Microsoft certification AI-900 the latest examination questions and answers come out

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Microsoft AI-900 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Describe AI workloads and considerations	20-25%	- Fundamentals of artificial intelligence concepts 1. Common AI workloads 2. Responsible AI principles
Topic 2: Fundamentals of machine learning on Azure	25-30%	- Core machine learning concepts 1. Training and validation concepts 2. Supervised vs unsupervised learning
Topic 3: Features of computer vision workloads on Azure	15-20%	- Computer vision solutions 1. Object detection 2. OCR and image analysis 3. Image classification
Topic 4: Features of natural language processing (NLP) workloads on Azure	30-35%	- Text analytics and language understanding 1. Language modeling and translation 2. Sentiment analysis 3. Key phrase extraction

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Microsoft Azure AI Fundamentals Sample Questions (Q203-Q208):

NEW QUESTION # 203
You have a frequently asked questions (FAQ) PDF file.
You need to create a conversational support system based on the FAQ.
Which service should you use?

A. QnA Maker
B. Computer Vision
C. Language Understanding (LUIS)
D. Text Analytics

Answer: A

Explanation:
Explanation
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/

NEW QUESTION # 204
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer.
What should you do?

A. Split the original dataset into a dataset for training and a dataset for testing. Use the training dataset for evaluation.
B. Split the original dataset into a dataset for training and a dataset for testing. Use the testing dataset for evaluation.
C. Use the original dataset for training and evaluation.
D. Split the original dataset into a dataset for features and a dataset for labels. Use the features dataset for evaluation.

Answer: B

NEW QUESTION # 205
You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of Al workload should you use?

A. anomaly detection
B. natural language processing
C. conversational Al
D. computer vision

Answer: B

Explanation:
According to the AI-900 study guide and Microsoft Learn module "Identify features of natural language processing workloads", Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language. Tasks such as extracting key terms, summarizing documents, identifying topics, or determining sentiment fall under NLP workloads.
In this question, you have insurance claim reports stored as text, and you need to extract key terms to generate summaries. This matches the Text Analytics service in Azure Cognitive Services, which uses NLP techniques such as key phrase extraction to identify important concepts within textual data.
The other options are incorrect because:
* A. Conversational AI focuses on chatbots or dialogue systems.
* B. Anomaly detection identifies unusual data patterns, not textual meaning.
* D. Computer vision processes image or video content, not text.
Therefore, extracting key terms from documents is a clear example of Natural Language Processing.
Reference:Microsoft Learn - Identify features of Natural Language Processing (NLP) workloads

NEW QUESTION # 206
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:

NEW QUESTION # 207
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

NEW QUESTION # 208
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