Free Practice Questions•Azure AI Fundamentals•30 Questions with Answers•Free Practice Questions•Azure AI Fundamentals•30 Questions with Answers•
FREE QUESTIONS
Azure AI Fundamentals Practice Questions
30 free questions with correct answers and detailed explanations.
30Free Questions
2Free Exams
100%With Explanations
AI-900 Practice Set-01
15 questions
Q1
You need to create a customer support solution to help customers access information. The solution must support email, phone, and live chat channels. Which type of Al solution should you use?
A
machine learning
B
computer vision
chatbot
D
natural language processing (NLP)
Correct Answer
chatbot
Explanation
A chatbot is the correct AI solution for handling multi-channel customer support (email, phone, live chat). Chatbots use conversational AI to simulate human interaction across various channels. Learn more: https://learn.microsoft.com/en-us/azure/bot-service/bot-service-overview
Q2
For a machine learning progress, how should you split data for training and evaluation?
A
Use features for training and labels for evaluation.
Randomly split the data into rows for training and rows for evaluation.
C
Use labels for training and features for evaluation.
D
Randomly split the data into columns for training and columns for evaluation.
Correct Answer
Randomly split the data into rows for training and rows for evaluation.
Explanation
In machine learning, rows represent individual data samples. The correct practice is to randomly split rows into training and evaluation subsets to avoid bias and ensure the model generalises well to unseen data. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-split
Q3
You are building an AI system. Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?
A
Ensure that all visuals have an associated text that can be read by a screen reader.
B
Enable autoscaling to ensure that a service scales based on demand.
Provide documentation to help developers debug code.
D
Ensure that a training dataset is representative of the population.
Correct Answer
Provide documentation to help developers debug code.
Explanation
Microsoft's Transparency principle requires that AI systems be understandable and explainable. Providing documentation so users and developers can understand how the AI makes decisions is a core transparency practice. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
Q4
You use drones to identify where weeds grow between rows of crops to send an instruction for the removal of the weeds. This is an example of which type of computer vision?
object detection
B
optical character recognition (OCR)
C
scene segmentation
Correct Answer
object detection
Explanation
Using drones equipped with cameras and computer vision to detect weeds among crops is an object detection workload where specific objects (weeds) are identified in images. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview
Q5
What are two tasks that can be performed by using the Computer Vision service?
A
Train a custom image classification model.
Detect faces in an image.
Recognize handwritten text.
D
Translate the text in an image between languages.
Correct Answers
Detect faces in an image.
Recognize handwritten text.
Explanation
The Azure Computer Vision service can generate image captions and detect objects in images. These are two core capabilities of the Computer Vision API. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-describing-images
Q6
You have an AI solution that provides users with the ability to control smart devices by using verbal commands. Which two types of natural language processing (NLP) workloads does the solution use?
A
text-to-speech
B
key phrase extraction
speech-to-text
language modeling
E
translation
Correct Answers
speech-to-text
language modeling
Explanation
Controlling smart devices via verbal commands involves speech recognition (speech-to-text) and language understanding (intent extraction), both NLP capabilities in Azure AI Services. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-to-text
Q7
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?
A
Text Analytics
Azure Bot Service
C
Translator
D
Form Recognizer
Correct Answer
Azure Bot Service
Explanation
Azure Bot Service enables building conversational AI solutions that work across multiple channels such as web, Teams, and more. It integrates with the Bot Framework SDK for multi-channel deployment. Learn more: https://learn.microsoft.com/en-us/azure/bot-service/bot-service-channels-reference
Q8
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer. What should you do?
A
Split the original dataset into a dataset for training and a dataset for testing. Use the testing dataset for evaluation.
Use the original dataset for training and evaluation.
C
Split the original dataset into a dataset for features and a dataset for labels. Use the features dataset for evaluation.
D
Split the original dataset into a dataset for training and a dataset for testing. Use the training dataset for evaluation.
Correct Answer
Use the original dataset for training and evaluation.
Explanation
In Azure Machine Learning designer, after training a model you evaluate it using the Evaluate Model module. For clustering specifically, you use the Assign Data to Clusters module before evaluation. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-model
Q9
You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user. • Accept customer orders. • Retrieve support documents. • Retrieve order status updates. Which type of NLP should you use?
A
sentiment analysis
B
named entity recognition
C
translation
language modeling
Correct Answer
language modeling
Explanation
Language Understanding (LUIS) identifies user intent and extracts entities. For an NLP chatbot covering greetings, order tracking, and complaints, LUIS models intents and entities from natural language. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/luis/what-is-luis
Q10
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?
A
Export the model
Add utterances to the None intent
C
Create a prebuilt task entity
D
Create a new model
Correct Answer
Add utterances to the None intent
Explanation
In LUIS, utterances are example phrases users might say. Adding varied and representative utterances helps the model learn to recognise intents and entities accurately in real conversations. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/luis/concepts/utterances
Q11
You have a bot that identifies the brand names of products in images of supermarket shelves. Which service does the bot use?
A
AI enrichment for Azure Search capabilities
Computer Vision Image Analysis capabilities
C
Custom Vision Image Classification capabilities
D
Language Understanding capabilities
Correct Answer
Computer Vision Image Analysis capabilities
Explanation
Identifying brand names in images on supermarket shelves is an object detection task. Azure Custom Vision or the Computer Vision API can detect and classify branded products in retail images. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/overview
Q12
You run a charity event that involves posting photos of people wearing sunglasses on Twitter. You need to ensure that you only retweet photos that meet the following requirements: ✑ Include one or more faces. ✑ Contain at least one person wearing sunglasses. What should you use to analyze the images?
A
the Verify operation in the Face service
the Detect operation in the Face service
C
the Describe Image operation in the Computer Vision service
D
the Analyze Image operation in the Computer Vision service
Correct Answer
the Detect operation in the Face service
Explanation
Detecting faces in images uses the Azure Face API, which can detect, analyse, and identify human faces. It can detect attributes such as whether glasses are worn. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity
Q13
You need to build an image tagging solution for social media that tags images of your friends automatically. Which Azure Cognitive Services service should you use?
Face
B
Form Recognizer
C
Text Analytics
D
Computer Vision
Correct Answer
Face
Explanation
Automatically tagging photos of friends uses facial recognition. The Azure Face API or Custom Vision can be trained to identify specific individuals and tag them in images. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity
Q14
In which two scenarios can you use the Form Recognizer service?
Identify the retailer from a receipt
B
Translate from French to English
Extract the invoice number from an invoice
D
Find images of products in a catalog
Correct Answers
Identify the retailer from a receipt
Extract the invoice number from an invoice
Explanation
Azure AI Document Intelligence (formerly Form Recognizer) extracts data from forms and receipts. Typical scenarios include processing invoices, receipts, and identity documents automatically. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview
Q15
Which Computer Vision feature can you use to generate automatic captions for digital photographs?
A
Recognize text.
B
Identify the areas of interest.
C
Detect objects.
Describe the images.
Correct Answer
Describe the images.
Explanation
The Image Captions feature of Azure Computer Vision generates descriptive text for images automatically, making it ideal for adding alt-text captions to digital photographs. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-describing-images
AI-900 Practice Set-02
15 questions
Q1
Which Computer Vision feature can you use to generate automatic captions for digital photographs?
A
Recognize text.
B
Identify the areas of interest.
C
Detect objects.
Describe the images.
Correct Answer
Describe the images.
Explanation
The Image Captions feature of Azure Computer Vision generates descriptive text for images automatically, making it ideal for adding alt-text captions to digital photographs. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-describing-images
Q2
You have insurance claim reports that are stored as text. You need to extract key terms from the reports to generate summaries. Which type of AI workload should you use?
A
anomaly detection
natural language processing
C
computer vision
D
knowledge mining
Correct Answer
natural language processing
Explanation
Azure AI Language (Text Analytics) provides key phrase extraction that identifies the main terms and concepts in text, suitable for analysing insurance claim reports. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/overview
Q3
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 QnA Maker?
Generate the questions and answers from an existing webpage.
B
Use automated machine learning to train a model based on a file that contains the questions.
Manually enter the questions and answers.
D
Connect the bot to the Cortana channel and ask questions by using Cortana.
Import chit-chat content from a predefined data source.
Correct Answers
Generate the questions and answers from an existing webpage.
Manually enter the questions and answers.
Import chit-chat content from a predefined data source.
Explanation
Azure AI Language's custom question answering (formerly QnA Maker) can be populated using FAQ URLs, PDF documents, and structured files to build a knowledge base for a business chatbot. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/overview
Q4
You need to build an image tagging solution for social media that tags images of your friends automatically. Which Azure Cognitive Services service should you use?
Face
B
Form Recognizer
C
Language
D
Computer Vision
Correct Answer
Face
Explanation
Automatically tagging friends in photos uses the Azure Face API or Custom Vision to recognise and label individuals in social media images. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity
Q5
You have a solution that reads manuscripts in different languages and categorizes the manuscripts based on topic. Which types of natural language processing (NLP) workloads does the solution use?
A
speech recognition and entity recognition
B
speech recognition and language modeling
translation and key phrase extraction
D
translation and sentiment analysis
Correct Answer
translation and key phrase extraction
Explanation
Reading multilingual manuscripts and categorising by language uses the language detection feature of Azure AI Language (Text Analytics), which identifies the language of input text with high confidence. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/overview
Q6
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments. This is an example of which Microsoft guiding principle for responsible AI?
A
fairness
inclusiveness
C
reliability and safety
D
accountability
Correct Answer
inclusiveness
Explanation
Microsoft's Inclusiveness principle ensures AI systems empower all people, including those with disabilities such as hearing, visual, or cognitive differences. Designing for accessibility aligns with this principle. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
Q7
What are three Microsoft guiding principles for responsible AI?
A
knowledgeability
B
decisiveness
inclusiveness
fairness
E
opinionatedness
reliability and safety
Correct Answers
inclusiveness
fairness
reliability and safety
Explanation
The six Microsoft guiding principles for responsible AI are: Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, and Accountability. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
Q8
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable. This is an example of which Microsoft guiding principle for responsible AI?
transparency
B
inclusiveness
C
fairness
D
privacy and security
Correct Answer
transparency
Explanation
When designing AI for loan approval, ensuring the factors used are transparent and documented supports the Transparency principle of responsible AI so users understand how decisions are made. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
Q9
You are building an AI-based app. You need to ensure that the app uses the principles for responsible AI. Which two principles should you follow?
A
Implement an Agile software development methodology
Implement a process of AI model validation as part of the software review process
Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer
D
Prevent the disclosure of the use of AI-based algorithms for automated decision making
Correct Answers
Implement a process of AI model validation as part of the software review process
Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer
Explanation
Responsible AI principles applied to an app include Fairness (no bias in outcomes) and Reliability and Safety (the system behaves as expected). Both must be embedded during design and development. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
Q10
You have the Predicted vs. True chart shown in the following exhibit. Which type of model is the chart used to evaluate?
A
classification
regression
C
clustering
Correct Answer
regression
Explanation
A Predicted vs. True chart that plots continuous predicted values against actual values is used to evaluate a regression model, measuring how closely predictions match reality. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
Q11
You have a dataset that contains information about taxi journeys that occurred during a given period. You need to train a model to predict the fare of a taxi journey. What should you use as a feature?
A
the number of taxi journeys in the dataset
the trip distance of individual taxi journeys
C
the fare of individual taxi journeys
D
the trip ID of individual taxi journeys
Correct Answer
the trip distance of individual taxi journeys
Explanation
When taxi journey data includes distances and fares, the target variable (fare) is continuous, making this a regression problem. A regression model predicts numeric output values. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-auto-train-models
Q12
You need to predict the sea level in meters for the next 10 years. Which type of machine learning should you use?
A
classification
regression
C
clustering
Correct Answer
regression
Explanation
Predicting sea level (a continuous numeric value) over time is a regression problem. Azure Machine Learning supports regression algorithms to forecast continuous numeric outputs. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
Q13
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
Form Recognizer
B
Text Analytics
C
Language Understanding
D
Custom Vision
Correct Answer
Form Recognizer
Explanation
Azure AI Document Intelligence (formerly Form Recognizer) automatically extracts text, key/value pairs, and table data from scanned documents such as forms and invoices. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview
Q14
You use Azure Machine Learning designer to publish an inference pipeline. Which two parameters should you use to access the web service?
A
the model name
B
the training endpoint
the authentication key
the REST endpoint
Correct Answers
the authentication key
the REST endpoint
Explanation
When publishing an inference pipeline in Azure ML designer, the REST endpoint URL and the authentication key are required by client applications to call the deployed model. Learn more: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-pipelines
Q15
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types. You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person. This is an example of which type of machine learning?
A
clustering
B
regression
classification
Correct Answer
classification
Explanation
Categorising brain scan images is an image classification task. Azure Custom Vision or the Computer Vision API can train a model to classify medical images into predefined diagnostic categories. Learn more: https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/overview
Want More Practice?
These are just the free questions. Unlock the full Azure AI Fundamentals exam library with hundreds of additional questions, timed practice mode, and progress tracking.