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Microsoft AI-900 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Describe features of Generative AI workloads on Azure	15-20%	- Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities - Describe generative AI concepts
Topic 2: Describe fundamental principles of machine learning on Azure	30-35%	- Describe core machine learning concepts - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML - Identify common machine learning tasks
Topic 3: Describe features of Natural Language Processing (NLP) workloads on Azure	15-20%	- Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks
Topic 4: Describe features of computer vision workloads on Azure	15-20%	- Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks
Topic 5: Describe AI workloads and considerations	15-20%	- Identify features of common AI workloads - Identify guiding principles for responsible AI

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Microsoft Azure AI Fundamentals Sample Questions (Q10-Q15):

NEW QUESTION # 10
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point

Answer:

Explanation:
Explanation
Graphical user interface, application Description automatically generated.

NEW QUESTION # 11
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

NEW QUESTION # 12
When training a model, why should you randomly split the rows into separate subsets?

A. to train multiple models simultaneously to attain better performance
B. to test the model by using data that was not used to train the model
C. to train the model twice to attain better accuracy

Answer: B

NEW QUESTION # 13
You need to predict the income range of a given customer by using the following dataset.

Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Age
B. First Name
C. Income Range
D. Education Level
E. Last Name

Answer: A,D

Explanation:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.

NEW QUESTION # 14
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?

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

Answer: B

Explanation:
Explanation
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview

NEW QUESTION # 15
......

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