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

This question tests your understanding of machine learning workflow tasks as described in the Microsoft Azure AI Fundamentals (AI-900) study guide and the Microsoft Learn module “Explore the machine learning process.” The AI-900 curriculum divides the machine learning lifecycle into key phases: data preparation, feature engineering and selection, model training, model evaluation, and model deployment. Each phase has specific tasks designed to prepare, build, and assess predictive models before deployment.

Examining the values of a confusion matrix → Model evaluationIn Azure Machine Learning, evaluating a model involves checking its performance using metrics such as accuracy, precision, recall, and F1-score. The confusion matrix is one of the most common tools for this purpose. According to Microsoft Learn, “model evaluation is the process of assessing a trained model’s performance against test data to ensure reliability before deployment.” Analyzing the confusion matrix helps determine whether predictions align with actual outcomes, making this task part of model evaluation.

Splitting a date into month, day, and year fields → Feature engineeringFeature engineering refers to transforming raw data into features that better represent the underlying patterns to improve model performance. The study guide describes it as “the process of creating new input features from existing data.” Splitting a date field into separate numeric fields (month, day, year) is a classic example of feature engineering because it enables the model to learn from temporal patterns that might otherwise remain hidden.

Picking temperature and pressure to train a weather model → Feature selectionFeature selection involves identifying the most relevant variables that have predictive power for the model. As defined in Microsoft Learn, “feature selection is the process of choosing the most useful subset of input features for training.” In this scenario, selecting temperature and pressure variables as inputs for a weather prediction model fits perfectly within the feature selection stage.

Therefore, the correct matches are:

✅ Examining confusion matrix → Model evaluation

✅ Splitting date field → Feature engineering

✅ Picking temperature & pressure → Feature selection

According to the Microsoft Azure OpenAI Service documentation and AI-900 official study materials, the DALL-E model is specifically designed to generate and edit images from natural language prompts. When a user provides a descriptive text input such as “a futuristic city skyline at sunset”, DALL-E interprets the textual prompt and produces an image that visually represents the content described. This functionality is known as text-to-image generation and is one of the creative AI capabilities supported by Azure OpenAI.

DALL-E belongs to the family of generative models that can create new visual content, expand existing images, or apply transformations to images based on textual instructions. Within Azure OpenAI, the DALL-E API enables developers to integrate image creation directly into applications—useful for design assistance, marketing content generation, or visualization tools. The model learns from vast datasets of text–image pairs and is optimized to ensure alignment, diversity, and accuracy in the produced visuals.

By contrast, the other options serve different purposes:

A. GPT-4 is a large language model for text-based generation, reasoning, and conversation, not for creating images.

C. GPT-3 is an earlier text generation model, primarily used for language tasks like summarization, classification, and question answering.

D. Whisper is an automatic speech recognition (ASR) model used to convert spoken language into written text; it has no image-generation capability.

Therefore, when the requirement is to generate images based on user prompts, the only Azure OpenAI model that fulfills this purpose is DALL-E. This aligns directly with the AI-900 learning objective covering Azure OpenAI generative capabilities for text, code, and image creation.

Classification

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of common AI workloads”, classification is a supervised machine learning technique used when the goal is to predict which category or class an item belongs to. In supervised learning, the model is trained with labeled data—data that already contains known outcomes. The system learns patterns and relationships between input features and their corresponding labels so it can predict future classifications accurately.

In the scenario provided — “A banking system that predicts whether a loan will be repaid” — the model’s output is a binary decision, meaning there are two possible outcomes:

The loan will be repaid (positive class)

The loan will not be repaid (negative class)

This kind of problem involves predicting a discrete value (a label or category), not a continuous numeric output. Therefore, it perfectly fits the classification type of machine learning.

The AI-900 learning materials describe classification as being used in many real-world examples, including:

Determining whether an email is spam or not spam.

Predicting whether a customer will churn (leave) or stay.

Detecting fraudulent transactions.

Assessing medical test results as positive or negative.

By contrast:

Regression predicts continuous numeric values, such as predicting house prices, temperatures, or sales revenue. It would not apply here because repayment prediction is not a numeric value but a categorical decision.

Clustering is an unsupervised learning method that groups similar data points without predefined categories, such as segmenting customers by purchasing behavior.

Thus, based on Microsoft’s Responsible AI and AI-900 study guide concepts, a banking system that predicts whether a loan will be repaid uses the Classification type of machine learning.

The Azure AI Language service provides several NLP features, including language detection, key phrase extraction, sentiment analysis, and named entity recognition (NER).

When you need to extract specific data points such as dates, names, organizations, or locations from unstructured text (for example, social media posts), the correct feature is Entity Recognition.

Entity Recognition identifies and classifies information in text into predefined categories like:

Person names (e.g., “John Smith”)

Organizations (e.g., “Contoso Ltd.”)

Dates and times (e.g., “October 22, 2025”)

Locations, events, and quantities

This capability helps transform unstructured textual data into structured data that can be analyzed or stored.

Option analysis:

A (Language detection): Determines the language of a text (e.g., English, French).

B (Speech recognition): Converts spoken audio to text; not applicable here.

C (Key phrase extraction): Identifies important phrases or topics but not specific entities like names or dates.

D (Entity recognition): Correctly extracts names, dates, and other specific data from text.

Hence, the accurate feature for this scenario is D. Entity Recognition.

The correct answer is B. Optical Character Recognition (OCR).

Optical Character Recognition (OCR) is a feature of Azure AI Vision that converts printed or handwritten text within images into machine-readable text. In this scenario, the goal is to read runner numbers on shirts from race photos. OCR can identify and extract these numbers, allowing them to be associated with specific participants.

Option analysis:

A. Image classification: Categorizes entire images (e.g., “runner,” “crowd”), not text.

B. Optical Character Recognition (OCR) — ✅ Correct. Extracts alphanumeric text from images.

C. Object detection: Identifies and locates objects (e.g., shoes, cars) but doesn’t read text.

D. Facial recognition: Identifies individuals by matching facial features to known identities, not by reading numbers.

Therefore, to read and extract runner numbers from photos, the correct computer vision technique is Optical Character Recognition (OCR).

According to the Microsoft Azure AI Fundamentals (AI-900) syllabus and Microsoft Learn module “Describe features of natural language processing (NLP) workloads on Azure,” translation is a core NLP workload that converts text from one language into another while maintaining meaning and context.

In this scenario, the website stores reviews in English and must present them in the user’s native language based on geographical location. This directly requires a translation workload, which uses Azure Cognitive Services — specifically, the Translator service — to automatically translate content dynamically for each user.

Other options explained:

B. Language modeling involves predicting the next word in a sentence or understanding linguistic patterns; it’s used in model training, not translation.

C. Key phrase extraction identifies main ideas in text, not language conversion.

D. Speech recognition converts spoken words into written text but does not perform translation or handle geographic adaptation.

Microsoft’s Translator service supports real-time text translation, multi-language detection, and context preservation, making it ideal for global websites. The AI-900 study guide emphasizes translation as one of the most common NLP workloads, enabling applications to break language barriers and enhance accessibility for diverse audiences.

Therefore, based on official Microsoft Learn material, the correct answer is:

✅ A. translation.

According to the Microsoft Azure AI Fundamentals (AI-900) learning content, Language Understanding Intelligent Service (LUIS) is part of Azure Cognitive Services used to interpret the meaning or intent behind a user’s input in natural language. When a user says, “Call me back later,” the system must recognize that the user intends for a call to be scheduled or delayed—this is not just about translating or analyzing text but understanding intent and relevant entities.

LUIS allows developers to train models to identify intents (such as ScheduleCall, CancelMeeting, etc.) and extract key entities (like names, times, or actions) from text inputs. It is typically integrated with conversational agents such as Azure Bot Service, enabling more natural, human-like interactions.

Other options do not fit the scenario:

Translator Text (A) translates text between languages but does not interpret meaning.

Text Analytics (B) performs sentiment analysis, key phrase extraction, and named entity recognition, but it doesn’t identify intent.

Speech (C) converts spoken language to text or text to speech but doesn’t interpret what the words mean.

Therefore, for understanding user intent such as “Call me back later,” the correct AI service is D. Language Understanding (LUIS).

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under the topic “Describe features of common AI workloads” and “Identify capabilities of Azure AI services”, QnA Maker is the service designed specifically to create a knowledge base (KB) or question-and-answer bot from existing content such as FAQ documents, product manuals, support pages, or structured knowledge sources.

QnA Maker enables developers to take semi-structured text (for example, an FAQ document or webpage) and automatically generate a knowledge base of pairs of questions and corresponding answers. This knowledge base can then be connected to a chatbot, typically through the Azure Bot Service, so that users can interact with it conversationally. The key advantage is that the process does not require deep machine learning or programming expertise. The service uses natural language processing (NLP) to match user queries with the most relevant pre-defined answers in the knowledge base.

In the AI-900 curriculum, this falls under the Conversational AI workload—creating intelligent bots that can respond naturally to user questions. Microsoft’s training content explains that “QnA Maker extracts pairs of question and answer from your content and builds a knowledge base that can be queried by bots and other applications.” The output, as shown in the example diagram, demonstrates how user input (the question) triggers a request to the QnA Maker API, which returns a JSON response containing the best-matched answer.

The other options are not correct because:

B. Language Understanding (LUIS) is used to interpret user intent and extract entities, not to create FAQs.

C. Text Analytics performs text extraction, sentiment analysis, and key-phrase detection but does not build a Q & A knowledge base.

D. Speech handles speech-to-text or text-to-speech, not Q & A matching.

Therefore, per the AI-900 study guide and Microsoft Learn, the verified and correct answer is A. QnA Maker.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module “Describe features of common AI workloads”, a chatbot (also known as a conversational AI agent) is a solution designed to interact with users through natural language conversation across multiple channels such as email, phone, webchat, and messaging apps.

Chatbots use Natural Language Processing (NLP) to interpret what users are saying, identify their intent, and provide relevant responses. In Azure, this functionality is implemented using the Azure Bot Service integrated with the Azure Cognitive Service for Language (Question Answering and Language Understanding). The study guide emphasizes that chatbots are used in customer service, information retrieval, and support automation to reduce the workload on human agents and improve response times.

The requirement in this question — supporting email, phone, and live chat channels — aligns exactly with the definition of a conversational AI chatbot, which can operate across multiple communication platforms. Microsoft Learn clearly identifies that chatbots can be deployed to assist customers in retrieving information, answering FAQs, and escalating complex issues when necessary.

The other options are incorrect because:

A. NLP is the underlying technology used by the chatbot but not the solution itself.

B. Computer vision involves analyzing images or videos, which is unrelated to this scenario.

C. Machine learning is a broader AI field and not a specific customer support solution type.

Statement

Yes / No

Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI.

Yes

A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI.

Yes

An AI solution that is offered at different prices for different sales territories is an example of the Microsoft inclusiveness principle for responsible AI.

No

This question is based on the Responsible AI principles defined by Microsoft, which are part of the AI-900 Microsoft Azure AI Fundamentals curriculum. Microsoft’s Responsible AI framework consists of six key principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle ensures that AI systems are developed and used in a way that benefits people and society responsibly.

Transparency Principle – YesProviding an explanation for a loan decision aligns with the Transparency principle. Microsoft defines transparency as helping users and stakeholders understand how AI systems make decisions. For example, when a credit scoring AI model approves or denies a loan, explaining the factors that influenced that outcome (such as credit history or income level) ensures that customers understand the reasoning process. This builds trust and supports responsible deployment.

Reliability and Safety Principle – YesA triage bot that prioritizes insurance claims based on injury severity relates directly to Reliability and Safety. This principle ensures AI systems operate consistently, perform accurately, and produce dependable outcomes. In the case of the triage bot, it must reliably assess the input data (injury descriptions) and rank claims appropriately to avoid harm or misjudgment, aligning with Microsoft’s emphasis on designing AI systems that are safe and robust.

Inclusiveness Principle – NoAn AI solution priced differently across sales territories is not related to Inclusiveness. Inclusiveness focuses on ensuring accessibility and eliminating bias or exclusion for all users—especially those with disabilities or underrepresented groups. Pricing strategy is a business decision, not an inclusiveness issue. Therefore, this statement is No.

In summary, based on the AI-900 Responsible AI principles, the correct selections are: