Microsoft Azure AI Fundamentals - AI-900 Exam Practice Test
You need to predict the population size of a specific species of animal in an area.
Which Azure Machine Learning type should you use?
Which Azure Machine Learning type should you use?
Correct Answer: B
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Select the answer that correctly completes the sentence.


Correct Answer:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", regression is a supervised machine learning technique used to predict continuous numerical values based on one or more input features. In this scenario, the task is to predict a vehicle's miles per gallon (MPG)-a continuous numeric value-based on several measurable factors such as weight, engine power, and other specifications.
Regression models learn the mathematical relationship between input variables (independent features) and a numeric target variable (dependent outcome). Common regression algorithms include linear regression, decision tree regression, and support vector regression. In the example, the model would analyze historical data of vehicles and learn patterns that map characteristics (like engine size, horsepower, and weight) to fuel efficiency. Once trained, it can predict the MPG for a new vehicle configuration.
The other options describe different problem types:
* Classification predicts discrete categories (for example, whether a car is "fuel efficient" or "not fuel efficient"), not continuous values.
* Clustering is an unsupervised learning method that groups data points based on similarities without predefined labels, not predictive modeling.
* Anomaly detection identifies data points that significantly deviate from normal patterns, such as detecting engine sensor failures or fraudulent transactions.
Since predicting MPG involves estimating a numeric value within a continuous range, regression is the most appropriate model type.
In summary, per AI-900 training content, regression models are used when the output variable is numeric, classification for categorical outputs, and clustering for pattern discovery. Therefore, predicting miles per gallon based on vehicle features is a textbook example of a regression problem in Azure Machine Learning.
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using Azure Al Language Service ' s question answering? Each correct answer presents a complete solution.
NOTE: Each correct and ask questions by selection is worth one point.
What are three ways to create question and answer text by using Azure Al Language Service ' s question answering? Each correct answer presents a complete solution.
NOTE: Each correct and ask questions by selection is worth one point.
Correct Answer: A,B,E
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You need to generate images based on user prompts. Which Azure OpenAI model should you use?
Correct Answer: B
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Which Computer Vision feature can you use to generate automatic captions for digital photographs?
Correct Answer: D
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Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:

This question tests understanding of AI workload types, a fundamental topic in the Microsoft Azure AI Fundamentals (AI-900) curriculum. Each workload type-Computer Vision, Natural Language Processing, Machine Learning (Regression), and Anomaly Detection-serves a specific function within the AI landscape, as explained in Microsoft Learn's module "Describe features of common AI workloads."
* Computer Vision enables computers to "see" and interpret visual information such as images or videos.
Identifying handwritten letters requires analyzing image patterns, shapes, and strokes, which is a classic image recognition task. Azure's Computer Vision API and Custom Vision services are specifically designed for such tasks.
* Natural Language Processing (NLP) involves interpreting human language, both written and spoken.
Determining the sentiment of a social media post (positive, negative, or neutral) is a typical text analytics use case within NLP, often implemented using Azure's Text Analytics for Sentiment Analysis.
* Anomaly Detection focuses on identifying data points that deviate from normal patterns. Detecting fraudulent credit card payments requires finding transactions that are unusual compared to historical spending behavior. Azure's Anomaly Detector API applies machine learning to identify such irregularities.
* Machine Learning (Regression) is used for predicting continuous numerical outcomes based on historical data. Estimating next month's toy sales is a regression problem-an example of supervised learning where the model predicts future sales values from past sales data.
Thus, based on Microsoft's official AI-900 learning objectives, the correct mapping of workloads to scenarios is:
* Computer Vision # Identify handwritten letters
* NLP # Predict sentiment
* Anomaly Detection # Fraud detection
* Machine Learning (Regression) # Predict toy sales
In which two scenarios can you use the Azure Al Document Intelligence service (formerly Form Recognizer)?
Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Correct Answer: A,D
Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).
You need to develop a mobile app for employees to scan and store their expenses while travelling.
Which type of computer vision should you use?
Which type of computer vision should you use?
Correct Answer: D
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