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Microsoft AI-900 Exam Syllabus Topics:

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

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                      Microsoft Azure AI Fundamentals Sample Questions (Q313-Q318):

                      NEW QUESTION # 313
                      Providing contextual information to improve the responses quality of a generative Al solution is an example of which prompt engineering technique?

                      Answer: A

                      Explanation:
                      In Microsoft Azure OpenAI Service and the AI-900/AI-102 study materials, grounding data is the correct term used to describe the process of providing contextual or external information to improve the accuracy, relevance, and quality of responses generated by a generative AI model such as GPT-3.5 or GPT-4.
                      Grounding is a prompt engineering technique where the AI model is supplemented with relevant background data, such as company documents, knowledge bases, or user context, that helps the model generate factually correct and context-aware responses. Microsoft Learn defines grounding as a way to connect the model's general knowledge to specific, real-world information. For example, if you ask a GPT-3.5 model about your organization's HR policies, the base model will not know them unless that policy information is provided (grounded) in the prompt. By embedding this contextual data, the AI becomes "grounded" in the facts it needs to respond reliably.
                      This technique differs from other prompt engineering concepts:
                      * A. Providing examples (few-shot prompting) shows the model sample inputs and outputs to guide formatting or style, not factual context.
                      * B. Fine-tuning involves retraining the model with labeled data to permanently adjust its behavior - it's not a prompt-based technique.
                      * D. System messages define the model's role, tone, or style (for example, "You are a helpful assistant") but do not add factual context.
                      Therefore, when you provide contextual information (like product details, policy documents, or reference text) within a prompt to enhance the quality and factual reliability of the model's responses, you are applying the grounding data technique.


                      NEW QUESTION # 314
                      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:


                      NEW QUESTION # 315
                      Match the types of machine learning to the appropriate scenarios.
                      To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
                      NOTE: Each correct selection is worth one point.

                      Answer:

                      Explanation:

                      Reference:
                      https://developers.google.com/machine-learning/practica/image-classification
                      https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
                      https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/


                      NEW QUESTION # 316
                      You plan to develop a bot that will enable users to query a knowledge base by using natural language processing.
                      Which two services should you include in the solution? Each correct answer presents part of the solution.
                      NOTE: Each correct selection is worth one point.

                      Answer: A,B

                      Explanation:
                      Reference:
                      https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
                      https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service


                      NEW QUESTION # 317
                      What is a use case for classification?

                      Answer: C


                      NEW QUESTION # 318
                      ......

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