Free Practice Questions for Microsoft AI-900 Exam

In Natural Language Processing (NLP), entities are pieces of information extracted from text, such as names, locations, or phone numbers. According to the Microsoft Learn module “Explore natural language processing in Azure,” Azure’s Language Understanding (LUIS) supports several entity types:

Machine-learned entities – Automatically learned based on context in training data.

List entities – Used for predefined, limited sets of values (e.g., colors or product names).

Pattern.any entities – Capture flexible, unstructured phrases in user input.

Regular expression entities – Use regex patterns to match specific data formats such as phone numbers, postal codes, or dates.

A regular expression is ideal for recognizing phone numbers because phone numbers follow specific numeric or symbol-based patterns (e.g., (555)-123-4567 or +1 212 555 0199). By defining a regex pattern, the AI model can accurately extract phone numbers regardless of text context.

The Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn’s module “Translate text with the Translator service”, this service provides two main capabilities: text translation and automatic language detection.

“You can use the Translator service to translate text between languages.” → YesThis statement is true. The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.

“You can use the Translator service to detect the language of a given text.” → YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.

“You can use the Translator service to transcribe audible speech into text.” → NoThis statement is false. Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech-to-Text API, not the Translator service.

Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.

The correct answers are B and D.

Speech recognition, part of Azure’s Speech service, converts spoken audio into written text. It is a core feature of Azure Cognitive Services for speech-to-text scenarios.

Providing closed captions for recorded or live videos (B) – This is a typical application of speech recognition. The AI system listens to audio content from a video and generates real-time or post-event captions. Azure’s Speech-to-Text API is frequently used in broadcasting and video platforms to improve accessibility and searchability.

Creating a transcript of a telephone call or meeting (D) – Another common use case is automated transcription. The Speech service can process real-time audio streams (such as meetings or calls) and produce accurate text transcripts. This is widely used in customer service, call analytics, and meeting documentation.

The incorrect options are:

A. an in-car system that reads text messages aloud – This uses Text-to-Speech, not speech recognition.

C. creating an automated public address system for a train station – This also uses Text-to-Speech, since it generates spoken output from text.

Therefore, scenarios that convert spoken words into text correctly represent speech recognition, making B and D the right answers.

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision encompasses several key capabilities: image classification, object detection, facial detection, and optical character recognition (OCR). When the task requires counting the number of distinct objects (in this case, animals) in an image, object detection is the correct type of vision model.

Object detection not only classifies what is present in an image but also identifies where each object appears by drawing bounding boxes around them. Each detected object is individually labeled, enabling the system to count or track them accurately. In contrast, image classification would only tell you the overall category (e.g., “This is an image of animals”) without counting how many animals are present.

Facial detection focuses solely on identifying human faces, while OCR extracts text from images — neither applies here.

Therefore, the AI-900 official learning modules confirm that object detection is the appropriate solution for identifying and counting multiple entities within an image.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common machine learning types”, the three main machine learning model types differ by their purpose and the kind of data they use — whether supervised (using labeled data) or unsupervised (using unlabeled data).

Regression → A supervised machine learning model used to predict numeric values.Regression is a type of supervised learning that predicts continuous numerical outcomes. It learns the relationship between input features (independent variables) and a continuous target variable (dependent variable). Examples include predicting house prices, sales revenue, or temperature. The AI-900 curriculum emphasizes regression for “predicting numeric values based on known data,” using algorithms such as linear regression or decision tree regression.

Classification → A supervised machine learning model used to predict categories.Classification is also a supervised learning technique, but it predicts discrete outcomes (classes) instead of continuous values. It assigns input data to one or more categories based on learned patterns. Typical examples include spam detection (spam vs. not spam), sentiment analysis (positive, neutral, negative), or predicting loan approval (approved/denied). The AI-900 study materials describe classification as “predicting a category or label for new observations.”

Clustering → An unsupervised machine learning model used to group similar entities based on features.Clustering is an unsupervised learning approach, meaning it works on unlabeled data. It automatically identifies patterns and groups similar data points into clusters based on shared characteristics. Examples include customer segmentation (grouping customers by behavior) and grouping similar documents. The AI-900 learning module explains clustering as “discovering natural groupings in data without predefined labels.”

Thus, per Microsoft’s official AI-900 learning objectives:

Regression → Predicts numeric/continuous values.

Classification → Predicts categories/labels.

Clustering → Groups similar entities (unsupervised).

✅ Final Verified Configuration:

Regression → Predict numeric values

Classification → Predict categories

Clustering → Group similar entities based on features

In Microsoft’s Responsible AI principles, Fairness ensures that AI systems treat all individuals and groups equitably and make unbiased decisions. The AI-900 study guide explicitly states that fairness is violated when an AI model produces outcomes that systematically favor one group over another — such as preferring a particular gender, race, or age group.

In this scenario, a loan approval system shows gender bias, meaning it approves or rejects applications differently based on gender. This directly contradicts the fairness principle, as the AI system must make decisions solely based on relevant financial attributes (e.g., credit score, income) rather than personal characteristics.

Other principles explained in the AI-900 course include:

Accountability: Ensures human oversight and responsibility.

Transparency: Ensures users understand how decisions are made.

Reliability and Safety: Ensures consistent and accurate operation.

Since gender bias undermines equitable treatment, the principle violated is Fairness.

Statements

Yes

No

A webchat bot can interact with users visiting a website.

Yes

Automatically generating captions for pre-recorded videos is an example of natural language processing.

No

A smart device in the home that responds to questions such as “What will the weather be like today?” is an example of natural language processing.

Yes

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn modules on AI workloads, each of these statements maps to a distinct area of artificial intelligence — namely Conversational AI, Speech AI, and Natural Language Processing (NLP).

“A webchat bot can interact with users visiting a website.” – YesThis is true. A webchat bot represents an example of Conversational AI. It leverages natural language understanding (NLU) to interpret user input and generate appropriate responses. These bots can be created using Azure services such as Azure AI Bot Service and Language Understanding (LUIS). They enable automated interactions with users through text-based communication on websites, applications, or messaging platforms.

“Automatically generating captions for pre-recorded videos is an example of natural language processing.” – NoThis is false. Generating captions from audio involves speech recognition, not NLP. Specifically, it uses speech-to-text technology to transcribe spoken words into written text. This function is typically performed by Azure’s Speech service, which is part of the Speech AI workload, not the language-processing workload.

“A smart device in the home that responds to questions such as ‘What will the weather be like today?’ is an example of natural language processing.” – YesThis is true. Smart assistants like Alexa or Cortana use NLP to interpret spoken queries, extract meaning, and generate appropriate responses. NLP allows these devices to understand human language, retrieve relevant information, and respond conversationally.

The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.

Example:

The provided data set contains the following columns:

vendor_id: The ID of the taxi vendor is a feature.

rate_code: The rate type of the taxi trip is a feature.

passenger_count: The number of passengers on the trip is a feature.

trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don ' t know how long the trip would take. Thus, the trip time is not a feature and you ' ll exclude this column from the model.

trip_distance: The distance of the trip is a feature.

payment_type: The payment method (cash or credit card) is a feature.

fare_amount: The total taxi fare paid is the label.

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Azure Machine Learning documentation, Automated Machine Learning (AutoML) is a feature designed to help users build, train, and tune machine learning models automatically without requiring deep knowledge of programming or data science.

First Statement: “Automated machine learning provides you with the ability to include custom Python scripts in a training pipeline.”This is False (No). AutoML automates the model selection and tuning process but does not allow the inclusion of custom Python scripts within its workflow. Custom Python integration is supported in Azure Machine Learning designer pipelines or SDK-based training, not in AutoML.

Second Statement: “Automated machine learning implements machine learning solutions without the need for programming experience.”This is True (Yes). One of AutoML’s core benefits is that it enables non-programmers to train and evaluate models by simply selecting data, choosing a target column, and letting Azure automatically test algorithms and hyperparameters. This aligns with Microsoft’s AI-900 objective to democratize AI development.

Third Statement: “Automated machine learning provides you with the ability to visually connect datasets and modules on an interactive canvas.”This is False (No). That feature belongs to Azure Machine Learning Designer, not AutoML. The designer offers a drag-and-drop visual interface for connecting datasets and modules, whereas AutoML provides a wizard-driven approach focused on automation.

The service that can complete a paragraph based on a sentence is Azure OpenAI. According to Microsoft Learn’s AI-900 study guide, Azure OpenAI provides access to advanced language models like GPT-3.5 and GPT-4, which can generate and continue text, summarize, or create content based on prompts. The task described—text completion—is precisely what GPT models are designed for.

Azure AI Language performs language understanding and analysis (sentiment, key phrases, translation), Azure Machine Learning trains custom models, and Azure AI Vision handles images. Hence, Azure OpenAI is the correct choice.