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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
  • 1. Training and validation concepts
    • 2. Supervised vs unsupervised learning
      Topic 2: Features of computer vision workloads on Azure15-20%- Computer vision solutions
      • 1. OCR and image analysis
        • 2. Image classification
          • 3. Object detection
            Topic 3: Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
            • 1. Common AI workloads
              • 2. Responsible AI principles
                Topic 4: Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
                • 1. Key phrase extraction
                  • 2. Language modeling and translation
                    • 3. Sentiment analysis

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                      Microsoft Azure AI Fundamentals Sample Questions (Q63-Q68):

                      NEW QUESTION # 63
                      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://machinelearningmastery.com/difference-test-validation-datasets/


                      NEW QUESTION # 64
                      Select the answer that correctly completes the sentence.

                      Answer:

                      Explanation:


                      NEW QUESTION # 65
                      brectly completes the sentence.

                      Answer:

                      Explanation:


                      NEW QUESTION # 66
                      You build a QnA Maker bot by using a frequently asked questions (FAQ) page.
                      You need to add professional greetings and other responses to make the bot more user friendly.
                      What should you do?

                      Answer: A

                      Explanation:
                      Reference:
                      https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/chit-chat-knowledge-base?tabs=v1


                      NEW QUESTION # 67
                      When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.
                      This is an example of which Microsoft guiding principle for responsible AI?

                      Answer: B

                      Explanation:
                      Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
                      Reference:
                      https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
                      https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai


                      NEW QUESTION # 68
                      ......

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