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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. 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. Language modeling and translation
                  • 2. Key phrase extraction
                    • 3. Sentiment analysis

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                      Microsoft Azure AI Fundamentals Sample Questions (Q307-Q312):

                      NEW QUESTION # 307
                      To complete the sentence, select the appropriate option in the answer area.

                      Answer:

                      Explanation:

                      Explanation:

                      According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of regression machine learning", regression is a type of supervised machine learning used when the target variable (the value you want to predict) is a continuous numeric value.
                      In this scenario, the task is to predict how many hours of overtime a delivery person will work based on the number of orders received. Both the input (number of orders) and the output (hours of overtime) are numeric variables. Since the goal is to estimate a quantitative value rather than categorize or group data, this is a classic example of a regression problem.
                      Regression models analyze the relationship between variables to make numerical predictions. For example, the model might learn that each additional 20 orders increases overtime by about two hours. Common algorithms used for regression include linear regression, decision tree regression, and boosted regression models. These models produce outputs such as "expected overtime = 5.6 hours," which are continuous numeric results.
                      To contrast with the other options:
                      * Classification is used for predicting categories or labels, such as "overtime required" vs. "no overtime," or "high-risk" vs. "low-risk." It deals with discrete outputs rather than continuous numbers.
                      * Clustering is an unsupervised learning approach used to group similar data points based on shared characteristics, such as grouping delivery staff by performance patterns or customer types.
                      As emphasized in Microsoft's Responsible AI and Machine Learning Fundamentals learning paths, regression models are ideal for numeric forecasting problems such as predicting sales, revenue, demand, or working hours.
                      Therefore, the correct answer is: Regression.
                      Reference:Microsoft Learn - Identify the types of machine learning models: Regression, Classification, and Clustering (AI-900 Learning Path)


                      NEW QUESTION # 308
                      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:
                      Graphical user interface, text, application Description automatically generated

                      Reference:
                      https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot
                      https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-webchat-overview?view=azure-bot-service-4.0


                      NEW QUESTION # 309
                      To complete the sentence, select the appropriate option in the answer area.

                      Answer:

                      Explanation:


                      NEW QUESTION # 310
                      Match the machine learning tasks to the appropriate scenarios.
                      To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
                      NOTE: Each correct selection is worth one point.

                      Answer:

                      Explanation:

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


                      NEW QUESTION # 311
                      To complete the sentence, select the appropriate option in the answer area.

                      Answer:

                      Explanation:

                      Reference:
                      https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai


                      NEW QUESTION # 312
                      ......

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