment pipeline that will use Azure Cognitive Search. The knowledge store contains unstructured JSON data and scanned PDF documents that contain text.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Object projection
Object projections are JSON representations of the enrichment tree that can be sourced from any node.
Box 2: File projection
File projections are similar to object projections and only act on the normalized_images collection.
Reference:
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview


NEW QUESTION # 180
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 181
For each of the following statements, select Yes if the statement is tine. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 182
You are developing the knowledgebase.
You use Azure Video Analyzer for Media (previously Video indexer) to obtain transcripts of webinars.
You need to ensure that the solution meets the knowledgebase requirements.
What should you do?

  • A. Enable multi-language detection for videos
  • B. Configure audio indexing for videos only
  • C. Build a custom Person model for webinar presenters
  • D. Create a custom language model

Answer: B

Explanation:
Can search content in different formats, including video
Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.
Keywords extraction: Extracts keywords from speech and visual text.
Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).
Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.
Artifacts: Extracts rich set of "next level of details" artifacts for each of the models.
Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.
Reference:
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overview


NEW QUESTION # 183
You are building a Language Understanding model for purchasing tickets.
You have the following utterance for an intent named PurchaseAndSendTickets.
Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to [[email protected]] You need to select the entity types. The solution must use built-in entity types to minimize training data whenever possible.
Which entity type should you use for each label? To answer, drag the appropriate entity types to the correct labels. Each entity type 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.

Answer:

Explanation:

Explanation
Graphical user interface, application Description automatically generated

Box 1: GeographyV2
The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.
Box 2: Email
Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance.
Because this entity is already trained, you do not need to add example utterances containing email to the application intents.
Box 3: Machine learned
The machine-learning entity is the preferred entity for building LUIS applications.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity


NEW QUESTION # 184
......

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