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

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                      Microsoft Azure AI Fundamentals Sample Questions (Q315-Q320):

                      NEW QUESTION # 315
                      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:

                      Reference:
                      https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/


                      NEW QUESTION # 316
                      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 # 317
                      Select the answer that correctly completes the sentence

                      Answer:

                      Explanation:

                      Explanation:

                      According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft's Responsible AI Framework, the Reliability and Safety principle ensures that AI systems operate consistently, accurately, and as intended, even when confronted with unexpected data or edge cases. It emphasizes that AI systems must be tested, validated, and monitored to ensure stable performance and to prevent harm caused by inaccurate or unreliable outputs.
                      In the given scenario, the AI system is designed not to provide predictions when key fields contain unusual or missing values. This approach demonstrates that the system is built to avoid unreliable or unsafe outputs that could result from incomplete or corrupted data. Microsoft explicitly outlines that reliable AI systems must handle data anomalies and input validation properly to prevent incorrect predictions.
                      Here's how the other options differ:
                      * Inclusiveness ensures accessibility for all users, including those with disabilities or from different backgrounds. It's unrelated to prediction control or data reliability.
                      * Privacy and Security protects sensitive data and ensures proper handling of personal information, not system prediction logic.
                      * Transparency ensures that users understand how an AI system makes its decisions but doesn't address prediction reliability.
                      Thus, stopping a prediction when data is incomplete or abnormal directly supports the Reliability and Safety principle - it ensures that the AI model functions correctly under valid conditions and avoids unintended or harmful outcomes.
                      This principle aligns with Microsoft's Responsible AI guidance, which highlights that AI solutions must
                      "operate reliably and safely, even under unexpected conditions, to protect users and maintain trust."


                      NEW QUESTION # 318
                      You have the process shown in the following exhibit.

                      Which type AI solution is shown in the diagram?

                      Answer: A


                      NEW QUESTION # 319
                      Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.
                      NOTE; Each correct selection is worth one point.

                      Answer: B,D

                      Explanation:
                      According to the Microsoft Learn module "Describe features of Azure Machine Learning" and the AI-900 study guide, Azure Machine Learning designer supports extending workflows through custom code modules written in Python and R.
                      * Python is the most commonly used language for AI and machine learning due to its extensive library support (such as TensorFlow, Scikit-learn, and PyTorch).
                      * R is widely used for statistical computing and data visualization, making it valuable for analytical workloads.
                      In Azure Machine Learning, users can insert Python Script or Execute R Script modules within the visual designer to perform advanced operations or custom data transformations.
                      C# and Scala are not supported directly in Azure Machine Learning Designer. C# is more common in application development, and Scala is primarily used in big data frameworks like Apache Spark.
                      Hence, the correct answers are C. Python and D. R.


                      NEW QUESTION # 320
                      ......

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