AI-900 Exam Dumps - Microsoft Certified: Azure AI Fundamentals Questions and Answers

Within Azure AI Document Intelligence (formerly Form Recognizer), the Contract prebuilt model is designed to extract key information from legal and business contracts, including parties, jurisdictions, dates, and terms. According to Microsoft Learn, this prebuilt model identifies structured entities such as contracting parties, effective dates, governing jurisdictions, and termination clauses.

Layout (B) extracts text, tables, and structure but does not identify semantic information such as parties or jurisdictions.

General document (C) extracts key-value pairs and entities but lacks domain-specific contract analysis.

Read (D) performs OCR (optical character recognition) to extract raw text but not contextual metadata.

Thus, when the requirement is to extract parties and jurisdictions from a legal document, the Contract model is the correct Azure AI Document Intelligence choice.

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Azure Cognitive Services documentation, the Custom Vision service is a specialized computer vision tool that allows users to build, train, and deploy custom image classification and object detection models. It is part of the Azure Cognitive Services suite, designed for scenarios where pre-built Computer Vision models do not meet specific business requirements.

“The Custom Vision service can be used to detect objects in an image.” → YesThis statement is true. The Custom Vision service supports object detection, enabling the model to identify and locate multiple objects within a single image using bounding boxes. For example, it can locate cars, products, or animals in photos.

“The Custom Vision service requires that you provide your own data to train the model.” → YesThis statement is true. Unlike pre-trained models such as the standard Computer Vision API, the Custom Vision service requires users to upload and label their own images. The system uses this labeled dataset to train a model specific to the user’s scenario, improving accuracy for custom use cases.

“The Custom Vision service can be used to analyze video files.” → NoThis statement is false. The Custom Vision service works only with static images, not videos. To analyze video files, Azure provides Video Indexer and Azure Media Services, which are designed for extracting insights from moving visual content.

In Azure Machine Learning designer, the Split Data module is specifically designed to divide a dataset into training and validation (or testing) subsets. The AI-900 study guide and the Microsoft Learn module “Split data for training and evaluation” explain that this module allows users to control how data is partitioned, ensuring that models are trained on one portion of the data and tested on unseen data to assess performance.

By default, the Split Data module uses a 70/30 or 80/20 ratio, meaning 70–80% of the data is used for training and the remaining 20–30% for validation or testing. This ensures the model’s generalizability and prevents overfitting.

The other options serve different purposes:

A. Select Columns in Dataset: Used to choose specific columns or features from a dataset.

B. Add Rows: Combines multiple datasets vertically.

D. Join Data: Combines datasets horizontally based on a common key.

Only Split Data performs the function of dividing data into training and validation subsets.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of Natural Language Processing (NLP) workloads and services,” the Azure Cognitive Service for Language – Question Answering capability is designed to allow applications to respond to user questions using information from a prebuilt or custom knowledge base. It relies on Natural Language Processing (NLP) to match user queries to the most relevant answers but does not directly execute queries against databases or infer user intent.

“You can use Language Service’s question answering to query an Azure SQL database.” → NOThe Question Answering feature does not connect directly to or query structured databases such as Azure SQL. Instead, it retrieves answers from unstructured or semi-structured content (FAQs, manuals, documents). Querying SQL databases would require traditional database access, not a cognitive service.

“You should use Language Service’s question answering when you want a knowledge base to provide the same answer to different users who submit similar questions.” → YESThis statement is correct and aligns exactly with Microsoft’s official documentation. Question Answering enables organizations to create a knowledge base that can automatically answer repeated or similar customer queries using natural language understanding. For instance, two users asking “How do I reset my password?” and “Can you help me change my password?” would receive the same predefined response.

“Language Service’s question answering can determine the intent of a user utterance.” → NODetermining user intent is handled by Language Understanding (LUIS) or Conversational Language Understanding, not by Question Answering. While both belong to the Language Service, Question Answering focuses on retrieving relevant answers, whereas LUIS focuses on intent detection and entity extraction.

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.

Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.

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.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of computer vision workloads”, Optical Character Recognition (OCR) is a core capability within the computer vision domain that enables systems to detect and extract text from images or documents. OCR technology can identify printed or handwritten characters in photographs, scanned documents, or camera feeds, and convert them into machine-readable text.

In this scenario, the task is to read the numbers on runners’ shirts in race photos. These numbers are textual or numeric characters embedded within images. OCR is specifically designed for this purpose — to locate and recognize characters within visual data and convert them into usable text. Once extracted, those numbers can be cross-referenced with a database to identify each runner.

Let’s analyze why the other options are incorrect:

A. Facial recognition focuses on identifying individuals based on unique facial features, not reading text or numbers.

C. Semantic segmentation classifies each pixel of an image into categories (for example, separating road, sky, and people), but it doesn’t read text.

D. Object detection identifies and locates objects within an image (such as detecting people or vehicles) but does not extract readable text or numbers.

Therefore, since the task involves reading textual or numeric content from an image, the appropriate type of computer vision to use is Optical Character Recognition (OCR).

This question tests the understanding of features and labels in machine learning, a core concept covered in the Microsoft Azure AI Fundamentals (AI-900) syllabus under “Describe fundamental principles of machine learning on Azure.”

In supervised machine learning, data is divided into features (inputs) and labels (outputs).

Features are the independent variables — measurable properties or characteristics used by the model to make predictions.

Labels are the dependent variables — the target outcome the model is trained to predict.

From the provided dataset, the goal of the Azure Machine Learning model is to predict product quality (Pass or Fail). Therefore:

Mass (kg) is a feature – Yes“Mass (kg)” represents an input variable used by the model to learn patterns that influence product quality. It helps the algorithm understand how variations in mass might correlate with passing or failing the quality test. Thus, it is correctly classified as a feature.

Quality Test is a label – YesThe “Quality Test” column indicates the outcome of the manufacturing process, marked as either Pass or Fail. This is the target the model tries to predict during training. In Azure ML terminology, this column is the label, as it represents the dependent variable.

Temperature (C) is a label – No“Temperature (C)” is an input that helps the model determine quality outcomes, not the outcome itself. It influences the quality result but is not the value being predicted. Therefore, temperature is another feature, not a label.

In conclusion, per Microsoft Learn and AI-900 study materials, features are measurable inputs (like mass and temperature), while the label is the target output (like the quality test result).

This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore natural language processing.” NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language — both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.

Monitoring online service reviews for profanities → YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: “Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content.” Hence, this task is correctly classified as NLP.

Identifying brand logos in an image → NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.

Monitoring public news sites for negative mentions of a product → YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.

In Azure Machine Learning Designer, the Dataset output visualization feature is specifically used to explore and understand the distribution of values in potential feature columns before model training begins. This capability is critical for data exploration and preprocessing, two essential stages of the machine learning pipeline described in the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning learning paths.

When a dataset is imported into Azure Machine Learning Designer, users can right-click on the dataset output port and select “Visualize”. This launches the dataset visualization pane, which provides detailed statistical summaries for each column, including:

Data type (numeric, categorical, string, Boolean)

Minimum, maximum, mean, and standard deviation values for numeric columns

Frequency counts and distinct values for categorical columns

Missing value counts

This visual inspection helps determine which columns should be used as features, which might need normalization or encoding, and which contain missing or irrelevant data. It is a vital step in ensuring the dataset is clean and ready for model training.

Let’s examine why other options are incorrect:

Normalize Data module is used to scale numeric data, not to visualize distributions.

Select Columns in Dataset module is used to include or exclude columns, not to analyze them.

Evaluation results visualization feature is used after model training to interpret performance metrics like accuracy or recall, not data distributions.

Therefore, based on official Microsoft documentation and AI-900 study materials, to explore the distribution of values in potential feature columns, you use the Dataset output visualization feature in Azure Machine Learning Designer.