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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals (AI-900) Exam |
| Exam Number: | AI-900 |
| Available Languages: | Japanese, Spanish, English, Korean, Portuguese (Brazil), German, Chinese (Traditional), French, Chinese (Simplified) |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Related Certifications: | Microsoft Azure Data Fundamentals (DP-900) Microsoft Azure Fundamentals (AZ-900) |
| Exam Format: | Drag and drop, Multiple choice, Case study (limited), Multiple response |
| Exam Price: | Approx. 99 USD (varies by region) |
| Passing Score: | 700 (out of 1000) |
| Recommended Training: | Microsoft Learn - AI-900 Learning Path Azure AI Fundamentals Course |
| Exam Registration: | Microsoft Certification Portal Schedule exam via Pearson VUE |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/ |
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NEW QUESTION # 129
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The Model catalog is used for comparing and deploying a wide range of models for generative AI development.
Microsoft states that the model catalog in Foundry portal is the hub for discovering and using a wide range of models to build generative AI applications. Microsoft training also states that the model catalog is used to compare models by benchmark metrics and deploy models to endpoints.
NEW QUESTION # 130
Hotspot Question
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.
Answer:
Explanation:
NEW QUESTION # 131
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 # 132
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: natural language processing
The interactive answering of questions entered by a user as part of an application is a direct example of natural language processing (NLP).
How NLP Powers Interactive Q&A
Text interpretation: The system uses linguistic rules and machine learning models to read and interpret the user's typed input.
Intent recognition: It figures out what the user wants or needs by analyzing the meaning behind the words.
Response generation: It formulates and delivers a human-like reply back through the application interface.
Reference:
https://www.gauthmath.com/solution/1800845210023942/The-interactive-answering-of-questions-entered-by-a-user-as-part-of-an-applicati
NEW QUESTION # 133
What should you use to explore pretrained generative AI models available from Microsoft and third-party providers?
Answer: C
Explanation:
Azure AI Foundry Models gives you access to flagship models in Azure AI Foundry to consume them as APIs with flexible deployment options.
Depending on what kind of project you're using in Azure AI Foundry, you might see a different selection of these models. Specifically, if you're using a Foundry project, built on an Azure AI Foundry resource, you see the models that are available for standard deployment to a Foundry resource. Alternatively, if you're using a hub-based project, hosted by an Azure AI Foundry hub, you see models that are available for deployment to managed compute and serverless APIs.
These model selections do overlap in many cases, since many models support the multiple deployment options.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models
NEW QUESTION # 134
......
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