Microsoft Azure AI Fundamentals - AI-900 Exam Questions
QUESTION NO: 1
You need to predict the income range of a given customer by using the following dataset.
Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
You need to predict the income range of a given customer by using the following dataset.
Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Correct Answer: B,C
Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).
QUESTION NO: 2
What is an example of a regression model in machine learning?
What is an example of a regression model in machine learning?
Correct Answer: D
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QUESTION NO: 3
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat.
Which service should you use?
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat.
Which service should you use?
Correct Answer: A
Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).
QUESTION NO: 4
You need to implement a pre-built solution that will identify well-known brands in digital photographs.
Which Azure Al sen/tee should you use?
You need to implement a pre-built solution that will identify well-known brands in digital photographs.
Which Azure Al sen/tee should you use?
Correct Answer: B
Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).
QUESTION NO: 5
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
This question is based on identifying Natural Language Processing (NLP) workloads, which is a fundamental topic in the Microsoft Azure AI Fundamentals (AI-900) certification. According to the official Microsoft Learn module "Describe features of natural language processing (NLP) workloads on Azure", NLP enables computers to understand, interpret, and generate human language - both written and spoken.
* A bot that responds to queries by internal users - YesThis is an example of a natural language processing workload because it involves understanding and generating human language. A chatbot interprets user input (queries written or spoken) using language understanding and text analytics, and then produces appropriate responses. On Azure, this can be implemented using Azure AI Language (LUIS) and the Azure Bot Service, both core NLP technologies.
* A mobile application that displays images relating to an entered search term - NoThis application involves searching for or displaying images, which falls under the computer vision workload, not NLP.
Computer vision focuses on analyzing and interpreting visual data like photos or videos, while NLP deals with language and text processing.
* A web form used to submit a request to reset a password - NoA password reset form involves structured input fields and user authentication, not natural language understanding or generation. It's part of standard web development and identity management, not an NLP-related process.
Therefore, based on Microsoft's AI-900 curriculum definitions:
# The only true NLP example is the bot responding to user queries, since it processes and understands natural language input to generate conversational output.
QUESTION NO: 6
Which two resources can you use to analyze code and generate explanations of code function and code comments? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Which two resources can you use to analyze code and generate explanations of code function and code comments? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Correct Answer: C,D
Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).
QUESTION NO: 7
You are building a Language Understanding model for an e-commerce business.
You need to ensure that the model detects when utterances are outside the intended scope of the model.
What should you do?
You are building a Language Understanding model for an e-commerce business.
You need to ensure that the model detects when utterances are outside the intended scope of the model.
What should you do?
Correct Answer: D
Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).
QUESTION NO: 8
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Correct Answer: A,C
Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).
QUESTION NO: 9
Select the answer that correctly completes the sentence.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of common AI workloads," an anomaly detection workload is designed to identify data points or patterns that deviate significantly from what is expected or normal. These anomalies often indicate irregularities, faults, or potential issues that require attention.
In this scenario, the AI system monitors temperature data from a large machine. Normally, the machine operates within a predictable temperature range. When the AI detects sudden or unexpected temperature spikes or drops - behavior that does not match the historical pattern - it flags these occurrences as anomalies. This type of workload is fundamental in predictive maintenance and industrial monitoring, where it helps detect equipment failures, safety hazards, or energy inefficiencies before they escalate.
Microsoft's AI-900 curriculum emphasizes that anomaly detection workloads are often used in:
* Industrial IoT systems (detecting abnormal sensor readings or machine behavior)
* Finance (fraud detection or unusual transaction monitoring)
* Cybersecurity (detecting irregular network traffic or access patterns)
* Operations (identifying abnormal variations in production data)
The Azure service used for this purpose is Azure Anomaly Detector, part of Azure Cognitive Services, which uses advanced statistical and machine learning models to automatically detect outliers in time-series data such as temperature, pressure, or transaction logs.
By comparison:
* Computer vision handles image or video analysis.
* Knowledge mining extracts insights from large document collections.
* Natural Language Processing (NLP) interprets human language.
Thus, based on the official Microsoft AI-900 study guide and Microsoft Learn, the correct and verified answer is An anomaly detection workload, since detecting unusual temperature fluctuations precisely fits this AI workload type.
QUESTION NO: 10
Select the answer that correctly completes the sentence.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
The Azure OpenAI Service provides access to advanced Generative Pre-trained Transformer (GPT) models developed by OpenAI, such as GPT-3, GPT-3.5, and GPT-4. These models are capable of performing a wide range of natural language processing (NLP) and generative AI tasks - including text completion, summarization, translation, question answering, content creation, and code generation.
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure OpenAI, this managed service allows developers to deploy and integrate GPT-based models within their own applications using REST APIs or the Azure SDK. The service handles scalability, performance, and infrastructure automatically, meaning users do not need to manage servers or computational resources manually.
Option review:
* Supports the deployment of GPT-based models - # Correct. Azure OpenAI is specifically designed for deploying and operationalizing GPT models and similar transformer-based architectures.
* Provides capabilities exclusively for vision-related tasks - # Incorrect. Vision tasks (like image classification or object detection) are part of Azure AI Vision.
* Provides capabilities exclusively for speech-related tasks - # Incorrect. Speech processing (speech-to- text, text-to-speech, translation) belongs to Azure AI Speech Services, not Azure OpenAI.
* Requires manual infrastructure management for scalability - # Incorrect. Azure OpenAI is a fully managed service; scalability and performance are handled automatically by Azure.
Therefore, the correct completion of the sentence is:
"Azure OpenAI Service supports the deployment of GPT-based models."
QUESTION NO: 11
To complete the sentence, select the appropriate option in the answer area.
To complete the sentence, select the appropriate option in the answer area.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, object detection is a type of computer vision workload that not only identifies objects within an image but also determines their location by drawing bounding boxes around them. This functionality is clearly described in the Microsoft Learn module "Identify features of computer vision workloads." In this scenario, the AI system analyzes an image to find a vehicle and then returns a bounding box showing where that vehicle is located within the image frame. That ability - to detect, classify, and localize multiple objects - perfectly defines object detection.
Microsoft's study content contrasts object detection with other computer vision workloads as follows:
* Image classification: Determines what object or scene is present in an image as a whole but does not locate it (e.g., "this is a car").
* Object detection: Identifies what objects are present and where they are, usually returning coordinates for bounding boxes (e.g., "car detected at position X, Y").
* Optical Character Recognition (OCR): Extracts text content from images or scanned documents.
* Facial detection: Specifically locates human faces within an image or video feed, often as part of face recognition systems.
In Azure, object detection capabilities are available through services such as Azure Computer Vision, Custom Vision, and Azure Cognitive Services for Vision, which can be trained to detect vehicles, products, or other objects in various image datasets.
Therefore, based on the AI-900 study guide and Microsoft Learn materials, the verified and correct answer is Object detection, as it accurately describes the process of returning a bounding box indicating an object's position in an image.
QUESTION NO: 12
Select the answer that correctly completes the sentence.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning," regression is a type of supervised machine learning used to predict continuous numeric values.
In this question, the goal is to predict how many vehicles will travel across a bridge on a given day. The predicted output (the number of vehicles) is a continuous value-meaning it can take on any numerical value depending on various factors like time, weather, or day of the week. This makes it a regression problem, as the model learns from historical numeric data to estimate a continuous outcome.
How Regression Works:
Regression models find patterns between input features (such as temperature, weekday/weekend, traffic trends) and a numerical output (number of vehicles). Common regression algorithms include linear regression, decision trees for regression, and neural network regression. In Azure Machine Learning, regression tasks are used for business scenarios such as:
* Predicting sales revenue for a future month.
* Estimating house prices based on property characteristics.
* Forecasting energy consumption or traffic flow, as in this case.
Why not the other options?
* Classification: Used for predicting discrete categories (e.g., "spam" vs. "not spam"). It does not handle continuous numeric values.
* Clustering: An unsupervised learning technique used to group data points based on similarity without predefined labels (e.g., segmenting customers into groups).
Therefore, the task of predicting the number of vehicles-a numeric, continuous value-is a regression problem.
