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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Features of computer vision workloads on Azure15-20%- Computer vision solutions
  • 1. Object detection
    • 2. Image classification
      • 3. OCR and image analysis
        Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
        • 1. Supervised vs unsupervised learning
          • 2. Training and validation concepts
            Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
            • 1. Common AI workloads
              • 2. Responsible AI principles
                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

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                      Microsoft Azure AI Fundamentals Sample Questions (Q267-Q272):

                      NEW QUESTION # 267
                      For a machine learning progress, how should you split data for training and evaluation?

                      Answer: B

                      Explanation:
                      Section: Describe Artificial Intelligence workloads and considerations
                      Explanation:
                      In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data.
                      Reference:
                      https://www.sqlshack.com/prediction-in-azure-machine-learning/


                      NEW QUESTION # 268
                      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:
                      Explanation

                      Box 1: Model evaluation
                      The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
                      Box 2: Feature engineering
                      Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
                      Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
                      Box 3: Feature selection
                      In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
                      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 # 269
                      For a machine learning progress, how should you split data for training and evaluation?

                      Answer: A

                      Explanation:
                      https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data


                      NEW QUESTION # 270
                      Select the answer that correctly completes the sentence.

                      Answer:

                      Explanation:


                      NEW QUESTION # 271
                      You need to create a training dataset and validation dataset from an existing dataset.
                      Which module in the Azure Machine Learning designer should you use?

                      Answer: D

                      Explanation:
                      A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
                      Use the Split Data module to divide a dataset into two distinct sets.
                      The studio currently supports training/validation data splits
                      Reference:
                      https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2


                      NEW QUESTION # 272
                      ......

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