確かにMicrosoft AI-901試験に準備する過程は苦しいんですけど、Microsoft AI-901資格認定を手に入れるなり、IT業界で仕事のより広い将来性を持っています。あなたの努力を無駄にするのは我々Jpexamのすべきことです。Jpexamのレビューから見ると、弊社Jpexamは提供している質高い試験資料は大勢の顧客様の認可を受け取ったと考えられます。我々はあなたにMicrosoft AI-901試験に合格させるために、全力を尽くします。
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AI-901有用なテストガイド資料は、最も重要な情報を最も簡単な方法でクライアントに提示するので、AI-901有用なテストガイドを学習するための時間とエネルギーはほとんど必要ありません。クライアントは、テストの学習と準備に20〜30時間しかかかりません。仕事や学習などで忙しい人にとっては、これは良いニュースです。なぜなら、テストの準備に十分な時間がないことを心配する必要がなく、主なことをゆっくりとできるからです。 AI-901学習実践ガイドをご覧ください。ですから、AI-901試験の教材の大きな利点であり、クライアントにとって非常に便利です。
質問 # 62
You plan to create an Al application that will use Azure OpenAI in Foundry Models. The solution requires that a specific amount of throughput be allocated.
Which type of deployment should you use?
正解：A
解説：
Use a provisioned deployment when the solution requires a specific allocated amount of throughput.
Microsoft states that provisioned throughput is a deployment type that lets you specify the amount of throughput required for a model deployment. Unlike standard deployments, where capacity is shared and throughput can vary, a provisioned deployment holds a fixed amount of processing capacity for the deployment.
Why the other options are incorrect:
A). global batch is for asynchronous batch processing, not dedicated real-time throughput allocation.
B). data zone standard and D. standard use shared capacity; they do not allocate a fixed, specified amount of throughput to the deployment.
質問 # 63
You are developing an application that analyze invoices by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application retrieves the analysis results after processing completes.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
The completed code is:
poller = client.begin_analyze(
analyzer_id= " invoice " ,
input_url=url
)
result = poller.result()
Azure Content Understanding analysis uses a long-running operation pattern. The Python SDK returns a poller from begin_analyze(), and Microsoft documentation states that the SDK poller handles polling automatically when you call .result().
Therefore, to retrieve the analysis results after processing completes, the correct option is:
result
The other options are incorrect because status checks operation state, wait waits without returning the final analysis object, and get_results is not the method shown for retrieving the begin_analyze() result in this code pattern.
質問 # 64
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 65
You are developing an application that extracts structured information from different types of content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate analyzer types to the correct content types. Each analyzer type 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.
正解：
解説：
Explanation:
Azure Content Understanding analyzers define the type of content to process, such as documents, images, audio, or video , and what to extract, such as text, layout, fields, transcripts, and structured JSON output.
Microsoft lists base analyzer types such as prebuilt-document, prebuilt-audio, prebuilt-video, and prebuilt- image.
For scanned invoices in PDF format , the correct choice is document analyzer , because invoices are document/form content. Microsoft's Content Understanding quickstart specifically uses the prebuilt-invoice analyzer to extract structured data from an invoice PDF.
For voicemail recordings in WAV format , the correct choice is audio analyzer , because voicemail is audio content. Content Understanding supports structured data extraction from audio files, and the analyzer reference identifies prebuilt-audio as the base analyzer for audio content.
質問 # 66
You are developing an application that will coordinate a travel-booking workflow by gathering user preferences, searching for options, requesting approval, and booking services. The solution will maintain context across the steps.
Which type of Al workload is this an example of?
正解：C
解説：
The described solution coordinates a multi-step workflow : gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI
.
Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.
Why the other options are incorrect:
A). information extraction extracts structured data from content.
C). computer vision analyzes images or video.
D). generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI .
質問 # 67
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古く時から一寸の光陰軽るんずべからずの諺があって、あなたはどのぐらい時間を無駄にすることができますか？現時点からJpexamのAI-901問題集を学んで、時間を効率的に使用するだけ、AI-901知識ポイントを勉強してMicrosoftのAI-901試験に合格できます。短い時間でAI-901資格認定を取得するような高いハイリターンは嬉しいことではないでしょうか。
AI-901認定試験トレーリング: https://www.jpexam.com/AI-901_exam.html