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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Translator
- Describe capabilities of Azure Speech
- Describe capabilities of Azure Language
- Identify types of NLP solutions
Topic 2: Features of generative AI workloads on Azure20–25%- Describe responsible AI practices for generative AI
- Describe use cases for generative AI
- Describe generative AI concepts
- Describe capabilities of Azure OpenAI Service
Topic 3: Artificial Intelligence workloads and considerations15–20%- Describe considerations for developing AI solutions
- Describe responsible AI principles
- Identify types of AI workloads
Topic 4: Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Custom Vision
- Identify types of computer vision solutions
- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Face
- Describe capabilities of Azure Form Recognizer
Topic 5: Fundamental principles of machine learning on Azure15–20%- Describe capabilities of Azure Machine Learning
- Describe core concepts of machine learning
- Describe machine learning pipelines
- Describe automated machine learning

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Microsoft Azure AI Fundamentals Sample Questions (Q162-Q167):

NEW QUESTION # 162
You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?

Answer: B

Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles


NEW QUESTION # 163
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:

Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0


NEW QUESTION # 164
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
* Include one or more faces.
* Contain at least one person wearing sunglasses.
What should you use to analyze the images?

Answer: A

Explanation:
Section: Describe Artificial Intelligence workloads and considerations
Explanation/Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview


NEW QUESTION # 165
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:

Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing


NEW QUESTION # 166
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?

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 # 167
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