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Certifications›AI-900›Objectives›Describe fundamental principles of machine learning on Azure
Objective 2.0

Describe fundamental principles of machine learning on Azure

AI-900 Practice Questions

Use this page to practise Describe fundamental principles of machine learning on Azure questions for this certification. Focus on how the exam tests describe fundamental principles of machine learning on azure in scenario format — understanding the why behind each answer builds more durable knowledge than memorising options.

Full Practice Test →All Objectives

What this objective tests

AI-900 Describe fundamental principles of machine learning on Azure — Key Topics

Describe fundamental principles of machine learning on Azure questions on this certification test your ability to deploy and manage describe fundamental principles of machine learning on azure concepts in scenario-based situations.

  • Core Describe fundamental principles of machine learning on Azure concepts and how they apply in real-world cloud scenarios.
  • How to deploy describe fundamental principles of machine learning on azure correctly and verify the outcome.
  • Troubleshooting describe fundamental principles of machine learning on azure issues by interpreting error output and system state.
  • Cloud best practices and Describe fundamental principles of machine learning on Azure design trade-offs tested by this certification.

Common exam traps

Where candidates lose marks on Describe fundamental principles of machine learning on Azure

  • ⚠Selecting the most expensive service when a simpler managed option meets the requirement.
  • ⚠Forgetting that cloud resources must be explicitly secured — defaults are rarely secure.
  • ⚠Choosing a global service fix when the issue is region-specific.
  • ⚠Overlooking cost implications of cross-region data transfer in architecture questions.

AI-900 Describe fundamental principles of machine learning on Azure — Practice Questions

30 questions from this objective

Question 2mediummultiple choice
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A data scientist wants to train a machine learning model to predict the exact market price of a house based on features such as square footage, number of bedrooms, and location. Which type of machine learning task should be used?

Question 3mediummultiple choice
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A data scientist has trained a binary classification model to predict whether an email is spam (positive) or not spam (negative). On a test set, the model correctly identifies 90 out of 100 actual spam emails and 80 out of 100 actual non-spam emails. Which metric shows the proportion of actual spam emails that the model correctly predicted?

Question 4mediummultiple choice
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A retail company wants to predict which customers are likely to stop using their service. They have a dataset with many customer attributes including age, income, purchase history, website activity, and support interactions. They suspect some features are redundant. Which technique should they use to reduce the number of features while preserving as much information as possible?

Question 5easymultiple choice
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A retail company wants to automatically group its customers into distinct segments based on their purchasing patterns, without having pre-defined categories. The goal is to discover natural groupings in the customer data to tailor marketing campaigns. Which type of machine learning task should the company use?

Question 6mediummultiple choice
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A hospital has a dataset with historical patient records, each labeled as either 'readmitted within 30 days' or 'not readmitted'. The hospital wants to train a model to predict which current patients are likely to be readmitted. Which type of machine learning task is this?

Question 7easymultiple choice
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A data scientist trains a machine learning model to predict housing prices. On the training data, the model achieves an R-squared value of 0.99, but on a separate validation dataset it achieves an R-squared of only 0.65. What is the most likely issue with this model?

Question 8mediummultiple choice
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A data scientist trains a machine learning model on a dataset of housing prices. The model achieves 98% accuracy on the training data but only 72% accuracy on a separate test set. What is the most likely problem with this model?

Question 9easymultiple choice
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A retail company wants to predict the exact number of units of a product that will be sold next month. They have historical sales data and information about promotions and holidays. The target variable is the number of units sold, which is a continuous value. Which type of machine learning task should they perform?

Question 10mediummultiple choice
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A data scientist trains a regression model on a dataset with 100 features and 10,000 samples. The model achieves a low training error but a much higher error on a held-out test set. Which approach is most likely to improve the model's generalization performance?

Question 11hardmultiple choice
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A data scientist is training a binary classification model to detect fraudulent transactions. The dataset contains only 1% fraudulent transactions. The model achieves 99% accuracy on the test set, but when deployed, it fails to detect most actual fraud cases. Which metric would best reveal this issue?

Question 12mediummultiple choice
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A data scientist is building a classification model to predict customer churn. The dataset has only 5% churn cases. The model achieves 95% accuracy on the test set, but upon investigation, the data scientist finds the model predicts 'not churn' for nearly every customer. Which metric should the data scientist primarily use to evaluate the model's performance on this imbalanced dataset?

Question 13mediummultiple choice
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A bike-sharing company wants to predict the number of rentals per hour. Their model's predictions are usually close but occasionally have large errors due to unexpected events like sudden rain. They want a metric that heavily penalizes these large errors to ensure the model is not overly confident. Which evaluation metric should they primarily use?

Question 14mediummultiple choice
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A data scientist trains a regression model to predict house prices. The model has a mean absolute error (MAE) of $5,000 on the test set. Which statement best interprets this metric?

Question 15easymultiple choice
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A company builds a machine learning model to predict whether a customer will purchase a product. They use a training dataset with 50% purchasers and 50% non-purchasers. The model achieves 90% accuracy on the test set. However, when deployed, the model performs poorly because the actual customer base has only 5% purchasers. What is the most likely cause of this poor performance?

Question 16easymultiple choice
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A data scientist trains a classification model to distinguish between images of cats and dogs. The model achieves 99% accuracy on the training set but only 75% accuracy on a validation set. Which concept best describes this situation?

Question 17easymultiple choice
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A data scientist is building a model to predict the exact temperature in degrees Celsius based on humidity and atmospheric pressure. The model will output a single numeric value for each input. Which type of machine learning task is this?

Question 18mediummultiple choice
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A media company wants to automatically organize a large collection of news articles into several topic-based categories (e.g., politics, sports, technology) without using any predefined labels. They plan to use Azure Machine Learning. Which type of machine learning task should they use?

Question 19mediummultiple choice
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A retail company wants to segment its customers into different groups based on purchasing behavior, without using predefined categories. Which type of machine learning task should they use?

Question 20mediummultiple choice
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A data scientist trains a deep neural network on a small dataset. The model achieves 100% accuracy on the training data but only 60% accuracy on a validation set. Which technique is most appropriate to address this issue?

Question 21hardmultiple choice
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An online retailer wants to build a recommendation system that learns from user interactions. The system suggests a product, and if the user clicks it, it receives a positive reward; if ignored, a negative reward. Over time, the system learns to make better suggestions. Which type of machine learning best describes this approach?

Question 22mediummultiple choice
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A manufacturing team wants to predict product defects based on sensor readings from the production line. They have 10,000 historical samples, each labeled as 'defective' or 'non-defective'. Which type of machine learning should they use in Azure Machine Learning?

Question 23mediummultiple choice
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A data scientist trains a classification model to predict whether an email is 'phishing' or 'legitimate'. The model achieves 99% accuracy on the training data but only 68% accuracy on the test data. Which action is most likely to help improve the model's generalization performance?

Question 24hardmultiple choice
Read the full NAT/PAT explanation →

A data scientist is training a regression model to predict house prices. The model performs near perfectly on the training data but poorly on a held-out test set. The scientist suspects the model is memorizing the training data instead of learning general patterns. Which technique is most appropriate to directly address this issue?

Question 25mediummultiple choice
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A data scientist is building a machine learning model to predict whether a credit card transaction is fraudulent or legitimate. The dataset contains 100,000 historical transactions, each labeled as 'fraudulent' or 'legitimate'. Which type of machine learning task should the data scientist use in Azure Machine Learning?

Question 26mediummultiple choice
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A data scientist is training a classification model on a dataset with 100 features and only 500 labeled samples. The model achieves 99% accuracy on the training data but only 68% accuracy on a held-out test set, indicating overfitting. Which technique is most appropriate to directly address this problem?

Question 27hardmultiple choice
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A data scientist is building a classification model to detect fraudulent transactions. The dataset has 1,000,000 legitimate transactions and only 1,000 fraudulent ones. The model achieves 99.9% accuracy on the test set, but it fails to catch most fraudulent cases. Which metric should the data scientist prioritize to better evaluate the model's performance on this imbalanced dataset?

Question 28easymultiple choice
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A data scientist trains a binary classification model to distinguish between images of cats and dogs. On the test set, the model achieves 98% accuracy, but a deeper inspection reveals that the test set contains 95% cats and 5% dogs, and the model predicts 'cat' for every single image. Which metric should the data scientist prioritize to get a more realistic evaluation of the model's performance on this imbalanced dataset?

Question 29mediummultiple choice
Read the full NAT/PAT explanation →

A retail company has a dataset of customer transaction records with no predefined categories. They want to identify natural groupings of customers based on their purchasing behavior to create targeted marketing campaigns. Which type of machine learning should they use in Azure Machine Learning?

Question 30mediummultiple choice
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A data scientist trains a regression model to predict house prices using features like square footage, number of bedrooms, and location. The model achieves very high accuracy on the training data but performs poorly on a held-out test set. Which technique should the data scientist apply to reduce overfitting?

Question 31easymultiple choice
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A data scientist has a dataset containing thousands of labeled images of cats and dogs. The data scientist wants to train a model that can automatically classify new unlabeled images as either 'cat' or 'dog'. Which type of machine learning should the data scientist use?

More Describe fundamental principles of machine learning on Azure questions available in the full practice test.

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All AI-900 Objectives

  • 1.Describe Artificial Intelligence workloads and considerations
  • 2.Describe fundamental principles of machine learning on Azure
  • 3.Describe features of computer vision workloads on Azure
  • 4.Describe features of Natural Language Processing workloads on Azure
  • 5.Describe features of generative AI workloads on Azure