100% Pass Quiz Microsoft - The Best AI-900 - Exam Microsoft Azure AI Fundamentals Assessment

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Microsoft AI-900 Exam Syllabus Topics:

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

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                      AI-900 Sample Exam & Valid AI-900 Exam Syllabus

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                      Microsoft Azure AI Fundamentals Sample Questions (Q220-Q225):

                      NEW QUESTION # 220
                      Select the answer that correctly completes the sentence.

                      Answer:

                      Explanation:

                      Explanation:


                      NEW QUESTION # 221
                      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:

                      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 # 222
                      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 # 223
                      Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

                      Answer: C

                      Explanation:
                      Section: Describe fundamental principles of machine learning on Azure
                      Explanation:
                      In the most basic sense, regression refers to prediction of a numeric target.
                      Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
                      You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
                      Reference:
                      https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression


                      NEW QUESTION # 224
                      Match the types of AI workloads to the appropriate scenarios.
                      To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
                      NOTE: Each correct selection is worth one point.

                      Answer:

                      Explanation:

                      Explanation:

                      Box 3: Natural language processing
                      Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
                      Reference:
                      https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing


                      NEW QUESTION # 225
                      ......

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