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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-901
Exam Format:Case studies, Multiple choice, Multiple select
Real Exam Qty:30-40
Certificate Validity Period:Lifetime
Exam Duration:60 minutes
Passing Score:700
Exam Price:$99 USD
Available Languages:Chinese (Simplified), English, Indonesian (Indonesia), French, German, Italian, Chinese (Traditional), Arabic (Saudi Arabia), Portuguese (Brazil), Japanese, Korean, Spanish, Russian
Recommended Training:Microsoft Learn: Get started with Microsoft Foundry
Microsoft Learn: AI concepts for developers and technology professionals
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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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 (Q77-Q82):

NEW QUESTION # 77
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?

Answer: D

Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.


NEW QUESTION # 78
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 79
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 80
For each of the following statements, select Yes if the statement is true Otherwise, select No NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
* No
* Yes
* No
Comprehensive and Detailed Explanation with all Azure AI documents : =
Statement 1: Speech synthesis converts spoken audio into written text. = No Speech synthesis, also called text to speech , converts written text into spoken audio. Converting spoken audio into written text is speech recognition or speech to text .
Statement 2: Speech recognition can detect and transcribe spoken input. = Yes Speech recognition is used to identify spoken language in audio and convert it into text.
Statement 3: Text to speech in Azure Speech in Foundry Tools can translate text input into audible speech in a different language. = No Text to speech converts text into spoken audio. Translation into another language is a separate translation capability, not standard text-to-speech synthesis.


NEW QUESTION # 81
You are developing an application that uses the Azure Language SDK and a Microsoft Foundry resource.
You need to return a list of important topics from unstructured text.
Which Python method should you use?

Answer: B

Explanation:
To return a list of important topics from unstructured text using the Azure Language Python SDK within a Microsoft Foundry resource, you should use the extract_key_phrases method.
Depending on whether you are running synchronous or asynchronous operations for large batches of documents, choose one of the following methods from the TextAnalyticsClient:
client.extract_key_phrases(documents)
Used for immediate, synchronous processing of text data. It returns results in real time and is ideal for smaller or interactive application workloads.client.begin_extract_key_phrases(documents) Used for asynchronous (batch) processing. It returns a long-running operation poller, which is highly recommended when analyzing large volumes of unstructured text or multiple documents simultaneously.
Reference:
https://notes.kodekloud.com/docs/AI-102-Microsoft-Certified-Azure-AI-Engineer-Associate/Analyzing-Text/Working-with-Azure-AI-Language-Services/page


NEW QUESTION # 82
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