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Microsoft Azure AI Fundamentals (AI-900) practice questions

200 free questions with answers and explanations.

Practice test
  1. 51.A retail company wants to analyze customer purchasing behavior to identify distinct groups of customers based on their transaction history, demographics, and browsing patterns. They do not have pre-defined labels for these customer groups. The goal is to discover natural groupings within the data. Which type of machine learning should be used?Describe fundamental principles of machine learning on Azure
  2. 52.A data scientist is training a deep learning model for image recognition. During training, the model's accuracy on the training data consistently improves, reaching nearly 100%, but its accuracy on a separate validation dataset starts to decrease after a certain number of epochs. What is the most likely issue occurring?Describe fundamental principles of machine learning on Azure
  3. 53.A machine learning engineer is developing a model to predict the future sales of a product based on historical sales data, promotional activities, and economic indicators. The model needs to output a specific numerical value representing the predicted sales quantity. Which type of machine learning problem is this?Describe fundamental principles of machine learning on Azure
  4. 54.A data scientist is building a machine learning model to predict the price of used cars based on features like mileage, age, brand, and engine size. What type of machine learning task is this?Describe fundamental principles of machine learning on Azure
  5. 55.A team of data scientists is building a recommendation system for an e-commerce platform. The system needs to suggest products to users based on their past purchase history and browsing behavior, as well as the behavior of similar users. This type of recommendation system, which analyzes relationships between users and items, is commonly known as what?Describe fundamental principles of machine learning on Azure
  6. 56.A team of data scientists is building a recommendation system for an e-commerce platform. The system needs to suggest products to users based on their past interactions (e.g., purchases, views) and the interactions of similar users. The goal is to provide personalized recommendations without relying on explicit product descriptions or metadata, focusing purely on user-item interaction patterns. Which common machine learning type is best suited for this approach?Describe fundamental principles of machine learning on Azure
  7. 57.A data engineering team is tasked with preparing a large dataset for a machine learning model. The dataset contains several features with widely different ranges, such as 'Age' (0-100) and 'Income' (10,000-1,000,000). Many machine learning algorithms, especially those based on distance calculations like K-Nearest Neighbors or Support Vector Machines, perform poorly with such disparities. Which preprocessing technique should be applied to address this issue?Describe fundamental principles of machine learning on Azure
  8. 58.A healthcare provider is using a machine learning model to predict the risk of a patient developing a certain disease. The model was trained on a dataset where 95% of patients are healthy and only 5% develop the disease. If the model achieves 95% accuracy by simply predicting 'healthy' for every patient, what concept does this scenario highlight as a potential issue?Describe fundamental principles of machine learning on Azure
  9. 59.A retail company wants to analyze customer purchasing behavior to identify distinct groups of customers with similar buying patterns, without any prior knowledge of what these groups might be. They aim to tailor marketing strategies to these identified segments. Which type of machine learning would be most suitable for this task?Describe fundamental principles of machine learning on Azure
  10. 60.A retail company wants to analyze customer purchasing behavior to identify distinct groups of customers for targeted marketing campaigns. They have a large dataset of past transactions but no predefined labels for customer segments. Which machine learning approach is most suitable for this task?Describe fundamental principles of machine learning on Azure
  11. 61.A data scientist is training a machine learning model. During the training process, the model's performance on the training dataset continuously improves, achieving near-perfect accuracy. However, when evaluated on a separate, unseen validation dataset, the model's performance is significantly worse. This indicates a common problem in machine learning. What is this problem called?Describe fundamental principles of machine learning on Azure
  12. 62.A data scientist is preparing a dataset for a regression model that predicts house prices. One of the features, 'Area in SqFt', ranges from 500 to 10,000, while another feature, 'Number of Bedrooms', ranges from 1 to 8. The model is sensitive to the scale of input features. To ensure that both features contribute equally to the model's learning process and prevent features with larger values from dominating, which data preprocessing technique should be applied?Describe fundamental principles of machine learning on Azure
  13. 63.A hospital is developing an AI system to assist radiologists in detecting subtle abnormalities in medical images, such as tumors. The system needs to not only identify the presence of an abnormality but also precisely outline its boundaries within the image. Which computer vision technique is best suited for this task?Describe fundamental principles of machine learning on Azure
  14. 64.A data scientist is building a machine learning model to predict the likelihood of equipment failure in a manufacturing plant. The model will analyze sensor data, maintenance logs, and environmental conditions. The primary goal is to classify whether a piece of equipment will fail within the next month (binary outcome). Which evaluation metric is most critical if missing a potential failure (false negative) is extremely costly, but flagging a healthy machine as potentially failing (false positive) is less severe?Describe fundamental principles of machine learning on Azure
  15. 65.A team of data scientists is evaluating a machine learning model designed to predict customer churn. They have identified that the cost of incorrectly classifying a loyal customer as a churner (False Positive) is significantly lower than the cost of incorrectly classifying a churner as a loyal customer (False Negative), which leads to lost revenue. To optimize the model for business impact, which metric should they prioritize to minimize the latter type of error (missing actual churners)?Describe fundamental principles of machine learning on Azure
  16. 66.A data scientist is preparing a dataset for a machine learning model that predicts customer purchasing intent. One of the features is 'Customer_Segment', which can be 'New', 'Existing', or 'Premium'. The data scientist needs to convert this categorical feature into a numerical format suitable for most machine learning algorithms. Which of the following techniques should be used to represent this feature without implying any ordinal relationship?Describe fundamental principles of machine learning on Azure
  17. 67.A manufacturing company uses sensors to collect data from its machinery. They want to identify unusual patterns or anomalies in the sensor data that might indicate equipment malfunction or impending failure, without having any prior examples of 'failure' data. Which machine learning approach is best suited for this task?Describe fundamental principles of machine learning on Azure
  18. 68.A machine learning engineer is deploying a model to an Azure service that requires high availability, scalability, and the ability to process real-time predictions. The model is containerized and needs to be accessible via a REST API endpoint. Which Azure service is specifically designed for deploying and managing machine learning models in such a production environment?Describe fundamental principles of machine learning on Azure
  19. 69.A financial institution is developing a machine learning model to detect potential money laundering activities. The model needs to analyze large volumes of transaction data to identify patterns that deviate significantly from normal behavior without prior labeled examples of money laundering. Which type of machine learning approach is most suitable for this task?Describe fundamental principles of machine learning on Azure
  20. 70.A research team is developing an AI system to analyze satellite imagery for detecting deforestation. The system needs to identify areas where trees have been removed. This task involves classifying each pixel in an image as either 'forest' or 'deforested'. Which type of computer vision task does this scenario best represent?Describe fundamental principles of machine learning on Azure
  21. 71.A financial institution wants to develop a machine learning model to predict whether a loan applicant will default on their loan. The model will analyze various features such as credit score, income, and debt-to-income ratio. The output of the model needs to be a clear 'Yes' or 'No' decision regarding default. Which type of machine learning task best fits this scenario?Describe fundamental principles of machine learning on Azure
  22. 72.A medical research team is using a machine learning model to identify rare disease outbreaks. The dataset is highly imbalanced, with very few positive cases (outbreaks) compared to negative cases (no outbreak). The team wants to ensure the model does not miss any potential outbreaks, even if it means having some false alarms. Which type of error should the team prioritize minimizing?Describe fundamental principles of machine learning on Azure
  23. 73.A data scientist observes that their machine learning model performs exceptionally well on the training data, achieving very high accuracy. However, when the model is tested on new, unseen data, its performance drops significantly, and it generalizes poorly. This indicates that the model has learned the training data too specifically, including noise and irrelevant details. What is this phenomenon called?Describe fundamental principles of machine learning on Azure
  24. 74.A data scientist is preparing a dataset for a regression model that predicts house prices. The dataset includes features such as 'Area_sqft' (ranging from 500 to 5000), 'Number_of_Bedrooms' (1 to 5), and 'Year_Built' (1900 to 2023). Some machine learning algorithms are sensitive to the scale of input features, where features with larger numerical ranges might disproportionately influence the model. Which data preprocessing technique should be applied to ensure all numerical features contribute equally to the model?Describe fundamental principles of machine learning on Azure
  25. 75.A financial institution is developing an AI system to analyze customer feedback from various sources, such as emails, social media posts, and call transcripts. The goal is to automatically identify the sentiment (positive, negative, neutral) expressed in each piece of feedback and extract key topics or entities mentioned. Which area of AI is primarily concerned with these types of tasks?Describe fundamental principles of machine learning on Azure
  26. 76.A healthcare provider is using a machine learning model to predict the risk of a patient developing a rare but severe disease. The model has been trained and achieved high accuracy on a dataset where healthy patients vastly outnumber those with the disease. During evaluation, it's observed that while the model rarely misclassifies a healthy person as having the disease, it frequently fails to detect actual disease cases among the sick. Which type of error is the model making that is of most concern in this critical medical scenario?Describe fundamental principles of machine learning on Azure
  27. 77.A data scientist is building a machine learning model to predict the likelihood of equipment failure in a manufacturing plant. The model will analyze sensor data, maintenance logs, and environmental conditions. To effectively train this model, the data scientist needs a comprehensive environment that allows for data preparation, model training, deployment, and monitoring, all integrated within the Azure ecosystem. Which Azure machine learning service provides this end-to-end platform?Describe fundamental principles of machine learning on Azure
  28. 78.A research team is developing an AI system to analyze medical images (e.g., X-rays, MRIs) to precisely delineate organs, tumors, or other anatomical structures. The goal is not just to classify the image as 'diseased' or 'healthy', but to identify the exact boundaries and location of specific regions of interest within the image. Which computer vision task is most appropriate for this objective?Describe fundamental principles of machine learning on Azure
  29. 79.A data scientist is working on a machine learning project to predict customer churn. After training a classification model, they observe that the model performs exceptionally well on the training data but significantly worse on new, unseen data. This indicates a common problem in machine learning. Which concept does this scenario describe?Describe fundamental principles of machine learning on Azure
  30. 80.A data scientist is preparing a dataset for a machine learning model that predicts house prices. The dataset contains a 'Neighborhood' column with categorical values like 'Downtown', 'Suburban', and 'Rural'. To make this data suitable for most machine learning algorithms, which preprocessing technique should be applied?Describe fundamental principles of machine learning on Azure
  31. 81.A research team is developing an AI system to analyze medical images (e.g., X-rays, MRIs) to automatically detect and delineate tumors or other anomalies. The system needs to precisely identify the boundaries of these regions within the image. Which computer vision technique is most suitable for this task?Describe fundamental principles of machine learning on Azure
  32. 82.A data scientist is building a machine learning model to predict the energy consumption of a building. The model uses historical data including temperature, humidity, and occupancy levels. The primary goal is to minimize the average magnitude of errors between the predicted consumption and the actual consumption, regardless of the direction of the error. Which evaluation metric should the data scientist prioritize?Describe fundamental principles of machine learning on Azure
  33. 83.A financial institution wants to develop a machine learning model to detect fraudulent transactions. They have a large dataset of past transactions, with each transaction clearly labeled as 'fraudulent' or 'legitimate'. The primary goal is to accurately classify new, unseen transactions. Which type of machine learning is most appropriate for this scenario?Describe fundamental principles of machine learning on Azure
  34. 84.A data scientist is evaluating various machine learning models for a binary classification task. To thoroughly assess the models' performance across different classification thresholds and understand their ability to distinguish between positive and negative classes, the data scientist plots the True Positive Rate against the False Positive Rate. Which evaluation curve is the data scientist using?Describe fundamental principles of machine learning on Azure
  35. 85.A data scientist is training a machine learning model. During the training process, the model's performance on the training dataset continuously improves, reaching nearly perfect accuracy. However, when the model is evaluated on a separate, unseen validation dataset, its performance is significantly worse. The model struggles to generalize to new data. Which core machine learning concept describes this phenomenon?Describe fundamental principles of machine learning on Azure
  36. 86.A machine learning engineer is evaluating a binary classification model for predicting a rare disease. The model has an accuracy of 99%, but further analysis reveals that the disease prevalence in the dataset is only 1%. This high accuracy is primarily due to the model correctly identifying healthy individuals, while its performance on actual disease cases is poor. Which concept explains why accuracy alone is misleading in this scenario?Describe fundamental principles of machine learning on Azure
  37. 87.A machine learning engineer is developing a model to identify spam emails. The model is trained on a large dataset of emails labeled as 'spam' or 'not spam'. After training, the engineer evaluates the model's performance. Which of the following metrics is most crucial if the primary goal is to ensure that legitimate emails are almost never incorrectly classified as spam?Describe fundamental principles of machine learning on Azure
  38. 88.A data scientist is preparing a dataset for a machine learning model that predicts customer churn. The dataset includes a feature called 'Customer_ID', which is a unique identifier for each customer. This feature has no inherent numerical meaning or order. What is the most appropriate action for this feature before training the model?Describe fundamental principles of machine learning on Azure
  39. 89.An AI developer is building a model to predict the likelihood of a customer churning (canceling their subscription). The model's output is a probability score between 0 and 1. Which evaluation metric would be most appropriate to assess the model's ability to distinguish between churning and non-churning customers across various probability thresholds?Describe fundamental principles of machine learning on Azure
  40. 90.A data scientist is working on a recommendation system for an e-commerce platform. The system needs to suggest products to users based on their past interactions (e.g., purchases, views) and the interactions of similar users. The goal is to leverage the collective behavior of users to provide personalized recommendations without relying on explicit product descriptions or user profiles. Which machine learning technique is most appropriate for this scenario?Describe fundamental principles of machine learning on Azure
  41. 91.A research team is developing an AI system that can read and understand medical reports written in natural language, extracting key information such as patient diagnoses, prescribed medications, and treatment plans. This extracted information will then be used to populate structured databases. Which branch of AI is primarily concerned with enabling computers to understand, interpret, and generate human language?Describe fundamental principles of machine learning on Azure
  42. 92.A data engineering team is setting up a data pipeline for a machine learning project on Azure. They need to store large volumes of unstructured data, including images, videos, and log files, which will be accessed by various Azure AI services for training and inference. The storage solution must be highly scalable, durable, and accessible via REST APIs. Which Azure storage service is best suited for this purpose?Describe fundamental principles of machine learning on Azure
  43. 93.An AI developer is tasked with building a system that can understand and respond to customer queries in natural language, automating parts of customer support. The system needs to extract key information from unstructured text inputs and provide relevant answers. Which core machine learning concept is primarily involved in enabling this functionality?Describe fundamental principles of machine learning on Azure
  44. 94.An AI developer is building a model to predict the likelihood of a customer churning (canceling their subscription). The model outputs a probability score between 0 and 1. To evaluate the model's ability to distinguish between churners and non-churners across all possible classification thresholds, which metric is most appropriate?Describe fundamental principles of machine learning on Azure
  45. 95.A machine learning engineer is deploying a model that predicts manufacturing defects. The model outputs a probability score for each product. To decide which products to send for inspection, a threshold needs to be set on this probability score. Changing this threshold will affect the trade-off between identifying actual defects and incorrectly flagging good products. Which graphical tool is most appropriate for visualizing and choosing an optimal threshold based on this trade-off?Describe fundamental principles of machine learning on Azure
  46. 96.An AI developer is tasked with building a system that can understand and respond to customer queries in natural language, automating customer support. The system needs to extract key entities (e.g., product names, order numbers) from text, identify the customer's intent (e.g., 'shipping inquiry', 'refund request'), and generate human-like responses. Which field of AI encompasses these capabilities?Describe fundamental principles of machine learning on Azure
  47. 97.A data scientist is preparing a dataset that contains a 'Product_Category' feature with values like 'Electronics', 'Clothing', 'Home Goods', and 'Books'. These categories have no inherent order or numerical relationship. To use this feature in a machine learning model, it must be converted into a numerical format without implying any false sense of order or magnitude. Which technique is most appropriate?Describe fundamental principles of machine learning on Azure
  48. 98.A data scientist is evaluating a machine learning model's performance on a binary classification task. The model predicted 100 positive cases, of which 80 were actually positive. It also predicted 50 negative cases, of which 45 were actually negative. The total number of actual positive cases in the dataset was 90. Which of the following metrics would be most suitable for evaluating the model's ability to correctly identify all actual positive cases?Describe fundamental principles of machine learning on Azure
  49. 99.A data scientist is evaluating a machine learning model for predicting customer satisfaction scores on a scale of 1 to 5. The model's predictions are compared against the actual scores. The goal is to measure the average magnitude of the errors without considering their direction (i.e., whether the prediction was too high or too low). Which evaluation metric is most appropriate for this scenario?Describe fundamental principles of machine learning on Azure
  50. 100.A data scientist is evaluating a machine learning model designed to predict whether a customer will click on an advertisement. The model outputs a probability score between 0 and 1. To assess the model's performance across all possible classification thresholds, which visualization tool should be used?Describe fundamental principles of machine learning on Azure