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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 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 |
| Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Manage security, compliance, and responsible AI - Select appropriate Azure AI services |
| Implement Natural Language Processing Solutions | - Implement question answering and conversational AI - Translate and interpret language using Azure AI services - Analyze text using Azure AI Language |
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NEW QUESTION # 261
You have an Azure subscription that contains an Azure Cognitive Service for Language resource. You need to identify the URL of the REST interface for the Language service. Which blade should you use in the Azure portal?
Answer: D
NEW QUESTION # 262
You are building a retail chatbot that will use a QnA Maker service.
You upload an internal support document to train the model. The document contains the following question:
"What is your warranty period?"
Users report that the chatbot returns the default QnA Maker answer when they ask the following question:
"How long is the warranty coverage?"
The chatbot returns the correct answer when the users ask the following question: 'What is your warranty period?" Both questions should return the same answer.
You need to increase the accuracy of the chatbot responses.
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. (Choose three.)
Answer:
Explanation:
Explanation
Step 1: Add alternative phrasing to the question and answer (QnA) pair.
Add alternate questions to an existing QnA pair to improve the likelihood of a match to a user query.
Step 2: Retrain the model.
Periodically select Save and train after making edits to avoid losing changes.
Step 3: Republish the model
Note: A knowledge base consists of question and answer (QnA) pairs. Each pair has one answer and a pair contains all the information associated with that answer.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/edit-knowledge-base
NEW QUESTION # 263
SIMULATION
You plan to create a solution to generate captions for images that will be read from Azure Blob Storage.
You need to create a service in Azure AI Services for the solution. The service must be named captions12345678 and must use the Free pricing tier.
To complete this task, sign in to the Azure portal.
Answer:
Explanation:
Part 1: Create a search service captions12345678
Step 1: Sign in to the QnA portal.
Step 2: Create an Azure Cognitive multi-service resource:
Step 3: On the Create page, provide the following information.
Name: captions12345678
Pricing tier: Free -
Step 4: Click Review + create -
(Step 5: Create a data source
In Connect to your data, choose Azure Blob Storage. Choose an existing connection to the storage account and container you created. Give the data source a name, and use default values for the rest.)
Reference:
https://docs.microsoft.com/en-us/azure/search/search-create-service-portal
https://docs.microsoft.com/en-us/azure/search/cognitive-search-quickstart-ocr
NEW QUESTION # 264
You have 100,000 images.
You need to build an app that will perform the following actions:
* Identify road signs in the images and extract the text on the signs.
* Analyze the text to identify well-known locations.
The solution must minimize development effort.
What should you use for each action? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The app must:
* Identify road signs and extract text from images
* This is an OCR (Optical Character Recognition) task.
* Azure AI Vision provides OCR capabilities to detect and extract text from images (including road signs).
* Correct choice: Azure AI Vision.
* Analyze the text to identify well-known locations
* Once text is extracted, you need to detect if it refers to well-known places (e.g., cities, landmarks).
* This is a Named Entity Recognition (NER) problem.
* Azure AI Language service includes entity recognition for identifying locations, people, organizations, etc.
* Correct choice: Azure AI Language.
Other options:
* Azure AI Document Intelligence # Focused on structured documents (invoices, forms), not road signs.
* Azure AI Search # Useful for indexing and search, not text extraction or entity recognition.
Correct Answer Matrix:
* Extract the text # Azure AI Vision
* Identify well-known locations # Azure AI Language
* Azure AI Vision - OCR text extraction
* Azure AI Language - Named Entity Recognition (NER)
NEW QUESTION # 265
You have an Azure subscription that contains an Azure Al Search resource named AS1.
You implement a custom skill in AS1 that performs language and sentiment analysis of documents.
You ate evaluating the use of AS1 as part of an enrichment pipeline.
In which order will AS1 index the documents? To answer, move all indexing stages from the list of stages to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation
When an indexer runs in Azure AI Search, it processes each document through a fixed series of stages:
* Document cracking The indexer opens the item (file/row) and extracts text, images, and metadata, creating the root of the enriched document (/document). This is always the first stage.
* Field mappings Next, source fields are mapped directly to target index fields before any transformations . Field mappings occur after document cracking but before skillset execution .
Microsoft Learn
* Skillset execution Built-in or custom skills (for example, language and sentiment analysis in your scenario) run and write outputs into the in-memory enriched document. Microsoft Learn
* Output field mappings Because skill outputs live only in memory, you must define output field mappings to route selected nodes of the enriched document into index fields. This step comes after skillset execution.
* Push to index Finally, the indexer hands off raw and enriched content to the search index (the physical data structures) so it becomes searchable-i.e., the content is "pushed to the index." Microsoft Learn This order- document cracking # field mappings # skillset execution # output field mappings # push to index -ensures raw content is extracted, direct source-to- index mappings are applied, AI enrichment is performed, enriched outputs are projected to fields, and the completed documents are written into the index.
Microsoft Azure AI References
* Indexers in Azure AI Search - Stages of indexing (Document cracking; Field mappings; Skillset execution; Output field mappings, with field mappings occurring before transformations). Microsoft Learn
* Skillset concepts (Output of skillset is routed to the index via output field mappings after skillset execution). Microsoft Learn
* AI enrichment concepts (Field mappings move raw source content to the index; output field mappings move enriched content; indexing ingests raw and enriched content into the physical index). Microsoft Learn
* Annotations & enriched document tree (Enriched document is created during document cracking at
/document). Microsoft Learn
NEW QUESTION # 266
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