Microsoft · Free Practice Questions · Last reviewed May 2026
30real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
A bank is developing an AI system to automatically approve personal loans. To ensure the system does not discriminate against any group of applicants, which Microsoft responsible AI principle should the bank primarily focus on?
Accountability
Inclusiveness
Fairness
Fairness is the principle that AI systems should treat all people equitably and avoid bias, making it the correct focus for preventing discrimination in loan approvals.
Reliability and Safety
A manufacturing company uses an AI system to predict when machines will need maintenance. The system must work correctly under varying factory floor conditions such as temperature changes and noise levels. Which Microsoft responsible AI principle is most directly focused on ensuring the system performs reliably in these different conditions?
Fairness
Reliability & Safety
This principle directly ensures that AI systems perform consistently and safely across a range of conditions, which matches the requirement for reliable operation in different factory environments.
Privacy & Security
Inclusiveness
A data scientist is training a credit risk model and wants to use Azure Machine Learning's Responsible AI dashboard to identify if the model is biased against a certain demographic group. Which component of the dashboard should they use to evaluate this?
Model Interpretability
Model Fairness Assessment
This component analyzes model predictions across predefined sensitive groups to identify and measure unfair bias.
Error Analysis
Data Balance Analysis
A healthcare start-up proposes a fully automated AI system to diagnose patients from medical scans without any human doctor review. They claim the system is 99% accurate. According to Microsoft's responsible AI principles, which principle is most directly violated by removing human oversight from this critical decision-making process?
Fairness
Reliability and safety
Transparency
Accountability
Accountability demands that AI systems are designed with appropriate human oversight to ensure responsible use and to handle edge cases. Fully automating diagnosis removes human accountability.
A financial services company uses an AI system to recommend personalized investment portfolios. A customer requests an explanation of why a particular investment was recommended. Which Microsoft responsible AI principle is primarily focused on ensuring the company can provide this explanation?
Accountability
Transparency
Transparency requires that AI systems are understandable and that users can obtain meaningful explanations for decisions, which is exactly what the customer is asking for.
Fairness
Reliability
A healthcare organization is developing an AI system to recommend treatment plans for patients based on their medical history. According to Microsoft's responsible AI principles, which principle is most directly concerned with ensuring that the system protects patients' health data from unauthorized access or misuse?
Privacy and security
This principle requires AI systems to respect privacy, store data securely, and protect it from unauthorized access or misuse, which aligns directly with protecting patient data.
Transparency
Fairness
Reliability and safety
Want more Describe Artificial Intelligence workloads and considerations practice?
Practice this domainA 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?
Classification
Regression
Regression predicts a continuous numeric value, which is exactly what is needed for predicting house price.
Clustering
Anomaly Detection
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?
A. Precision
B. Recall
Correct. Recall = true positives / (true positives + false negatives) = 90 / (90 + 10) = 0.9, exactly the proportion of actual spam correctly identified.
C. F1 Score
D. Accuracy
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?
Normalization
Principal Component Analysis (PCA)
PCA summarizes data by creating new uncorrelated variables (principal components) that capture most of the variance, effectively reducing dimensionality.
One-hot encoding
Regression analysis
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?
Supervised learning - Classification
Unsupervised learning - Clustering
Clustering is an unsupervised learning technique that groups similar data points together based on features, without needing labels. This fits the scenario of discovering natural customer segments from purchasing patterns.
Reinforcement learning
Supervised learning - Regression
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?
Supervised regression
Supervised classification
Classification is used when the target variable is a category, and the data is labeled. Here, the output is one of two classes – readmitted or not readmitted.
Unsupervised clustering
Reinforcement learning
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?
Overfitting
Overfitting occurs when the model learns the training data too well, capturing noise and making it perform poorly on new, unseen data, as shown by the large gap between training and validation performance.
Underfitting
High bias
Insufficient training data
Want more Describe fundamental principles of machine learning on Azure practice?
Practice this domainA transportation company wants to automatically identify whether an image contains a car, a truck, or a motorcycle. The system should output a single label for the entire image. Which computer vision capability in Azure should they use?
Object detection
Image classification
Image classification assigns one or more labels to the entire image, matching the requirement to identify the type of vehicle shown.
Optical Character Recognition (OCR)
Semantic segmentation
A manufacturing company wants to use Azure AI to detect surface defects on metal parts. The team has a small set of labeled images of defective and non-defective parts, and images will be taken under various lighting conditions and angles. They need a solution that can leverage a pre-trained model and adapt it to their specific defect types with minimal new training data. Which approach should they take?
A. Use Custom Vision to train a classification or object detection model with transfer learning
Correct. Custom Vision uses transfer learning from pre-trained models, enabling effective training with a small dataset to detect specific defects.
B. Use the Optical Character Recognition (OCR) API
C. Use the Describe Image API (Image Captioning)
D. Use the Face API
A logistics company receives thousands of handwritten shipping labels each day. They want to use Azure AI to automatically read the handwritten addresses and convert them into digital text. Which Azure Cognitive Services capability should they use?
Image classification
Optical character recognition (OCR)
OCR extracts text from images, including handwritten content, and is ideal for this scenario.
Object detection
Face detection
A logistics warehouse uses a conveyor belt system to move packages. They need to automatically read the alphanumeric serial numbers printed on labels attached to each box. The labels may have different fonts and be somewhat dusty. Which Azure Computer Vision feature should they use?
Image Classification
Optical Character Recognition (OCR) using the Read API
The Read API extracts text from images and is robust to various fonts and image quality issues. It can return the serial number as a string, making it ideal for this use case.
Object Detection
Image Analysis (captioning and tagging)
A retail company wants to build a system that can verify the identity of customers by comparing their live photo with an uploaded government-issued ID photo. Which Azure Computer Vision service should they use to perform the face comparison?
Azure Computer Vision - Image Analysis
Azure Face API
Face API offers face verification, which checks if a live photo matches a reference photo (e.g., the ID photo) by comparing facial features.
Azure Custom Vision
Azure Form Recognizer
A retail warehouse uses a camera system to locate and count boxes on shelves. The system needs to output the exact positions of each box by drawing a rectangular frame around it in the image. Which Azure Computer Vision capability should they use?
Object detection
Object detection finds objects and returns their bounding boxes, which is precisely what is needed to locate and frame each box in an image.
Image classification
Semantic segmentation
Optical Character Recognition (OCR)
Want more Describe features of computer vision workloads on Azure practice?
Practice this domainA healthcare organization needs to extract specific data elements (such as patient names, medication dosages, and dates) from unstructured doctors' notes. Which Azure Cognitive Service is best suited for this task?
Language Understanding (LUIS)
Text Analytics
Text Analytics includes Named Entity Recognition, which can extract predefined categories of entities (e.g., Person, Date, Quantity) from unstructured text, making it ideal for this task.
Translator Text
Speech
A hospital wants to create a system that can transcribe doctor-patient conversations in real time and also extract medical conditions, medications, and dosages from the transcribed text. Which combination of Azure AI services should they use?
Speech to Text and Text Analytics API (standard)
Speech to Text and Text Analytics for Health
Speech to Text provides real-time transcription, and Text Analytics for Health is specifically designed to extract medical concepts from clinical text.
Translator Text and Language Understanding (LUIS)
Speaker Recognition and Question Answering
A customer service team wants to build an Azure AI-powered bot that can understand the intent behind customer messages. For example, the bot should recognize that 'I want to return my shoes' maps to a 'ReturnItem' intent, and 'Where is my order?' maps to 'TrackOrder'. Which Azure service provides pre-built models specifically for intent recognition?
Language Understanding (LUIS)
LUIS (part of Azure Language service) is designed for intent recognition and entity extraction from conversational utterances. It provides pre-built models for common intents.
Text Analytics
Translator Text
Speech-to-text
An online news platform receives thousands of articles daily. The editors want to automatically identify the most important topics discussed in each article to help with content categorization. Which Azure Text Analytics capability should they use?
Sentiment Analysis
Key Phrase Extraction
Key phrase extraction returns a list of the most important phrases or topics in the text. This directly matches the requirement to identify important topics from articles.
Named Entity Recognition
Language Detection
A company's HR department wants to create a self-service bot that can answer employee questions about company policies. They have a collection of policy documents in PDF format. Which Azure AI Language feature should they use to ingest these documents and enable the bot to provide answers based on them?
Sentiment Analysis
Key Phrase Extraction
Custom Question Answering
Custom Question Answering allows you to build a knowledge base by ingesting documents (e.g., PDFs) and then answers questions by extracting relevant passages from that knowledge base.
Language Detection
A retail company collects thousands of customer reviews. They want to automatically extract frequently mentioned aspects (e.g., 'battery life', 'customer service', 'price') to understand common topics. Which Azure AI Language capability should they use?
Sentiment analysis
Key phrase extraction
Key phrase extraction identifies the main topics, opinions, and themes in text, making it ideal for extracting frequently mentioned aspects like product features.
Named entity recognition
Language detection
Want more Describe features of Natural Language Processing workloads on Azure practice?
Practice this domainA marketing team wants to use Azure AI to automatically generate unique product descriptions for thousands of items in an e-commerce catalog based on a few keywords provided by the inventory team. Which Azure service should they use?
A. Azure OpenAI Service
Correct. Azure OpenAI Service offers powerful generative language models (e.g., GPT-4) that can produce text from prompts, perfectly suited for generating product descriptions from keywords.
B. Azure Computer Vision
C. Language Understanding (LUIS)
D. Azure Machine Learning
A company is developing a chatbot that can both answer customer questions in natural language and create images on demand (e.g., 'Generate a picture of a product prototype'). Which combination of Azure generative AI models should they integrate?
A. GPT-4 for text and DALL-E for images
Correct. GPT-4 handles conversational text, and DALL-E generates images from text prompts, making this the ideal combination for the described chatbot.
B. GPT-3 for text and Custom Vision for images
C. BERT for text and OCR for images
D. Language Understanding (LUIS) and Face API
A game development company uses Azure OpenAI Service to automatically generate in-game dialog for non-player characters (NPCs) based on character profiles. They need to ensure the generated text does not contain offensive language or harmful suggestions. Which Azure OpenAI Service feature should they configure to prevent this?
Content filters
Azure OpenAI Service includes configurable content filters that can block harmful, offensive, or inappropriate content in generated outputs.
Model deployment
Token limit
Prompt engineering
A company uses Azure OpenAI Service to generate marketing copy for social media posts. They want to prevent the model from producing content that contains offensive language, harmful stereotypes, or violent themes that go against their brand guidelines. Which feature should the company configure within Azure OpenAI Service?
Fine-tuning the model with a custom dataset
Configuring the content filtering (responsible AI filters)
Azure OpenAI’s content filtering system is a built-in safeguard that automatically screens inputs and outputs for categories like hate, violence, sexual content, and self-harm. Companies can configure severity levels to prevent undesirable content from being generated.
Increasing the token limit per response
Using prompt engineering techniques
A company uses Azure OpenAI Service to power a chat-based support assistant. They have extensive knowledge base documents that contain the correct information. The company wants the assistant to answer questions solely based on the provided documents and avoid generating plausible-sounding but incorrect information. Which approach should they implement to minimize the risk of such fabrications?
Retrieval Augmented Generation (RAG) — provide relevant document excerpts as context in the prompt
RAG supplies the model with pertinent knowledge from the documents at query time, ensuring the answer is grounded in the provided content and significantly reducing hallucinations.
Increase the temperature parameter to 1.0 to force more creative responses
Fine-tune the model on the knowledge base documents using supervised learning
Use prompt engineering with a system message that tells the model to never make up facts
A marketing team uses Azure OpenAI Service to generate multiple variations of a product description from a single prompt. They want the generated descriptions to be more creative and diverse, rather than repetitive. Which parameter should they increase to achieve this?
Temperature
Increasing temperature makes the model more likely to choose less likely tokens, leading to more creative and diverse outputs.
Max tokens
Top probability
Frequency penalty
Want more Describe features of generative AI workloads on Azure practice?
Practice this domainThe AI-900 exam has 50 questions and must be completed in 60 minutes. The passing score is 700/1000.
Conceptual questions on AI fundamentals, machine learning, computer vision, natural language processing, and Azure AI services.
The exam covers 5 domains: Describe Artificial Intelligence workloads and considerations, Describe fundamental principles of machine learning on Azure, Describe features of computer vision workloads on Azure, Describe features of Natural Language Processing workloads on Azure, Describe features of generative AI workloads on Azure. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Microsoft AI-900 exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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