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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-901 |
| Real Exam Qty: | 30-40 |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple choice, Multiple select, Case studies |
| Exam Price: | $99 USD |
| Certificate Validity Period: | Lifetime |
| Available Languages: | Portuguese (Brazil), German, Japanese, Arabic (Saudi Arabia), Korean, Chinese (Traditional), Italian, Indonesian (Indonesia), French, Russian, Chinese (Simplified), Spanish, English |
| Passing Score: | 700 |
| Recommended Training: | Microsoft Learn: AI concepts for developers and technology professionals Microsoft Learn: Get started with Microsoft Foundry |
| Exam Registration: | Microsoft Certification Exam Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No required prerequisites; basic understanding of cloud computing and familiarity with Python syntax recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-901/ |
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NEW QUESTION # 36
You need to build an AI solution that responds to user queries with detailed written explanations.
Which type of AI model should you use?
Answer: D
NEW QUESTION # 37
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous .
This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern - you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
* Synchronous is incorrect - the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
* Returned only as unstructured plain text is incorrect - Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
* Limited to OCR-only processing is incorrect - Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.
NEW QUESTION # 38
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis workloads include capabilities such as sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics features. In application code, these text analysis capabilities are accessed 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 development.
Azure OpenAI client library is used to call Azure OpenAI model deployments.
Azure Speech SDK is used for speech recognition, speech synthesis, and other speech workloads.
NEW QUESTION # 39
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.
Answer:
Explanation:
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 " )
NEW QUESTION # 40
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following table.
Which type of natural language processing was performed?
Answer: A
Explanation:
The output identifies text values and labels them with entity categories, such as:
now [DateTime]
students [PersonType]
Microsoft [Organization]
175 [Quantity-Number]
Today [DateTime-Date]
This is entity recognition , also known as Named Entity Recognition (NER) . NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.
Why the other options are incorrect:
B). translation would convert text from one language to another.
C). key phrase extraction would return important phrases or main topics, not categorized entity labels.
D). sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.
NEW QUESTION # 41
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