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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-901
Exam Format:Multiple select, Multiple choice, Case studies
Certificate Validity Period:Lifetime
Exam Price:$99 USD
Available Languages:Italian, Russian, Chinese (Traditional), Portuguese (Brazil), Arabic (Saudi Arabia), Korean, German, English, Indonesian (Indonesia), Japanese, Spanish, Chinese (Simplified), French
Real Exam Qty:30-40
Passing Score:700
Exam Duration:60 minutes
Recommended Training:Microsoft Learn: AI concepts for developers and technology professionals
Microsoft Learn: Get started with Microsoft Foundry
Exam Registration:Pearson VUE Scheduling
Microsoft Certification Exam Registration
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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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Topic 2
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

Microsoft Azure AI Fundamentals Sample Questions (Q54-Q59):

NEW QUESTION # 54
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 # 55
You have a Microsoft Foundry project that has a generative AI model deployment.
You need to ensure that responses generated by the model minimize costs and remain within a defined length.
Which parameter should you configure?

Answer: A


NEW QUESTION # 56
Which feature of the Azure Language in Foundry Tools service should you use to automate the masking of names and phone numbers in text data?

Answer: A

Explanation:
To automate masking of names and phone numbers in text data, use Personally Identifiable Information (PII) detection in Azure Language.
PII detection identifies sensitive personal information such as names, phone numbers, email addresses, addresses, and other personal identifiers. It can be used to detect and redact or mask that information from text.
The other options are incorrect:
A). custom text classification categorizes documents or text into labels.
C). entity linking identifies entities and links them to knowledge base entries.
D). custom named entity recognition (NER) can extract custom entities, but for built-in masking of names and phone numbers, PII detection is the correct feature.


NEW QUESTION # 57
You are creating an app to help employees write emails and reports based on user prompts.
What should you use?

Answer: A

Explanation:
Azure OpenAI Service can generate text based on user prompts. It leverages GPT (Generative Pre-trained Transformer) models that are trained to understand and generate human-like text.
Users interact with these models by providing a prompt, and the model attempts to produce the most likely continuation or completion of that text.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models


NEW QUESTION # 58
Your company wants to build a recycling machine for bottles. The recycling machine must use cameras to automatically identify bottles of the correct shape and reject all other items.
Which type of Al workload should the company use?

Answer: C

Explanation:
A recycling machine that uses cameras to identify bottles of the correct shape and reject other items requires analysis of visual input. This is a computer vision workload.
Computer vision is used for image and video analysis tasks such as object detection, classification, shape recognition, and visual inspection.
The other options are incorrect:
A). knowledge mining extracts insights from large collections of documents or data.
B). natural language processing analyzes or generates language.
C). generative AI creates new content, but the requirement is to inspect camera images and identify objects.


NEW QUESTION # 59
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