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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Generative AI Solutions | 10-15% | - Use Azure OpenAI Service for generative AI - Integrate Azure OpenAI models into applications - Implement prompt engineering techniques - Apply responsible AI practices |
| Topic 2: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement Azure AI Search solutions - Implement semantic and vector search - Design document processing pipelines - Create custom skills for Azure AI Search |
| Topic 3: Implement Natural Language Processing Solutions | 20-25% | - Translate text and speech - Create conversational AI solutions with Azure AI Bot Service - Analyze text using Azure AI Language - Implement speech capabilities using Azure AI Speech - Build question answering solutions |
| Topic 4: Plan and Manage an Azure AI Solution | 15-20% | - Implement governance and compliance for AI solutions - Select appropriate Azure AI services and technologies - Plan and configure secure AI solutions - Manage AI costs and resource allocation |
| Topic 5: Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Integrate Azure AI services into applications - Design and implement decision automation |
| Topic 6: Implement Computer Vision Solutions | 15-20% | - Implement object detection and image classification - Read and process documents using Azure AI Document Intelligence - Analyze images using Azure AI Vision - Process video and generate insights - Implement Azure AI Face service |
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NEW QUESTION # 346
You have an Azure subscription that contains an Azure Al Video Indexer account.
You need to add a custom brand and logo to the indexer and configure an exclusion for the custom brand.
How should you complete the REST API call? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 347
You have a product knowledgebase that contains multiple PDF documents.
You need to build a chatbot that will provide responses based on data in the knowledgebase. The solution must minimize development effort and costs.
What should you include in the solution?
Answer: C
Explanation:
* The requirement: A chatbot that answers based on a product knowledgebase (multiple PDFs).
* Best choice: Custom Question Answering in Azure AI Language.
* It allows you to ingest documents like PDFs, FAQs, manuals.
* Minimal development effort since you just upload documents and the service automatically indexes them.
* Other options:
* CLU # for intent/intent-based classification, not for knowledgebases.
* Language detection # only detects language, not Q&A.
* Azure OpenAI # could work, but more costly and requires prompt engineering (not the
"minimize effort/cost" requirement).
Microsoft Reference:
Custom question answering in Azure AI Language
NEW QUESTION # 348
You are planning the product creation project.
You need to build the REST endpoint to create the multilingual product descriptions.
How should you complete the URI? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-translate
NEW QUESTION # 349
You are developing the shopping on-the-go project.
You are configuring access to the QnA Maker resources.
Which role should you assign to AllUsers and LeadershipTeam? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/reference-role-based-access-control
NEW QUESTION # 350
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: B,C
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.
A). storageContainer
C). storageConnectionString
Microsoft References
Knowledge store in Azure Cognitive Search
How to define a knowledge store
NEW QUESTION # 351
......
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