Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) - AI-102日本語 Exam Practice Test

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Explanation:
Data Analyst.
When dealing with data roles in an organization, responsibilities are divided among data analysts, data engineers, and data scientists. Let's review them:
Data Analyst
Focuses on understanding business requirements and ensuring that the right rules and logic are applied to data so it becomes meaningful for decision-making.
They work closely with stakeholders to define business rules such as data validation rules, aggregation logic, and reporting metrics.
This makes them responsible for identifying which business rules must be applied to data.
Data Engineer
Responsible for designing, building, and maintaining the infrastructure and pipelines that move and process data.
They ensure data availability, reliability, and scalability, but do not usually define business rules.
Data Scientist
Focuses on advanced analytics, predictive modeling, and machine learning.
They use data prepared by engineers and analysts but are not typically the ones who define the business rules that govern how raw data should be interpreted.
Therefore, the correct answer is Data Analyst.
The answer: A data analyst
Microsoft References
Data roles and responsibilities in Azure
What is a Data Analyst?
Responsibilities of Data Engineers vs Data Scientists vs Data Analysts

Explanation:
Comprehensive Detailed Explanation
You're adding multiple images for each person into a PersonGroup using the Face API SDK.
File.OpenRead(imagePath) returns a Stream, so the using declaration must declare Stream t = File.OpenRead (imagePath) .
To upload an image from a stream to a person in a person group, call
faceClient.PersonGroupPerson.AddFaceFromStreamAsync(personGroupId, personId, t); Putting it together:
Parallel.For( 0 , PersonCount, async i = >
{
Guid personId = persons[i].PersonId;
string personImageDir = $ " /path/to/person/ {i} /images " ;
foreach ( string imagePath in Directory.GetFiles(personImageDir, " *.jpg " ))
{
using (Stream t = File.OpenRead(imagePath))
{
await faceClient.PersonGroupPerson.AddFaceFromStreamAsync(
personGroupId, personId, t);
}
}
});
This loops the images for each person, opens each as a stream, and adds the face to the specified person in the person group.
References
Microsoft Docs - Face API: Add a face to a person ( AddFaceFromStreamAsync , AddFaceFromUrlAsync ) in PersonGroupPerson operations.
https://learn.microsoft.com/azure/ai-services/face/ (see SDK/PersonGroupPerson methods) NET File.OpenRead returns Stream .
https://learn.microsoft.com/dotnet/api/system.io.file.openread

Explanation:

To let an Azure AI Agent access publicly available, recent web content, you add the Bing grounding tool. In the SDK, you first define a ToolConnectionList with the Bing connection ID (here, " bingConnectionId " ), then construct a BingGroundingToolDefinition(connectionList) so the agent can call Bing Search with recency filters (e.g., last 90 days) when answering questions.
When creating the agent, tool definitions are passed via the tools: parameter of CreateAgentAsync(...) as a List < ToolDefinition > . The toolResources parameter is used when you must also supply resource descriptors (for example, an Azure AI Search index configuration), which is not needed here because the Bing grounding tool already references the connection through the ToolConnectionList .
Therefore:
Use BingGroundingToolDefinition to ground the agent on current public web data.
Provide that tool in tools: new List < ToolDefinition > { grounding } when calling CreateAgentAsync(...) .
Microsoft Azure AI References (titles only)
Azure AI Agent Service - Bing web grounding tool (tool definitions and connections) Azure AI Agent Service SDK - Agent creation ( CreateAgentAsync parameters: tools vs toolResources ) Azure AI Agent Service - Tool connections and ToolConnectionList usage

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Explanation:

Comprehensive Detailed Explanation
You're analyzing a PDF (Test.pdf) that "contains tabular data." In Azure AI Document Intelligence, the Layout prebuilt model is the right choice when your primary goal is to extract document structure such as tables, lines, words, selection marks, and paragraphs. Microsoft's documentation clearly states that the Layout model "extracts text, tables, selection marks, and document structure," which is exactly what you need for tabular content. Microsoft Learn While the General document (prebuilt-document) model can also return tables, it is intended for broader extraction (including key-value pairs, entities, etc.). For a document whose key requirement is table extraction, Microsoft guidance recommends starting with Layout for structure-first scenarios.
For authentication in the REST call, the correct header to pass your resource key is Ocp-Apim-Subscription- Key . This is explicitly shown in the official REST API reference for the Analyze Document operation under Security (Type: apiKey, In: header). Microsoft Learn Putting it together, the command shape is:
curl -v -i POST " {endpoint}/formrecognizer/documentModels/prebuilt-layout:analyze?api-version=2023-07-
31 " \
-H " Content-Type: application/json " \
-H " Ocp-Apim-Subscription-Key: {yourkey} " \
--data-ascii " {\ " urlSource\ " : \ " test.pdf\ " } "
This uses a single analyze call to the prebuilt-layout model to extract the tables and authenticates with the Ocp-Apim-Subscription-Key header-matching the two selections in the answer area. The model matrix and overview pages also confirm Layout supports tables. Microsoft Learn References (Microsoft Documentation) Document layout analysis (Layout model) - capabilities include extracting tables, text, selection marks, and structure. Microsoft Learn Choose the best Document Intelligence model - guidance on when to use Layout (structure-first; tables).
Microsoft Learn
General document (prebuilt-document) model - broader extraction including key-value pairs and tables.
Microsoft Learn
Model overview / matrix - shows Layout supports Tables. Microsoft Learn Analyze Document (REST API) - Ocp-Apim-Subscription-Key header for authentication. Microsoft Learn

Explanation:

To build narration for video training content using Custom Neural Voice, we must know where each step is done:
Creating a Custom Neural Voice
A Custom Neural Voice is a text-to-speech (TTS) feature within Azure AI Speech.
It allows you to create a unique, natural-sounding synthetic voice.
The correct tool to build and configure a custom voice is the Speech Studio portal.
Not the Language Understanding portal (LUIS), Azure portal directly, or Bot Framework Composer.
Generating narration
Narration requires converting written text into speech.
This is done via the Text-to-Speech service in Azure AI Speech.
Other options:
Language Understanding # For intent/utterance processing, not narration.
Speaker Recognition # For identifying/authenticating speakers, not narration.
Speech-to-text # Converts spoken words to text (the opposite direction).
Thus:
Custom Neural Voice # Speech Studio portal
Narration # Text-to-speech
Correct Answers:
Custom neural voice: The Speech Studio portal
Narration: Text-to-speech
Microsoft References
Custom Neural Voice in Speech Studio
Text-to-Speech overview

Explanation:

Scenario: All videos must have transcripts that are associated to the video and included in product descriptions.
Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.
Step 1: Upload the video to blob storage
Given a video or audio file, the file is first dropped into a Blob Storage. T Step 2: Index the video by using the Video Indexer API.
When a video is indexed, Video Indexer produces the JSON content that contains details of the specified video insights. The insights include: transcripts, OCRs, faces, topics, blocks, etc.
Step 3: Extract the transcript from the Video Indexer API.
Step 4: Translate the transcript by using the Translator API.
Reference:
https://azure.microsoft.com/en-us/blog/get-video-insights-in-even-more-languages/
https://docs.microsoft.com/en-us/azure/media-services/video-indexer/video-indexer-output-json-v2

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Explanation:
The agent will reason over the uploaded file.##
The code will create a run and check the output.##
The code will create an agent and enable a file search.##
The agent will reason over the uploaded file.
Yes : The code includes file = project_client.agents.upload_file_and_poll(file_path= " ./data/file1.zip " , purpose=FilePurpose.AGENTS). The upload_file_and_poll method with FilePurpose.AGENTS suggests that the file (file1.zip) is uploaded for the agent to process, and agents in the Azure AI Foundry Agent Service are typically designed to reason over uploaded data. This indicates the agent will reason over the uploaded file.
The code will create a run and check the output.
No : The provided code snippet does not include any explicit call to create a run (e.g., a method like create_run or start_run) or check the output (e.g., a method to retrieve or poll run results). The code only uploads a file and creates a vector store, but it does not initiate or monitor a run.
The code will create an agent and enable a file search.
No : The code does not contain a method to create an agent (e.g., create_agent) or explicitly enable a file search. The create_vector_store_and_poll method creates a vector store, which is typically used for indexing data for search, but there is no indication of enabling a file search or creating an agent in this snippet.