Prepare with Microsoft AI-900 PDF Questions [2026]-Best Preparation Materials

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Microsoft AI-900 Exam Syllabus Topics:

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

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                      Microsoft Azure AI Fundamentals Sample Questions (Q176-Q181):

                      NEW QUESTION # 176
                      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 # 177
                      You have insurance claim reports that are stored as text.
                      You need to extract key terms from the reports to generate summaries.
                      Which type of Al workload should you use?

                      Answer: D

                      Explanation:
                      Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s).
                      Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.
                      https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure


                      NEW QUESTION # 178
                      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 the Microsoft Learn module "Explore fundamental principles of machine learning", feature engineering is the process used to generate additional features or transform existing data into forms that improve model performance. Features are individual measurable properties or characteristics used as input for machine learning algorithms. The goal of feature engineering is to create new informative variables that better represent the underlying patterns in the data.
                      Feature engineering may include tasks such as:
                      * Combining or transforming raw data columns (e.g., creating a "total purchase amount" from price ร— quantity).
                      * Extracting time-based components (e.g., year, month, day, hour) from datetime values.
                      * Encoding categorical variables (e.g., one-hot encoding or label encoding).
                      * Scaling or normalizing numerical features.
                      * Creating polynomial or interaction terms to capture complex relationships.
                      Microsoft's AI-900 learning material emphasizes that the process of preparing data for machine learning involves data cleaning, feature engineering, and feature selection. While feature selection is about choosing the most relevant features from the existing dataset, feature engineering focuses on creating or generating new features to enhance model accuracy and generalization.
                      The other options do not fit this definition:
                      * Feature selection is about removing redundant or irrelevant features, not generating new ones.
                      * Model evaluation involves assessing the model's performance using metrics like accuracy or F1 score.
                      * Model training is the phase where the algorithm learns patterns from the data, not when features are created.
                      Therefore, based on the AI-900 official concepts and Microsoft's documentation, the correct answer is Feature engineering, as it is the process specifically used to generate additional features that improve machine learning model performance and predictive capability.


                      NEW QUESTION # 179
                      Select the answer that correctly completes the sentence.

                      Answer:

                      Explanation:

                      Explanation:


                      NEW QUESTION # 180
                      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


                      NEW QUESTION # 181
                      ......

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