Exam AI-900: Microsoft Azure AI Fundamentals Practice Test
Exam AI-900 practice covers foundational artificial intelligence concepts and how they apply to common Azure scenarios. The available questions address machine learning, computer vision, natural language processing, responsible handling of data, and cloud AI services. They ask you to recognize suitable capabilities for a task and interpret basic model behavior, such as prediction error and performance on new data.
Exam overview
About the Exam AI-900: Microsoft Azure AI Fundamentals Exam
Work through distinctions among supervised learning tasks, including classification for labeled categories and regression for numerical predictions. Relate model evaluation measures to the problem: for example, mean absolute error expresses typical regression error in the target variable’s units. In computer vision, distinguish image classification from object detection when a task requires locating and labeling multiple objects. For language workloads, identify text-to-speech and summarization by their inputs and outputs. Other scenarios call for data preparation, such as handling missing or duplicated records, and for recognizing overfitting when training performance fails to transfer to unseen examples. Questions also connect responsible AI practices, including privacy safeguards, with practical cloud-service use, such as APIs for application-to-service communication and scaling to accommodate changing demand.
Sample Practice Questions
Review a selection from this exam's published US Practice Tests practice bank.
A data scientist has two candidate models for predicting house prices. Which metric directly summarizes the typical size of their prediction errors in the same units as the prices?
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Correct answer: AMean absolute error averages the absolute differences between predicted and actual values, so it is expressed in the target variable's units. Accuracy, precision, and recall are commonly used for classification tasks.
A photo application must locate several people in an image and draw a box around each person. Which computer vision capability is most suitable?
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Correct answer: AObject detection identifies instances of objects in an image and typically returns their locations, such as bounding boxes. Image classification labels an image without necessarily locating each object.
An accessibility tool needs to read a written notification aloud in a natural-sounding voice. Which capability does it need?
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Correct answer: BText-to-speech, also called speech synthesis, generates spoken audio from text. Speech recognition performs the reverse task by converting speech to text.
When an organization uses AI to process personal information, which practice is most directly related to protecting that information from inappropriate access or disclosure?
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Correct answer: APrivacy and security practices include protecting personal data, limiting access to authorized users, and safeguarding data throughout its use and storage.
A team has a dataset containing thousands of customer records. Before training a model, it checks for missing values, inconsistent units, and duplicate rows. What is this work an example of?
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Correct answer: AData preparation includes inspecting and cleaning data so it is suitable for analysis or model training. Inference and deployment occur after a model is available.
A company wants to predict whether a customer will renew a subscription, using past examples labeled “renewed” or “did not renew.” Which machine learning task fits this goal?
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Correct answer: AClassification learns from labeled examples to assign new cases to categories, such as renewal or nonrenewal.
A team needs to keep a cloud AI application responsive as the number of users changes. Which operational practice is most directly useful?
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Correct answer: AMonitoring usage and performance helps teams identify changing demand and make appropriate capacity or scaling adjustments. Initial capacity may not remain adequate as usage changes.
How to use this practice test
The free US Practice Tests practice bank contains 50 questions. A session presents up to 20 random questions and shows your results afterward. Use the results to identify concepts to revisit, then retry questions to check whether you can distinguish similar AI tasks and select an appropriate capability for each scenario.
Preparation tips
- For each scenario, identify the input and desired output before choosing a machine learning task: a numeric value suggests regression, while a labeled category suggests classification.
- Compare regression metrics by what they communicate; note that mean absolute error is expressed in the same units as the predicted values.
- Practice separating object detection, which locates objects with bounding boxes, from image classification, which assigns labels to an image.
- Map language tasks to their outputs: text-to-speech produces spoken audio, while summarization condenses source text.
- Review data preparation and model generalization: spot missing, inconsistent, or duplicate records, and recognize overfitting when training results do not hold on new examples.