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The AI-900 Certification Exam is primarily designed for business stakeholders and technical professionals who want to gain a fundamental understanding of AI technologies and their potential applications in Microsoft Azure services. Microsoft Azure AI Fundamentals certification is an entry-level exam that validates the candidate's knowledge of AI concepts and Azure AI services, including Cognitive Services, Machine Learning, and Bot Services. AI-900 exam also covers the ethical and responsible use of AI, including privacy, security, and compliance considerations.

Microsoft AI-900 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Describe features of computer vision workloads on Azure: This topic discusses identifying common types of computer vision solution, Azure tools and services for computer vision tasks.
Topic 2
  • Describe features of Natural Language Processing (NLP) workloads on Azure: In this topic, questions about common NLP Workload Scenarios features, Azure tools, and services for NLP workloads appear.
Topic 3
  • Describe features of generative AI workloads on Azure: Features of generative AI solutions and capabilities of Azure OpenAI Service are discussed in this topic.
Topic 4
  • Describe fundamental principles of machine learning on Azure: It describes core machine learning concepts and Azure Machine Learning capabilities. Moreover, the topic also delves into identifying common machine-learning techniques.
Topic 5
  • Describe Artificial Intelligence workloads and considerations: It identifies specifications of common AI workloads and guiding principles for responsible AI.

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Microsoft Azure AI Fundamentals Sample Questions (Q95-Q100):

NEW QUESTION # 95
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer


NEW QUESTION # 96
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: Yes
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance


NEW QUESTION # 97
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 # 98
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection


NEW QUESTION # 99
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance


NEW QUESTION # 100
......

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