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質問 # 111
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：
質問 # 112
Hotspot Question
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
Box: analyzes images to detect
A multimodal model analyzes images to detect, recognize, and reason about objects by combining computer vision with natural language processing.
Reference:
https://farukalamai.substack.com/p/object-detection-with-multimodal
質問 # 113
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?
正解：C
解説：
The requirement is to extract structured fields, including nested values, from invoices by using a defined schema. In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.
Microsoft's Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms. Microsoft's quickstart also shows invoice processing with the prebuilt- invoice analyzer to extract structured data from an invoice document.
質問 # 114
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
Correct selection: natural language processing
The correct answer is natural language processing . Interactive question answering requires an application to receive language from a user, interpret the meaning or intent of that language, and generate or retrieve an appropriate response. These are core capabilities associated with natural language processing (NLP) and modern generative AI systems.
Microsoft's current Azure AI Fundamentals study guide places text-oriented capabilities under AI workloads
, including generative and agentic AI, text analysis, speech, computer vision, and information extraction
. It also requires candidates to recognize scenarios involving text processing and to implement AI solutions that work with textual user input.
Computer vision is concerned with understanding or generating visual information such as images and video.
It would therefore apply to tasks such as image classification, object detection, OCR-related visual processing, or image interpretation-not ordinary text-based question answering.
Forecasting predicts future numerical or categorical values from historical data, such as future sales, demand, or resource usage. It does not describe conversational interaction with users.
Therefore, an application that interactively receives questions expressed in natural language and returns answers is fundamentally performing a natural language processing workload .
Study Guide Reference: Identify AI concepts and capabilities # Identify AI workloads # text analysis and generative AI scenarios.
質問 # 115
You are developing an application that converts text into spoken audio and saves the synthesized audio to a file by using Azure Speech in Foundry Tools.
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:
AudioOutputConfig(filename= " output.wav " )
The question specifically states the application must save the synthesized audio to a file . In the Azure Speech SDK for Python, speechsdk.audio.AudioOutputConfig(filename= " output.wav " ) directs the synthesizer to write the generated speech output directly to a WAV file on disk - which is exactly the requirement.
Why the other options are wrong:
* AudioOutputConfig(stream) - This routes audio output to an in-memory audio stream object, not a file. It is used when you want to process or play the audio programmatically without saving it to disk.
* AudioStreamFormat(wave_stream_format=AudioStreamWaveFormat.PCM) - This class defines the format of an audio stream (e.g., PCM encoding, sample rate). It is used when configuring custom audio streams, not when specifying a file output destination. It is not a valid argument for speechsdk.audio. in this context.
The correct and complete line is:
audio_config = speechsdk.audio.AudioOutputConfig(filename= " output.wav " )
質問 # 116
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