{"id":16107,"date":"2026-09-18T11:39:35","date_gmt":"2026-09-18T11:39:35","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=16107"},"modified":"2026-09-18T11:39:35","modified_gmt":"2026-09-18T11:39:35","slug":"microsoft-ai-901-practice-test-questions-and-exam-dumps-part-1-q1-20","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-ai-901-practice-test-questions-and-exam-dumps-part-1-q1-20\/","title":{"rendered":"Microsoft AI-901 Practice Test Questions and Exam Dumps Part 1 Q1-20"},"content":{"rendered":"

<\/h1>\n

View Full <\/b>Microsoft AI-901 Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/p>\n

 <\/p>\n

Question 1. Which Azure service is designed to build, train, and deploy machine learning models?<\/b><\/h3>\n
    \n
  1. <\/b> Azure Machine Learning<\/span><\/li>\n
  2. <\/b> Azure Storage<\/span><\/li>\n
  3. <\/b> Azure DNS<\/span><\/li>\n
  4. <\/b> Azure Monitor<\/span><\/li>\n<\/ol>\n

    Correct Answer: 1. Azure Machine Learning<\/b><\/p>\n

    Explanation:<\/b><\/p>\n

    Azure Machine Learning is a cloud-based service designed to support the machine learning lifecycle. It provides capabilities for preparing data, developing and training models, evaluating results, registering models, and deploying models for inference. It can support both code-based and low-code development approaches and integrates with other Azure capabilities used in machine learning workflows. Azure Storage is primarily used for storing data, Azure DNS provides domain-name resolution, and Azure Monitor is used for monitoring applications and infrastructure. Therefore, when the requirement is to build, train, manage, and deploy machine learning models using Azure, Azure Machine Learning is the appropriate service.<\/span><\/p>\n

    Question 2. Which type of machine learning uses labeled data to train a model?<\/b><\/h3>\n
      \n
    1. <\/b> Unsupervised learning<\/span><\/li>\n
    2. <\/b> Supervised learning<\/span><\/li>\n
    3. <\/b> Reinforcement learning<\/span><\/li>\n
    4. <\/b> Generative learning<\/span><\/li>\n<\/ol>\n

      Correct Answer: 2. Supervised learning<\/b><\/p>\n

      Explanation:<\/b><\/p>\n

      Supervised learning trains a machine learning model using labeled training data. Each training example contains input features together with a known target or label. The model learns relationships between the inputs and expected outputs so it can make predictions on new, previously unseen data. Common supervised learning tasks include classification and regression. Unsupervised learning instead works with data without predefined labels, while reinforcement learning learns through interactions involving rewards and penalties. Generative AI refers to models that can create new content rather than defining a basic learning category equivalent to supervised learning. Therefore, labeled training data is a defining characteristic of supervised learning.<\/span><\/p>\n

      Question 3. Which machine learning task predicts a continuous numerical value?<\/b><\/h3>\n
        \n
      1. <\/b> Classification<\/span><\/li>\n
      2. <\/b> Clustering<\/span><\/li>\n
      3. <\/b> Regression<\/span><\/li>\n
      4. <\/b> Anomaly detection<\/span><\/li>\n<\/ol>\n

        Correct Answer: 3. Regression<\/b><\/p>\n

        Explanation:<\/b><\/p>\n

        Regression is a supervised machine learning task used to predict continuous numerical values. For example, a regression model could estimate a house price, predict monthly sales revenue, or forecast a temperature value based on historical and relevant input features. The model learns from examples where the target is a numerical value and then produces a predicted numerical output for new observations. Classification is used to predict categories or classes, while clustering groups similar observations without requiring predefined labels. Anomaly detection focuses on identifying unusual patterns. Therefore, when the target value is continuous and numerical, regression is the appropriate machine learning task.<\/span><\/p>\n

        Question 4. Which machine learning task predicts a category such as spam or not spam?<\/b><\/h3>\n
          \n
        1. <\/b> Regression<\/span><\/li>\n
        2. <\/b> Classification<\/span><\/li>\n
        3. <\/b> Clustering<\/span><\/li>\n
        4. <\/b> Dimensionality reduction<\/span><\/li>\n<\/ol>\n

          Correct Answer: 2. Classification<\/b><\/p>\n

          Explanation:<\/b><\/p>\n

          Classification is a supervised machine learning task used when a model needs to predict a discrete category or class. For example, an email filtering system might classify messages as spam or not spam, while another model could classify an image according to a known category. During training, the model receives examples containing features and corresponding labels. It learns patterns that help it assign appropriate categories to new observations. Regression is used for continuous numerical predictions, clustering groups unlabeled data based on similarity, and dimensionality reduction transforms data into fewer dimensions. Therefore, classification is the correct approach when the expected prediction is a category such as spam or not spam.<\/span><\/p>\n

          Question 5. Which machine learning approach groups unlabeled data based on similarities?<\/b><\/h3>\n
            \n
          1. <\/b> Clustering<\/span><\/li>\n
          2. <\/b> Classification<\/span><\/li>\n
          3. <\/b> Regression<\/span><\/li>\n
          4. <\/b> Supervised learning<\/span><\/li>\n<\/ol>\n

            Correct Answer: 1. Clustering<\/b><\/p>\n

            Explanation:<\/b><\/p>\n

            Clustering is an unsupervised machine learning technique that organizes data points into groups based on similarities in their characteristics. The training data does not require predefined labels indicating the correct group for each observation. Instead, the algorithm identifies patterns or structures within the data and assigns similar observations to clusters. A business might use clustering to group customers according to purchasing behavior or identify natural segments in a dataset. Classification requires labeled examples and predicts predefined categories, while regression predicts numerical values. Therefore, clustering is the appropriate machine learning approach when the goal is to discover groups in unlabeled data.<\/span><\/p>\n

            Question 6. What is the primary purpose of a validation dataset during model development?<\/b><\/h3>\n
              \n
            1. <\/b> To permanently store production data<\/span><\/li>\n
            2. <\/b> To replace the training dataset<\/span><\/li>\n
            3. <\/b> To evaluate and tune the model during development<\/span><\/li>\n
            4. <\/b> To encrypt the model<\/span><\/li>\n<\/ol>\n

              Correct Answer: 3. To evaluate and tune the model during development<\/b><\/p>\n

              Explanation:<\/b><\/p>\n

              A validation dataset is used during model development to evaluate how well a model performs on data that was not directly used to fit its parameters. Developers can use validation results to compare models, select hyperparameters, and identify potential overfitting before final evaluation. This helps guide the development process while keeping a separate test dataset available for a more independent assessment of the final model. The validation dataset is not intended to permanently store production information or replace the training data. Therefore, its primary purpose is to evaluate and tune the model during development.<\/span><\/p>\n

              Question 7. Which metric is commonly used to measure the proportion of correct predictions in a classification model?<\/b><\/h3>\n
                \n
              1. <\/b> Mean squared error<\/span><\/li>\n
              2. <\/b> Accuracy<\/span><\/li>\n
              3. <\/b> Root mean squared error<\/span><\/li>\n
              4. <\/b> Mean absolute error<\/span><\/li>\n<\/ol>\n

                Correct Answer: 2. Accuracy<\/b><\/p>\n

                Explanation:<\/b><\/p>\n

                Accuracy measures the proportion of predictions that a classification model gets correct out of all evaluated predictions. For example, if a model correctly classifies 90 of 100 observations, its accuracy is 90 percent. Accuracy can be useful when the classes are reasonably balanced and the costs of different types of errors are similar. However, it may not provide a complete picture for highly imbalanced datasets, where precision, recall, F1 score, or other metrics may be more informative. Mean squared error, root mean squared error, and mean absolute error are commonly associated with regression evaluation. Therefore, accuracy is the metric described.<\/span><\/p>\n

                Question 8. Which Azure capability provides a visual, low-code interface for developing machine learning models?<\/b><\/h3>\n
                  \n
                1. <\/b> Azure Machine Learning designer<\/span><\/li>\n
                2. <\/b> Azure Blob Storage<\/span><\/li>\n
                3. <\/b> Azure Key Vault<\/span><\/li>\n
                4. <\/b> Azure Virtual Network<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 1. Azure Machine Learning designer<\/b><\/p>\n

                  Explanation:<\/b><\/p>\n

                  Azure Machine Learning designer provides a visual interface that allows users to create machine learning pipelines by connecting modules in a graphical environment. This approach can reduce the amount of code required for common machine learning workflows and is useful for users who prefer a visual development experience. Data preparation, model training, evaluation, and related pipeline activities can be represented through connected components. Azure Blob Storage provides object storage, Azure Key Vault is designed for securely storing secrets and keys, and Azure Virtual Network provides network isolation. Therefore, Azure Machine Learning designer is the capability specifically intended for visual, low-code machine learning development.<\/span><\/p>\n

                  Question 9. What does overfitting occur when a machine learning model does?<\/b><\/h3>\n
                    \n
                  1. <\/b> Performs poorly on training data but well on new data<\/span><\/li>\n
                  2. <\/b> Uses no training data<\/span><\/li>\n
                  3. <\/b> Learns the training data too closely and performs poorly on unseen data<\/span><\/li>\n
                  4. <\/b> Always produces identical predictions<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 3. Learns the training data too closely and performs poorly on unseen data<\/b><\/p>\n

                    Explanation:<\/b><\/p>\n

                    Overfitting occurs when a machine learning model learns the training dataset too closely, including patterns that do not generalize well to new observations. An overfitted model can achieve very strong performance on its training data while producing significantly weaker results when evaluated against unseen data. This can happen when a model is excessively complex relative to the amount or quality of available training data. Techniques such as regularization, cross-validation, early stopping, simplifying the model, or obtaining more representative training data can help reduce overfitting. Therefore, learning training-specific patterns while performing poorly on unseen data is a key characteristic of overfitting.<\/span><\/p>\n

                    Question 10. Which Azure service provides cloud storage commonly used to store datasets for machine learning workflows?<\/b><\/h3>\n
                      \n
                    1. <\/b> Azure Blob Storage<\/span><\/li>\n
                    2. <\/b> Azure DNS<\/span><\/li>\n
                    3. <\/b> Azure Firewall<\/span><\/li>\n
                    4. <\/b> Azure Advisor<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 1. Azure Blob Storage<\/b><\/p>\n

                      Explanation:<\/b><\/p>\n

                      Azure Blob Storage is an object storage service that can store large amounts of unstructured data, including files and datasets used in machine learning workflows. Training data, validation data, images, documents, and other files can be stored in blob containers and accessed by supported Azure services and applications. Blob Storage is designed for scalable cloud data storage rather than domain-name resolution, network security, or general recommendation management. Azure DNS provides DNS services, Azure Firewall provides network security capabilities, and Azure Advisor offers recommendations related to Azure resources. Therefore, Azure Blob Storage is the appropriate service for storing datasets and other files used in machine learning solutions.<\/span><\/p>\n

                      Question 11. Which term describes the input variables used by a machine learning model?<\/b><\/h3>\n
                        \n
                      1. <\/b> Labels<\/span><\/li>\n
                      2. <\/b> Features<\/span><\/li>\n
                      3. <\/b> Predictions<\/span><\/li>\n
                      4. <\/b> Scores<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 2. Features<\/b><\/p>\n

                        Explanation:<\/b><\/p>\n

                        Features are the input variables or characteristics provided to a machine learning model so it can learn patterns and make predictions. For example, a house-price model might use features such as location, number of bedrooms, property size, and age. During supervised learning, a dataset commonly contains features along with a target label that the model is trained to predict. Predictions are outputs generated by the model, while scores can refer to various evaluation or confidence values depending on context. Therefore, the variables supplied as inputs to a machine learning model are commonly called features.<\/span><\/p>\n

                        Question 12. Which term describes the value a supervised learning model is trained to predict?<\/b><\/h3>\n
                          \n
                        1. <\/b> Feature<\/span><\/li>\n
                        2. <\/b> Dataset<\/span><\/li>\n
                        3. <\/b> Label<\/span><\/li>\n
                        4. <\/b> Hyperparameter<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 3. Label<\/b><\/p>\n

                          Explanation:<\/b><\/p>\n

                          In supervised machine learning, a label represents the known target value that the model is trained to predict. For a classification problem, the label might be a category such as fraudulent or legitimate. For a regression problem, the label could be a continuous numerical value such as a predicted price. Features provide the input information used to make the prediction, while hyperparameters are configuration values selected during model development rather than target values learned from individual training examples. Therefore, the known target value associated with a training example is commonly called the label.<\/span><\/p>\n

                          Question 13. Which machine learning technique is appropriate for identifying unusual transactions in financial data?<\/b><\/h3>\n
                            \n
                          1. <\/b> Anomaly detection<\/span><\/li>\n
                          2. <\/b> Regression only<\/span><\/li>\n
                          3. <\/b> Translation<\/span><\/li>\n
                          4. <\/b> Image classification<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 1. Anomaly detection<\/b><\/p>\n

                            Explanation:<\/b><\/p>\n

                            Anomaly detection is designed to identify observations or behaviors that differ significantly from expected patterns. In financial systems, it can be used to identify potentially unusual transactions based on characteristics such as transaction amount, location, timing, frequency, or historical behavior. The technique can be useful when examples of every possible abnormal event are difficult to label in advance. Regression focuses on predicting numerical values, translation converts content between languages, and image classification assigns categories to images. Anomaly detection does not automatically determine that every unusual event is fraudulent; rather, it identifies patterns that may warrant further investigation. Therefore, it is appropriate for identifying unusual transactions.<\/span><\/p>\n

                            Question 14. Which Azure Machine Learning capability tracks different versions of registered models?<\/b><\/h3>\n
                              \n
                            1. <\/b> Model registry<\/span><\/li>\n
                            2. <\/b> Azure DNS<\/span><\/li>\n
                            3. <\/b> Network Security Group<\/span><\/li>\n
                            4. <\/b> Azure Bastion<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 1. Model registry<\/b><\/p>\n

                              Explanation:<\/b><\/p>\n

                              The Azure Machine Learning model registry provides a centralized location for managing machine learning models and their versions. Registering models allows teams to organize model artifacts and maintain version information as models evolve through development and deployment processes. Versioning is important because a production application may need to identify exactly which model version is being used and may need to roll back or compare versions when necessary. Azure DNS handles domain-name resolution, Network Security Groups control network traffic, and Azure Bastion provides secure access to virtual machines. Therefore, the model registry is the Azure Machine Learning capability associated with registering and managing model versions.<\/span><\/p>\n

                              Question 15. Which responsible AI principle focuses on ensuring that AI systems treat people fairly?<\/b><\/h3>\n
                                \n
                              1. <\/b> Reliability<\/span><\/li>\n
                              2. <\/b> Transparency<\/span><\/li>\n
                              3. <\/b> Fairness<\/span><\/li>\n
                              4. <\/b> Accountability<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 3. Fairness<\/b><\/p>\n

                                Explanation:<\/b><\/p>\n

                                Fairness is a core responsible AI principle concerned with ensuring that AI systems do not create unjustified or inappropriate differences in treatment between people or groups. When developing an AI solution, organizations should consider whether training data, model design, and decision processes can introduce or amplify unwanted bias. Fairness does not necessarily mean that every individual receives identical outcomes; the appropriate assessment depends on the scenario, context, and intended use of the system. Transparency focuses on making system behavior understandable, while accountability addresses responsibility for AI outcomes. Therefore, the responsible AI principle specifically concerned with equitable treatment is fairness.<\/span><\/p>\n

                                Question 16. Which responsible AI principle requires users to understand how an AI system reaches or supports its results?<\/b><\/h3>\n
                                  \n
                                1. <\/b> Transparency<\/span><\/li>\n
                                2. <\/b> Fairness<\/span><\/li>\n
                                3. <\/b> Reliability<\/span><\/li>\n
                                4. <\/b> Inclusiveness<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 1. Transparency<\/b><\/p>\n

                                  Explanation:<\/b><\/p>\n

                                  Transparency involves providing understandable information about how an AI system operates, what data or factors may influence its results, and how users should interpret its outputs. Transparent AI systems can help users understand the system’s purpose, limitations, and decision-support role. This is especially important when AI outputs influence significant business or personal decisions. Fairness addresses equitable treatment, reliability and safety concern dependable operation, and inclusiveness focuses on designing systems that can serve people with diverse needs and abilities. Therefore, when the requirement is for users to understand how an AI system produces or supports its results, transparency is the relevant responsible AI principle.<\/span><\/p>\n

                                  Question 17. Which Azure service can provide a managed endpoint for real-time machine learning model inference?<\/b><\/h3>\n
                                    \n
                                  1. <\/b> Azure Machine Learning<\/span><\/li>\n
                                  2. <\/b> Azure DNS<\/span><\/li>\n
                                  3. <\/b> Azure Storage<\/span><\/li>\n
                                  4. <\/b> Azure Resource Graph<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 1. Azure Machine Learning<\/b><\/p>\n

                                    Explanation:<\/b><\/p>\n

                                    Azure Machine Learning provides managed deployment capabilities for machine learning models, including endpoints that can be used for real-time inference. After a model has been trained and prepared for deployment, it can be deployed to an appropriate managed endpoint so applications can send input data and receive predictions. This separates model development and training from the application layer that consumes predictions. Azure DNS manages domain-name resolution, Azure Storage stores data, and Azure Resource Graph is designed for querying information about Azure resources. Therefore, Azure Machine Learning is the Azure service appropriate for deploying managed real-time inference endpoints.<\/span><\/p>\n

                                    Question 18. Which type of AI model is primarily designed to generate new text, images, audio, or other content?<\/b><\/h3>\n
                                      \n
                                    1. <\/b> Classification model<\/span><\/li>\n
                                    2. <\/b> Generative AI model<\/span><\/li>\n
                                    3. <\/b> Regression model<\/span><\/li>\n
                                    4. <\/b> Clustering model<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 2. Generative AI model<\/b><\/p>\n

                                      Explanation:<\/b><\/p>\n

                                      Generative AI models are designed to create new content based on patterns learned from data. Depending on the model and service, generated content can include text, images, audio, video, code, or other forms of data. Large language models are a common example for generating and transforming text. Classification models primarily assign categories, regression models predict numerical values, and clustering methods group similar observations. Generative AI can also be used in applications that combine content generation with other AI capabilities, such as summarization or conversational systems. Therefore, a model designed primarily to create new content is referred to as a generative AI model.<\/span><\/p>\n

                                      Question 19. Which Microsoft Azure service provides access to foundation models through a managed AI platform?<\/b><\/h3>\n
                                        \n
                                      1. <\/b> Azure AI Foundry<\/span><\/li>\n
                                      2. <\/b> Azure DNS<\/span><\/li>\n
                                      3. <\/b> Azure Load Balancer<\/span><\/li>\n
                                      4. <\/b> Azure Files<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 1. Azure AI Foundry<\/b><\/p>\n

                                        Explanation:<\/b><\/p>\n

                                        Azure AI Foundry provides a platform for developing and managing generative AI and other AI applications using supported models and Azure capabilities. It is designed to help developers and organizations work with models, evaluate applications, connect data and tools, and build AI solutions within the Azure ecosystem. Foundation models can provide general capabilities that applications can adapt or use for tasks such as content generation, reasoning, summarization, and other scenarios depending on the model. Azure DNS, Azure Load Balancer, and Azure Files serve networking, traffic distribution, and storage purposes respectively. Therefore, Azure AI Foundry is the relevant Azure platform for working with foundation models and AI applications.<\/span><\/p>\n

                                        Question 20. Which Azure AI capability can analyze images and extract information such as objects or captions?<\/b><\/h3>\n
                                          \n
                                        1. <\/b> Azure AI Vision<\/span><\/li>\n
                                        2. <\/b> Azure AI Speech<\/span><\/li>\n
                                        3. <\/b> Azure AI Language<\/span><\/li>\n
                                        4. <\/b> Azure AI Search<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 1. Azure AI Vision<\/b><\/p>\n

                                          Explanation:<\/b><\/p>\n

                                          Azure AI Vision provides computer vision capabilities that can analyze visual content and extract useful information from images. Depending on the supported feature and configuration, vision capabilities can identify objects, generate descriptions or captions, analyze image characteristics, and perform other visual-processing tasks. Azure AI Speech focuses on speech recognition, speech synthesis, and related audio scenarios. Azure AI Language provides capabilities for processing and understanding text, while Azure AI Search supports search and retrieval scenarios. Therefore, when an application needs to analyze images and extract visual information such as objects or captions, Azure AI Vision is the appropriate Azure AI capability.<\/span><\/p>\n

                                           <\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Microsoft AI-901 Exam Dumps and Practice Test Dumps   Question 1. Which Azure service is designed to build, train, and deploy machine learning models? Azure Machine Learning Azure Storage Azure DNS Azure Monitor Correct Answer: 1. Azure Machine Learning Explanation: Azure Machine Learning is a cloud-based service designed to support the machine learning […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16107"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=16107"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16107\/revisions"}],"predecessor-version":[{"id":16146,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16107\/revisions\/16146"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=16107"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=16107"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=16107"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}