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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Describe features of computer vision workloads on Azure15-20%- Identify common computer vision tasks
- Describe Azure capabilities for computer vision
- Identify Azure AI services for computer vision
Topic 2: Describe fundamental principles of machine learning on Azure30-35%- Describe features of no-code automated ML
- Identify common machine learning tasks
- Describe core machine learning concepts
- Describe Azure Machine Learning capabilities
Topic 3: Describe AI workloads and considerations15-20%- Identify features of common AI workloads
- Identify guiding principles for responsible AI
Topic 4: Describe features of Generative AI workloads on Azure15-20%- Identify responsible AI considerations for generative AI
- Describe Azure OpenAI Service capabilities
- Describe generative AI concepts
Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure15-20%- Identify common NLP tasks
- Identify Azure AI services for NLP
- Describe Azure capabilities for NLP

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Microsoft Azure AI Fundamentals Sample Questions (Q328-Q333):

NEW QUESTION # 328
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.

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:


NEW QUESTION # 329
In which two scenarios can you use speech recognition? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: C,D

Explanation:
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features


NEW QUESTION # 330
What is an example of unsupervised machine learning?

Answer: B

Explanation:
In unsupervised machine learning, the algorithm learns patterns or structure within data without pre-labeled outputs or target values. The primary goal is to discover hidden relationships or group similar data points automatically. The Microsoft Azure AI Fundamentals (AI-900) study materials identify clustering as the key example of unsupervised learning.
In clustering, algorithms such as K-means, hierarchical clustering, or DBSCAN group data based on feature similarity. For example, a business may cluster customers by purchase behavior to discover natural customer segments without prior category labels. The model finds inherent patterns within the data rather than being told what to predict.
By contrast, classification and regression are supervised learning techniques. In supervised learning, the algorithm is trained using labeled data where correct outputs are already known. Therefore, the correct answer is B. Clustering, as it best represents unsupervised learning in Azure AI-900 principles.


NEW QUESTION # 331
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/cognitive-services/Custom-Vision-Service/overview


NEW QUESTION # 332
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:
Explanation
Graphical user interface, text, application, email Description automatically generated

Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles


NEW QUESTION # 333
......

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