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Prepare for the Microsoft Designing and Implementing a Microsoft Azure AI Solution exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.
QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Microsoft AI-102 exam and achieve success.
The questions for AI-102 were last updated on Feb 19, 2025.
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You are developing a method for an application that uses the Translator API.
The method will receive the content of a webpage, and then translate the content into Greek (el). The result will also contain a transliteration that uses the Roman alphabet.
You need to create the URI for the call to the Translator API. You have the following URI.
https://api.cognitive.microsofttranslator.com/translate?api-version=3.0
Which three additional query parameters should you include in the URI? Each correct answer presents part of the solution. (Choose three.)
NOTE: Each correct selection is worth one point.
C: textType is an optional parameter. It defines whether the text being translated is plain text or HTML text (used for web pages).
D: to is a required parameter. It specifies the language of the output text. The target language must be one of the supported languages included in the translation scope.
F: toScript is an optional parameter. It specifies the script of the translated text. We use Latin (Roman alphabet) script.
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-translate
You are developing the document processing workflow.
You need to identify which API endpoints to use to extract text from the financial documents. The solution must meet the document processing requirements.
Which two API endpoints should you identify? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
C: Analyze Receipt - Get Analyze Receipt Result.
Query the status and retrieve the result of an Analyze Receipt operation.
Request URL: https://{endpoint}/formrecognizer/v2.0-preview/prebuilt/receipt/analyzeResults/{resultId}
E: POST {Endpoint}/vision/v3.2/read/analyze
Use this interface to get the result of a Read operation, employing the state-of-the-art Optical Character Recognition (OCR) algorithms optimized for text-heavy documents.
Scenario: Contoso plans to develop a document processing workflow to extract information automatically from PDFs and images of financial documents
The document processing solution must be able to process standardized financial documents that have the following characteristics:
- Contain fewer than 20 pages.
- Be formatted as PDF or JPEG files.
- Have a distinct standard for each office.
*The document processing solution must be able to extract tables and text from the financial documents.
The document processing solution must be able to extract information from receipt images.
https://docs.microsoft.com/en-us/rest/api/computervision/3.1/read/read
You manage an application that stores data in a shared folder on a Windows server. You need to move the shared folder to Azure Storage. Which type of Azure Storage should you use?
You need to implement a table projection to generate a physical expression of an Azure Cognitive Search index.
Which three properties should you specify in the skillset definition JSON configuration table node? Each correct answer presents part of the solution. (Choose three.)
NOTE: Each correct selection is worth one point.
Defining a table projection.
Each table requires three properties:
tableName: The name of the table in Azure Storage.
generatedKeyName: The column name for the key that uniquely identifies this row.
source: The node from the enrichment tree you are sourcing your enrichments from. This node is usually the output of a shaper, but could be the output of any of the skills.
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview
You have an existing Azure Cognitive Search service.
You have an Azure Blob storage account that contains millions of scanned documents stored as images and PDFs.
You need to make the scanned documents available to search as quickly as possible. What should you do?
Incorrect Answers:
A: Need more search units to process the data in parallel. B: Run them in parallel, not sequentially.
C: Need a blob indexer.
Note: A blob indexer is used for ingesting content from Azure Blob storage into a Cognitive Search index. Index large datasets
Indexing blobs can be a time-consuming process. In cases where you have millions of blobs to index, you can speed up indexing by partitioning your data and using multiple indexers to process the data in parallel. Here's how you can set this up:
Partition your data into multiple blob containers or virtual folders Set up several data sources, one per container or folder.
Create a corresponding indexer for each data source. All of the indexers should point to the same target search index.
One search unit in your service can run one indexer at any given time. Creating multiple indexers as described above is only useful if they actually run in parallel.
https://docs.microsoft.com/en-us/azure/search/search-howto-indexing-azure-blob-storage
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