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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
        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 computer vision workloads on Azure15-20%- Computer vision solutions
            • 1. OCR and image analysis
              • 2. Image classification
                • 3. Object detection
                  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 (Q321-Q326):

                      NEW QUESTION # 321
                      What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution.
                      NOTE: Each correct selection is worth one point.

                      Answer: C,D

                      Explanation:
                      B: Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
                      C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.
                      Reference:
                      https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home


                      NEW QUESTION # 322
                      You plan to apply Text Analytics API features to a technical support ticketing system.
                      Match the Text Analytics API features to the appropriate natural language processing scenarios.
                      To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
                      NOTE: Each correct selection is worth one point.

                      Answer:

                      Explanation:

                      Explanation:
                      Box1: Sentiment analysis
                      Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
                      Box 2: Broad entity extraction
                      Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
                      Box 3: Entity Recognition
                      Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
                      Reference:
                      https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
                      https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics


                      NEW QUESTION # 323
                      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:

                      Reference:
                      https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview


                      NEW QUESTION # 324
                      What can be used to complete a paragraph based on a sentence provided by a user?

                      Answer: B


                      NEW QUESTION # 325
                      What should you do to ensure that an Azure OpenAI model generates accurate responses that include recent events?

                      Answer: B

                      Explanation:
                      In Azure OpenAI, grounding refers to the process of connecting the model to external data sources (for example, a database, search index, or API) so that it can retrieve accurate and up-to-date information before generating a response. This is particularly important for scenarios requiring current facts or events, since OpenAI models like GPT-3.5 and GPT-4 are trained on data available only up to a certain cutoff date.
                      By adding grounding data, the model's responses are "anchored" to factual sources retrieved at runtime, improving reliability and factual accuracy. Grounding is commonly implemented in Azure OpenAI + Azure Cognitive Search solutions (Retrieval-Augmented Generation or RAG).
                      Option review:
                      * A. Modify the system message: Changes model tone or behavior but doesn't supply real-time data.
                      * B. Add grounding data: # Correct - allows access to recent and domain-specific information.
                      * C. Add few-shot learning: Provides examples in the prompt to improve context understanding but not factual accuracy.
                      * D. Add training data: Refers to fine-tuning; this requires retraining and doesn't update the model's awareness of current events.
                      Hence, the best method to ensure accurate and current responses from an Azure OpenAI model is to add grounding data, enabling the model to reference real, updated sources dynamically.


                      NEW QUESTION # 326
                      ......

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