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

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                      Microsoft Azure AI Fundamentals Sample Questions (Q201-Q206):

                      NEW QUESTION # 201
                      To complete the sentence, select the appropriate option in the answer area.

                      Answer:

                      Explanation:

                      Explanation
                      Graphical user interface, table Description automatically generated

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


                      NEW QUESTION # 202
                      Match the Azure Cognitive Services service to the appropriate actions.
                      To answer, drag the appropriate service from the column on the left to its action on the right. Each service may he used once, more than once, or not at all.
                      NOTE: Each correct match is worth one point.

                      Answer:

                      Explanation:

                      Explanation:


                      NEW QUESTION # 203
                      Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
                      NOTE: Each correct selection is worth one point.

                      Answer: B,C

                      Explanation:
                      According to the AI-900 official study guide and Microsoft Learn module "Describe features of conversational AI workloads on Azure", conversational AI refers to artificial intelligence systems that interact with users through natural language via text or speech. These systems include chatbots, virtual assistants, and interactive voice response (IVR) systems that simulate human conversation.
                      * B. Chatbot that provides users with the ability to find answers on a website by themselvesThis is a classic example of conversational AI. Chatbots use natural language understanding (LUIS) and Azure Bot Service to interpret user input, identify intent, and provide relevant responses automatically. They help users self-serve information without human support, such as retrieving account details or answering FAQs.
                      * C. Telephone voice menus to reduce the load on human resourcesAutomated telephone systems or IVRs use conversational AI to interpret spoken commands and route calls intelligently. This is often implemented using Azure Cognitive Services Speech (for speech-to-text and text-to-speech) combined with Azure Bot Service for managing dialogue flow.


                      NEW QUESTION # 204
                      Match the Azure Cognitive Services to the appropriate Al workloads.
                      To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.
                      NOTE: Each correct match is worth one point.

                      Answer:

                      Explanation:


                      NEW QUESTION # 205
                      What is a use case for classification?

                      Answer: C

                      Explanation:
                      Explanation
                      Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
                      Reference:
                      https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
                      https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-m


                      NEW QUESTION # 206
                      ......

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