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Azure AI Fundamentals (AI-901) study materials
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Azure AI Fundamentals (AI-901) practice questions

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Question 1Identify AI concepts and capabilities

A company wants a system that can answer customer questions about its own product manuals without a developer writing a rule for every possible question. Which AI capability does this scenario describe?

  1. Optical character recognition
  2. Rule-based expert system
  3. Natural language processing Correct answer.
  4. Robotic process automation

Explanation

Natural language processing lets a system interpret and generate human language, which is what answering free-form questions about manuals requires. Because the questions are unpredictable, a hand-written rule set is impractical, so the system must learn language patterns from data.

Question 2Identify AI concepts and capabilities

Which statement best describes the relationship between artificial intelligence, machine learning, and deep learning?

  1. Machine learning and deep learning are unrelated fields within artificial intelligence
  2. Deep learning contains machine learning, which contains artificial intelligence Correct answer.
  3. Machine learning contains artificial intelligence, which contains deep learning
  4. Artificial intelligence is a subset of machine learning that uses deep learning

Explanation

Artificial intelligence is the broadest field, machine learning is a subset of AI that learns from data, and deep learning is a subset of machine learning that uses multi-layer neural networks. The nesting runs from broadest to narrowest in that order.

Question 3Identify AI concepts and capabilities

A hospital wants a model that flags patients likely to be readmitted, using historical records that already include readmission outcomes. Which type of machine learning is this?

  1. Reinforcement learning
  2. Generative modeling
  3. Supervised learning Correct answer.
  4. Unsupervised learning

Explanation

Supervised learning trains on labeled examples, where each input is paired with a known outcome. Because the historical records already contain the readmission result, the model learns a mapping from patient features to that known label.

Question 4Identify AI concepts and capabilities

What distinguishes unsupervised learning from supervised learning?

  1. It cannot be used for clustering
  2. It can only be applied to numeric data
  3. It always requires a human to label each example
  4. It trains on data without predefined labels Correct answer.

Explanation

Unsupervised learning finds patterns in data that has no preassigned labels, such as grouping similar customers. Supervised learning, by contrast, depends on examples paired with known target values.

Question 5Identify AI concepts and capabilities

Which task is an example of computer vision?

  1. Detecting defective parts on an assembly line from camera images Correct answer.
  2. Translating a contract from English into Japanese
  3. Summarizing a long email into three bullet points
  4. Converting a speech recording into a text transcript

Explanation

Computer vision extracts information from images and video, such as spotting defects in pictures of parts. The other listed tasks act on speech or text rather than visual content.

Question 6Identify AI concepts and capabilities

A team needs to convert recorded customer phone calls into searchable text. Which AI workload should they choose?

  1. Image classification
  2. Key phrase extraction
  3. Speech recognition Correct answer.
  4. Anomaly detection

Explanation

Speech recognition transcribes spoken audio into written text, which is exactly the step needed to make calls searchable. The other options operate on images, numeric data, or already-written text.

Question 7Identify AI concepts and capabilities

Which description best matches a generative AI model?

  1. It produces new content such as text or images in response to a prompt Correct answer.
  2. It sorts records into categories defined by historical labels
  3. It measures how similar two documents are
  4. It detects unusual values in a stream of sensor readings

Explanation

Generative AI models learn patterns in training data and create new content, such as text, images, or code, from a prompt. Classification, similarity scoring, and anomaly detection are different kinds of tasks.

Question 8Identify AI concepts and capabilities

Which of these is a typical use of natural language processing rather than computer vision?

  1. Detecting faces in a photo library
  2. Extracting key phrases from support tickets Correct answer.
  3. Recognizing handwritten digits on scanned forms
  4. Counting vehicles in a traffic camera feed

Explanation

Natural language processing works with written or spoken language, so pulling key phrases out of text fits. The other tasks analyze pixels in images, which places them in computer vision.

Question 9Identify AI concepts and capabilities

A retailer wants to group customers into segments based on purchasing behavior, but has no predefined segment labels. Which approach fits best?

  1. Classification with a supervised algorithm
  2. Reinforcement learning with a reward function
  3. Regression with a supervised algorithm
  4. Clustering with an unsupervised algorithm Correct answer.

Explanation

Clustering is an unsupervised technique that discovers natural groupings in unlabeled data, which matches the goal of finding segments without predefined labels. Supervised approaches would need known target labels to train on.

Question 10Identify AI concepts and capabilities

A model must predict the selling price of a house from its size, age, and location. Which machine learning task is this?

  1. Regression Correct answer.
  2. Clustering
  3. Anomaly detection
  4. Classification

Explanation

Regression predicts a continuous numeric outcome, and a price is a continuous value. Classification instead predicts a discrete category, while clustering and anomaly detection do not predict a known numeric target.

Question 11Identify AI concepts and capabilities

An email system must decide whether each incoming message is spam or legitimate. Which task type is this?

  1. Speech synthesis
  2. Regression
  3. Clustering
  4. Binary classification Correct answer.

Explanation

Binary classification assigns each item to one of two categories, which matches the spam versus legitimate decision. Regression would produce a number, and clustering would not use the known spam labels.

Question 12Identify AI concepts and capabilities

What is the primary role of training data in supervised machine learning?

  1. It stores the final predictions produced by the trained model
  2. It defines the hardware used to serve the model in production
  3. It provides labeled examples the model uses to learn a mapping to the target Correct answer.
  4. It replaces the need to evaluate the model after training

Explanation

Training data supplies input examples paired with correct answers so the algorithm can adjust its internal parameters. The quality and representativeness of those labeled examples strongly influence how well the resulting model generalizes.

Question 13Identify AI concepts and capabilities

A safety team wants a system that learns which actions reduce accidents by receiving feedback as it interacts with a simulated environment. Which learning paradigm is described?

  1. Supervised learning
  2. Reinforcement learning Correct answer.
  3. Transfer learning
  4. Unsupervised learning

Explanation

Reinforcement learning trains an agent through rewards and penalties received as it acts in an environment, which is what the simulated feedback provides. Supervised and unsupervised learning instead rely on labeled or unlabeled datasets rather than interactive feedback.

Question 14Identify AI concepts and capabilities

A company wants to build a searchable index of scanned invoices by reading the printed text in the images. Which capability is required first?

  1. Sentiment analysis
  2. Optical character recognition Correct answer.
  3. Speech synthesis
  4. Recommendation scoring

Explanation

Optical character recognition converts text inside images into machine-readable characters, which is a prerequisite for indexing scanned invoices. Only after extraction can text search or analysis techniques be applied.

Question 15Identify AI concepts and capabilities

Which capability allows a model to determine whether a product review expresses a positive or negative opinion?

  1. Anomaly detection
  2. Object detection
  3. Sentiment analysis Correct answer.
  4. Speech recognition

Explanation

Sentiment analysis is a natural language processing task that classifies the attitude expressed in text as positive, negative, or neutral. The other options analyze images, audio, or numeric patterns rather than opinions in text.

Question 16Identify AI concepts and capabilities

A translation service must convert support articles from English to Spanish while preserving meaning. Which AI workload does this represent?

  1. Reinforcement learning
  2. Computer vision
  3. Anomaly detection
  4. Natural language processing Correct answer.

Explanation

Machine translation is a natural language processing workload because it transforms text from one human language to another. It relies on learned language patterns rather than on image analysis or reward signals.

Question 17Identify AI concepts and capabilities

Which scenario is the clearest example of an anomaly detection workload?

  1. Predicting next quarter's revenue from historical sales
  2. Grouping news articles by topic without labels
  3. Flagging a credit card transaction that differs sharply from a cardholder's normal pattern Correct answer.
  4. Generating a product description from a short prompt

Explanation

Anomaly detection identifies data points that deviate from expected patterns, such as an unusual card transaction. The other scenarios describe forecasting, clustering, and content generation respectively.

Question 18Identify AI concepts and capabilities

A developer uses a pretrained language model and supplies a short instruction describing the desired output for each request, without changing the model's weights. What is this practice called?

  1. Hyperparameter tuning
  2. Model fine-tuning
  3. Feature engineering
  4. Prompt engineering Correct answer.

Explanation

Prompt engineering is the practice of designing instructions and context so a pretrained model produces the desired response without retraining. Fine-tuning and hyperparameter tuning instead modify the model or its training configuration.

Question 19Identify AI concepts and capabilities

A team is choosing between a classification model and a regression model for a new project. Which question best determines the correct choice?

  1. How often will the model be retrained
  2. Which programming language will the team use
  3. Is the value to predict a discrete category or a continuous number Correct answer.
  4. How many rows does the training dataset contain

Explanation

The nature of the target variable decides the task: discrete categories call for classification, while continuous numeric values call for regression. Dataset size, language, and retraining cadence affect implementation but not this fundamental choice.

Question 20Identify AI concepts and capabilities

A document processing pipeline must extract names, dates, and organizations from free-form contracts. Which capability is most directly applicable?

  1. Image segmentation
  2. Regression
  3. Speech synthesis
  4. Named entity recognition Correct answer.

Explanation

Named entity recognition is a natural language processing capability that locates and classifies elements such as people, dates, and organizations in text. It is designed for exactly this kind of information extraction from unstructured documents.

Question 21Identify AI concepts and capabilities

An organization wants an AI system to summarize long reports while citing the source passages it used. Which combination of capabilities does this require?

  1. Computer vision with object detection
  2. Reinforcement learning with clustering
  3. Natural language processing with generative AI grounded in retrieved text Correct answer.
  4. Speech recognition with anomaly detection

Explanation

Summarizing and citing sources requires language understanding plus a generative model that is grounded in retrieved passages so its output can be traced. Computer vision, speech, and clustering do not address summarizing text with citations.

Question 22Identify AI concepts and capabilities

A team notices that a fraud model performs well on historical records but poorly on new transactions. Which explanation is most consistent with this observation?

  1. The model learned patterns that no longer match current transaction behavior Correct answer.
  2. The model is unsupervised and therefore cannot detect fraud
  3. The model was trained with too few input features to compute a prediction
  4. The model's predictions were stored in the training data

Explanation

A model that excels on past records but fails on new data is likely capturing patterns that have drifted away from current behavior, a form of overfitting to stale data. Retraining on recent, representative examples is the usual remedy.

Question 23Identify AI concepts and capabilities

A hospital must decide whether to deploy a model that predicts sepsis risk. Which consideration is most important beyond raw predictive accuracy?

  1. Whether the model was trained on a graphics processing unit
  2. Whether the model's errors and decisions can be explained and audited by clinicians Correct answer.
  3. Whether the model uses the newest available algorithm family
  4. Whether the training script runs faster than competing scripts

Explanation

In high-stakes settings, transparency and accountability matter as much as accuracy, because clinicians must be able to understand and justify decisions. Algorithm novelty, script speed, and training hardware are secondary to safety and trust.

Question 24Identify AI concepts and capabilities

A retailer wants to recommend products by finding customers with similar purchase histories and suggesting items those similar customers bought. Which capability underlies this approach?

  1. Regression forecasting
  2. Optical character recognition
  3. Similarity-based recommendation Correct answer.
  4. Speech synthesis

Explanation

Recommendation systems often work by measuring similarity between users or items and surfacing what comparable users chose. This relies on comparing behavioral patterns rather than on reading images, generating speech, or forecasting a numeric value.

Question 25Identify AI concepts and capabilities

A company wants a single assistant that answers questions about scanned brochures, including text embedded in images, and replies in spoken audio. Which set of capabilities must be combined?

  1. Object detection, anomaly detection, and reinforcement learning
  2. Optical character recognition, natural language processing, and speech synthesis Correct answer.
  3. Image segmentation, classification, and forecasting
  4. Speech recognition, clustering, and regression

Explanation

Reading text from scanned brochures requires optical character recognition, understanding and answering the question requires natural language processing, and producing spoken replies requires speech synthesis. The other combinations omit at least one essential capability.

Question 26Identify AI concepts and capabilities

Which statement accurately describes the relationship between artificial intelligence, machine learning, and deep learning?

  1. Deep learning, machine learning, and artificial intelligence are three unrelated fields.
  2. Machine learning is a subset of deep learning, which is a subset of artificial intelligence.
  3. Artificial intelligence is a subset of machine learning, which is a subset of deep learning.
  4. Deep learning is a subset of machine learning, which is a subset of artificial intelligence. Correct answer.

Explanation

These fields form nested categories: AI is the broad goal of building systems that perform intelligent tasks, machine learning is one approach to that goal using data-driven models, and deep learning is a specialized form of machine learning built on multilayer neural networks.

Question 27Identify AI concepts and capabilities

A system uses rules written by experts, such as 'if temperature exceeds 80 degrees, trigger a cooling alert,' without learning from data. Which approach does this describe?

  1. Reinforcement learning
  2. Expert system Correct answer.
  3. Clustering
  4. Neural network training

Explanation

An expert system encodes human knowledge as explicit if-then rules and reasons over them, so behavior comes from authored logic rather than statistical learning from examples.

Question 28Identify AI concepts and capabilities

Which task belongs to computer vision rather than natural language processing?

  1. Detecting pedestrians in frames from a traffic camera Correct answer.
  2. Translating a contract into another language
  3. Extracting key phrases from meeting notes
  4. Classifying the sentiment of a support ticket

Explanation

Computer vision deals with interpreting image and video content, so locating objects such as pedestrians in camera frames is a vision task. The other options all operate on written language.

Question 29Identify AI concepts and capabilities

Which machine learning category learns patterns from data that has no labels attached?

  1. Unsupervised learning Correct answer.
  2. Reinforcement learning
  3. Transfer learning
  4. Supervised learning

Explanation

Unsupervised learning finds structure in unlabeled data, for example by grouping similar records or reducing dimensionality, without any target answers supplied during training.

Question 30Identify AI concepts and capabilities

What does the term 'inference' mean when discussing a deployed machine learning model?

  1. Adjusting model weights using a training dataset
  2. Splitting a dataset into training and test portions
  3. Removing duplicate records before training
  4. Generating a prediction from new input data Correct answer.

Explanation

Inference is the production phase in which a trained model applies its learned parameters to fresh inputs and returns an output such as a score, label, or generated text.

Question 31Identify AI concepts and capabilities

Which capability is the primary purpose of optical character recognition?

  1. Converting speech into written transcripts
  2. Converting one spoken language into another
  3. Converting written text into spoken audio
  4. Converting text inside images into machine-readable characters Correct answer.

Explanation

Optical character recognition analyzes pixels in scanned or photographed documents and outputs editable, searchable text characters that software can process.

Question 32Identify AI concepts and capabilities

Which statement best describes a large language model?

  1. It schedules jobs across servers by optimizing resource queues.
  2. It predicts likely sequences of tokens based on patterns learned from large text corpora. Correct answer.
  3. It classifies images by comparing pixel histograms against stored templates.
  4. It stores a fixed table of answers that were entered by human editors.

Explanation

Large language models are trained on very large text collections to estimate the probability of token sequences, which lets them complete prompts, answer questions, and generate fluent language.

Question 33Identify AI concepts and capabilities

Which scenario is an example of a regression task?

  1. Grouping news articles by topic without labels
  2. Predicting the number of minutes until a delivery arrives Correct answer.
  3. Deciding whether a photo contains a cat
  4. Assigning each support ticket to one of five priority queues

Explanation

Regression predicts a continuous numeric quantity, and a duration in minutes is such a quantity, so estimating arrival time is regression.

Question 34Identify AI concepts and capabilities

A call center wants to measure how frequently customers mention competitor brands during conversations. Which AI workload should be applied to the audio?

  1. Recommendation followed by ranking
  2. Anomaly detection followed by regression
  3. Image classification followed by object detection
  4. Speech recognition followed by keyword extraction Correct answer.

Explanation

The audio must first be transcribed into text by speech recognition, and then text analytics such as keyword or entity extraction can identify brand mentions in the transcript.

Question 35Identify AI concepts and capabilities

Which pair correctly matches an AI workload with its typical input type?

  1. Natural language processing with written text Correct answer.
  2. Computer vision with audio waveforms
  3. Anomaly detection with translated subtitles only
  4. Speech recognition with scanned photographs

Explanation

Natural language processing consumes text such as documents, messages, and transcripts, and produces linguistic outputs like sentiment, entities, or translations.

Question 36Identify AI concepts and capabilities

An insurance company wants to automatically read the handwritten policy number on a photographed claim form. Which capability should be used?

  1. Speaker diarization
  2. Speech synthesis
  3. Handwriting recognition Correct answer.
  4. Text-to-speech translation

Explanation

Handwriting recognition extends character recognition to handwritten strokes, converting the photographed script into digital text that can be validated against policy records.

Question 37Identify AI concepts and capabilities

A media company wants to index video content by searching for specific spoken words. Which combination of capabilities is required?

  1. Sentiment analysis and clustering
  2. Object detection and image captioning
  3. Face detection and OCR
  4. Speech recognition and text search Correct answer.

Explanation

Spoken words must be transcribed from the audio track by speech recognition, and the resulting transcript is then indexed so that text search can locate the words.

Question 38Identify AI concepts and capabilities

Which description distinguishes a discriminative task from a generative task?

  1. A discriminative task assigns labels or values to inputs, whereas a generative task creates new content. Correct answer.
  2. A discriminative task requires no data, whereas a generative task requires labeled data.
  3. A discriminative task produces new content, whereas a generative task sorts inputs into categories.
  4. A discriminative task always uses images, whereas a generative task always uses audio.

Explanation

Discriminative models draw boundaries that map an input to a label or numeric value, such as spam or not spam, while generative models learn data distributions well enough to produce new text, images, or audio.

Question 39Identify AI concepts and capabilities

A team wants to build a knowledge base from thousands of internal wiki pages so employees can ask questions in plain language. Which AI capability is central to this solution?

  1. Question answering over a text corpus Correct answer.
  2. Image segmentation
  3. Robotic process automation
  4. Time series forecasting

Explanation

Question answering retrieves or reasons over a body of documents and returns relevant answers to natural language questions, which matches the goal of querying wiki content conversationally.

Question 40Identify AI concepts and capabilities

Which scenario best illustrates reinforcement learning in practice?

  1. A robot that improves its walking gait by receiving rewards for forward progress Correct answer.
  2. A model that predicts tomorrow's temperature from past readings
  3. A model that labels scanned receipts using thousands of pre-labeled examples
  4. A model that groups website visitors by browsing similarity

Explanation

Reinforcement learning trains an agent through trial and error, using rewards and penalties from the environment to shape a policy that maximizes long-term reward, as in learning to walk.

Question 41Identify AI concepts and capabilities

A bank wants to flag transactions that deviate sharply from a customer's normal spending pattern without having labeled fraud examples. Which approach fits best?

  1. Supervised binary classification on labeled fraud records
  2. Unsupervised anomaly detection on transaction features Correct answer.
  3. Regression on the transaction amount
  4. Optical character recognition on statements

Explanation

Because no labeled fraud examples are available, the system must learn what normal behavior looks like and flag deviations, which is the defining pattern of unsupervised anomaly detection.

Question 42Identify AI concepts and capabilities

A travel company wants to identify which named cities and dates appear in free-form customer emails. Which natural language processing capability applies?

  1. Entity extraction Correct answer.
  2. Speech synthesis
  3. Image captioning
  4. Text summarization

Explanation

Entity extraction, also called named entity recognition, locates and classifies mentions of people, places, dates, and organizations within unstructured text such as emails.

Question 43Identify AI concepts and capabilities

Which statement about responsible AI principles is accurate?

  1. Transparency means publishing the model weights for every commercial system.
  2. Privacy means collecting all available data to improve accuracy.
  3. Accountability means people remain answerable for the behavior of AI systems they deploy. Correct answer.
  4. Fairness requires that a model never makes an incorrect prediction.

Explanation

Accountability holds the humans and organizations that design and operate an AI system responsible for its outcomes, including having governance and oversight in place.

Question 44Identify AI concepts and capabilities

A team must choose between training a custom model and using a pretrained foundation model for a text summarization feature. Which consideration is most directly relevant to that choice?

  1. Whether the office network uses wired or wireless connections
  2. Whether sufficient labeled data and budget exist for custom training Correct answer.
  3. Whether the team prefers a particular programming font
  4. Whether the building has a backup generator

Explanation

Custom training demands substantial labeled data, compute, and time, whereas a pretrained model can be adapted with far less. Comparing those resource requirements is the central decision factor.

Question 45Identify AI concepts and capabilities

A model trained to classify product photos performs poorly on photos taken with a different camera in a new warehouse. Which concept does this illustrate?

  1. A distribution shift between training and deployment data Correct answer.
  2. Model overfitting to the training labels only
  3. A syntax error in the inference code
  4. Insufficient graphics memory at inference time

Explanation

The deployment data differs statistically from the training data because of lighting, resolution, and camera characteristics, which is a distribution shift that degrades model accuracy.

Question 46Identify AI concepts and capabilities

An organization wants an AI assistant to answer questions using only its internal policy documents and to refuse when the answer is absent. Which design choice most directly supports this requirement?

  1. Increasing the temperature setting of the model
  2. Grounding responses in retrieved document passages Correct answer.
  3. Removing all metadata from the documents
  4. Training the model on public internet text only

Explanation

Retrieval-augmented grounding supplies the model with relevant passages from the approved corpus at query time, so answers can be traced to those sources and unsupported questions can be declined.

Question 47Identify AI concepts and capabilities

A computer vision model detects defects on an assembly line but raises many false alarms, causing operators to ignore alerts. Which analysis best addresses the core problem?

  1. Increase the image resolution of the cameras and assume accuracy will improve.
  2. Retrain the model using only images that contain defects.
  3. Replace the model with a regression that predicts defect size.
  4. Evaluate precision and recall and tune the decision threshold to balance false positives against missed defects. Correct answer.

Explanation

Frequent false alarms indicate low precision at the current threshold. Examining the precision-recall tradeoff and adjusting the threshold, possibly with better training data, directly targets the alarm behavior.

Question 48Identify AI concepts and capabilities

A team plans to combine speech, vision, and language models into one assistant. Which statement about multimodal AI is most accurate?

  1. Multimodal models eliminate the need for any training data.
  2. Multimodal models can only accept text and cannot process images.
  3. Multimodal models can process more than one data type, such as text and images, within a single system. Correct answer.
  4. Multimodal models require all inputs to be converted to audio before processing.

Explanation

Multimodal systems accept and relate multiple modalities, for example pairing image understanding with text generation, which enables tasks such as describing a photo or answering questions about a chart.

Question 49Identify AI concepts and capabilities

A hospital deploys a model that recommends patient follow-up intervals. Clinicians cannot explain why specific recommendations appear. Which responsible AI concern is most directly implicated?

  1. Cost of graphics processing hardware
  2. Choice of programming language for the API
  3. Network latency between data centers
  4. Interpretability and transparency of model decisions Correct answer.

Explanation

When stakeholders cannot understand the basis of consequential recommendations, interpretability and transparency are at stake, and techniques such as feature attribution or inherently explainable models may be needed.

Question 50Identify AI concepts and capabilities

Which type of AI workload is designed to assign a label to an input such as an email or an image?

  1. Speech synthesis
  2. Classification Correct answer.
  3. Regression
  4. Clustering

Explanation

Classification predicts a discrete category from a fixed set of labels, such as spam versus not spam. The model learns boundaries between predefined classes during training and outputs the most likely class for new input.