AI-901日本語試験無料問題集「Microsoft Azure AI Fundamentals (AI-901日本語版) 認定」

Explanation:
Azure Text Analytics client library
Comprehensive and Detailed Explanation with all Azure AI documents : =
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure AI Language text analysis features, such as sentiment analysis, named entity recognition, key phrase extraction, language detection, and text summarization, are accessed in code by using the Azure Text Analytics client library .
The other options are incorrect:
Azure AI Projects client library is used for Foundry project and agent-related workflows, not specifically Azure Language text analysis.
Azure OpenAI client library is used to call OpenAI model deployments, not Azure Language text analytics APIs.
Azure Speech SDK is used for speech-to-text and text-to-speech workloads, not text analysis.

解説: (GoShiken メンバーにのみ表示されます)

Explanation:

Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze multiple content types, including documents, forms, images, audio, and video.
Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format.
= Yes
Azure Content Understanding returns structured analysis results in JSON format. This is how extracted fields, values, confidence scores, and other analysis results are represented.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can use analyzers and schemas to extract structured fields from documents and forms, such as invoices, receipts, contracts, and other business documents.

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 " )

解説: (GoShiken メンバーにのみ表示されます)

Explanation:

Statement 1: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No Fine-tuning is optional. Microsoft's Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.
Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project , and Foundry playgrounds are used for prototyping and validation before production.
Therefore, the statement using "only after you deploy" is too restrictive.
Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy .

Explanation:

The correct Python method is:
recognizer.recognize_once()
Completed code:
import azure.cognitiveservices.speech as speechsdk
speech_config = speechsdk.SpeechConfig(subscription=key, region=region) recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config) recognizer.recognize_once() Microsoft's Azure Speech documentation for Foundry Tools explains that Speech to text is used for real-time speech recognition and converting spoken audio into text. The Python Speech SDK uses a SpeechRecognizer object for recognition from audio input, such as a microphone.
Why the other options are incorrect:
recognizer.speak_text_async( " Ready " ) is incorrect because speaking text is text-to-speech , not speech-to- text recognition.
recognizer.start_continuous_recognition() can be used for continuous recognition, but the code shown is asking for the basic method to recognize spoken input and convert it to text from the SpeechRecognizer.
recognizer.start_keyword_recognition() is used for keyword/wake-word recognition, not general speech-to- text transcription of spoken commands.
Therefore, the correct answer is:
recognizer.recognize_once()

解説: (GoShiken メンバーにのみ表示されます)