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| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - Implement custom vision models - Analyze images and videos using Azure AI Vision - Extract information using OCR and form recognition |
| Implement Natural Language Processing Solutions | - Implement question answering and conversational AI - Analyze text using Azure AI Language - Translate and interpret language using Azure AI services |
| Plan and Manage an Azure AI Solution | - Select appropriate Azure AI services - Plan solution requirements and architecture - Manage security, compliance, and responsible AI |
| Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Enrich and index data for search scenarios - Implement document intelligence solutions |
| Implement Generative AI Solutions | - Integrate prompt engineering techniques - Build retrieval-augmented generation (RAG) solutions - Implement Azure OpenAI Service solutions |
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NEW QUESTION # 188
You need to analyze video content to identify any mentions of specific company names.
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
NEW QUESTION # 189
You are developing a text processing solution.
You have the function shown below.
Answer:
Explanation:
Explanation
NEW QUESTION # 190
You are developing a call to the Face API. The call must find similar faces from an existing list named employeefaces. The employeefaces list contains 60,000 images.
How should you complete the body of the HTTP request? 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:
You are calling the Face API to find similar faces from an existing list called employeefaces.
Key facts from the scenario:
* The list contains 60,000 images.
* In Face API:
* faceListId # limited to 1,000 faces.
* largeFaceListId # designed for very large lists, up to 1,000,000 faces.
Since employeefaces has 60,000 images, you must use LargeFaceListId, not faceListId.
* " faceId " : " 18c51a87-3a69-47a8-aedc-a54745f708a1 "
* This is the face to search for.
* " largeFaceListId " : " employeefaces "
* Must use LargeFaceListId because the list has 60,000 images.
* " maxNumOfCandidatesReturned " : 1
* Limits the number of matches returned.
* " mode " : " matchFace "
* Two options exist:
* " matchFace " # returns the most similar individual faces.
* " matchPerson " # groups faces by person.
* Since the requirement is find similar faces, use " matchFace " .
Parameters in the Request Body
Completed Answer {
" faceId " : " 18c51a87-3a69-47a8-aedc-a54745f708a1 " ,
" largeFaceListId " : " employeefaces " ,
" maxNumOfCandidatesReturned " : 1 ,
" mode " : " matchFace "
}
* LargeFaceListId # " employeefaces "
* matchFace # " mode "
Correct Values to Drag and Drop
Final Answer:
* First blank: LargeFaceListId
* Second blank: matchFace
* Face API - Find Similar
* Face API - Large Face List operations
Microsoft References
NEW QUESTION # 191
You have an Azure Cognitive Search solution and an enrichment pipeline that performs Sentiment Analysis on social media posts.
You need to define a knowledge store that will include the social media posts and the Sentiment Analysis results.
Which two fields should you include in the definition? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
In Azure Cognitive Search, a knowledge store is an optional capability that allows you to persist the enriched data from a skillset (enrichment pipeline) into an Azure Storage account. This is often used when you want to further analyze or visualize enriched content, e.g., sentiment analysis results from social media posts.
When defining a knowledge store, you need:
* storageConnectionString
* This is required so the enrichment pipeline can connect to your Azure Storage account.
* Without it, the knowledge store cannot persist any data.
* storageContainer
* This specifies the container in Blob Storage where enriched documents or projections will be stored.
* This allows you to persist the original documents and enriched outputs such as sentiment analysis scores.
Other options (not correct in this case):
* tables # This is a projection type used within a knowledge store to structure output in Table Storage, but it's not required in the definition itself.
* files # Another projection type for storing enriched documents as files in Blob Storage, but again, not a required field in the definition itself.
* objects # A projection type used for representing enriched documents as JSON objects in Blob Storage.
Thus, while tables, files, and objects are used inside projections within a knowledge store, the definition itself always requires a storage connection string and storage container.
The answer:
* A. storageContainer
* C. storageConnectionString
* Knowledge store in Azure Cognitive Search
* How to define a knowledge store
NEW QUESTION # 192
You need to build an app that will capture video meetings and perform the following actions:
- Generate subtitles in real time.
- Recognize speakers in the videos.
What should you use?
Answer: B
Explanation:
Azure AI Video Indexer is the ideal choice for analyzing video content, including the tasks of generating subtitles in real-time and recognizing speakers. Video Indexer uses AI to extract metadata, such as speech-to-text transcription, speaker identification, and other video analysis features. It is specifically designed to handle video files and process them comprehensively for such use cases.
NEW QUESTION # 193
......
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