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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals (AI-900) Exam
Exam Number:AI-900
Certificate Validity Period:Does not expire (Fundamentals certification)
Exam Price:Approx. 99 USD (varies by region)
Exam Duration:60 minutes
Passing Score:700 (out of 1000)
Real Exam Qty:40-60
Available Languages:German, Chinese (Traditional), Korean, English, Chinese (Simplified), Spanish, Portuguese (Brazil), French, Japanese
Exam Format:Case study (limited), Drag and drop, Multiple response, Multiple choice
Related Certifications:Microsoft Azure Fundamentals (AZ-900)
Microsoft Azure Data Fundamentals (DP-900)
Recommended Training:Microsoft Learn - AI-900 Learning Path
Azure AI Fundamentals Course
Exam Registration:Schedule exam via Pearson VUE
Microsoft Certification Portal
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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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.

Microsoft Azure AI Fundamentals Sample Questions (Q123-Q128):

NEW QUESTION # 123
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: B

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 # 124
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 125
Hotspot Question
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:
Box 1: No
No, a summarization model is not used to transcribe spoken audio into written text; instead, an automatic speech recognition (ASR) or speech-to-text model performs the transcription, while a summarization model is used afterward to shorten the resulting text.
Box 2: No
No, a standard chat completion model cannot directly generate vector embeddings for semantic tasks. While both types of models process natural language, they are distinct architectures optimized for entirely different outputs:
Chat Completion Models (e.g., gpt-4o, Claude 3.5 Sonnet) are generative. They take a text prompt and predict the next sequence of words to produce a conversational text response.
Embedding Models (e.g., text-embedding-3-small) are extractive. They compress text into a fixed- length numerical array (a vector) that mathematically represents the underlying meaning, or semantic context, of that text.
Box 3: Yes
Yes, an embedding model can generate vector representations of the provided input. It turns text, words, or images into a list of numbers. These numbers capture the meaning of the data.
How Embeddings Work
An embedding model maps complex data into a simple numeric space.
Inputs: Words, sentences, whole documents, or images.
Outputs: A dense vector of floating-point numbers.
Meaning: Similar inputs get vectors that sit close to each other in that space.
Reference:
https://aimultiple.com/speech-recognition
https://www.debutinfotech.com/blog/understanding-the-role-of-embedding-in-models-like-chat-gpt


NEW QUESTION # 126
Which natural language processing feature can be used to identify the main talking points in customer feedback surveys?

Answer: B

Explanation:
To identify main talking points in feedback using Azure natural language processing, the Key Phrase Extraction feature within the Azure AI Language service is the most appropriate choice.
This feature analyzes text and extracts the most relevant and important phrases, effectively highlighting the core topics or themes present in the feedback.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing


NEW QUESTION # 127
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 128
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