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

The correct answers are B. a smart device in the home that responds to questions such as, " What will the weather be like today? " and D. a website that uses a knowledge base to interactively respond to users ' questions.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of Natural Language Processing (NLP) workloads on Azure”, Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language in a meaningful way. NLP bridges the gap between human communication and machine understanding, allowing systems to process both spoken and written language.

Option B – A smart device in the home that responds to questions such as “What will the weather be like today?”This is an example of an NLP workload because the device must process spoken language (speech-to-text), interpret the user’s intent (language understanding), and generate a relevant spoken response (text-to-speech). This workflow involves several Azure Cognitive Services, such as Speech Service for recognizing and synthesizing speech, and Language Understanding (LUIS) for interpreting intent. This aligns with conversational AI and NLP tasks in the AI-900 syllabus.

Option D – A website that uses a knowledge base to interactively respond to users’ questions.This is also an NLP workload because the system interprets text input from users and retrieves appropriate answers from a knowledge base. Microsoft’s QnA Maker (now part of the Azure AI Language service) and Azure Bot Service enable such behavior. The model uses NLP to understand the user’s question, find the most relevant response, and generate an appropriate reply — key characteristics of natural language processing.

Incorrect options:

A (assembly line machinery) represents automation or robotics, not NLP.

C (monitoring temperature to activate a fan) is an example of an IoT (Internet of Things) or rule-based system, not related to language processing.

rrect answer is C. Predicting the sale price of a house based on historical data, the size of the house, and the number of bedrooms.

In machine learning, regression is a supervised learning technique used to predict continuous numeric values. Microsoft’s AI-900 study guide defines regression models as those that estimate relationships between variables—predicting a continuous outcome variable from one or more input features.

In this case, the house sale price is a continuous numeric value, and inputs such as size, location, and number of bedrooms are the features. Common regression algorithms include linear regression, decision tree regression, and boosted regression.

Other options represent different ML workloads:

A involves segmentation by categories (classification or clustering).

B represents clustering, grouping similar items without predefined labels.

D represents computer vision, counting animals in images rather than predicting a numeric value.

Hence, the verified answer is C. Regression.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of conversational AI workloads on Azure”, a Language Understanding (LUIS) model is designed to interpret natural language input by identifying intents (the purpose of an utterance) and entities (specific data items in the utterance).

Every LUIS model automatically includes a special intent called “None.” This intent is used to handle utterances that do not fall into any of the model’s defined intents. Adding examples of irrelevant or out-of-scope utterances to the None intent helps the model learn to recognize when a user’s input does not match any existing categories.

For example, if your e-commerce chatbot handles intents such as “TrackOrder” and “CancelOrder,” but a user says “What’s your favorite color?”, that input should be mapped to the None intent so the bot can respond appropriately, such as “I’m not sure how to answer that.”

The AI-900 curriculum emphasizes that including diverse None intent examples improves model robustness and prevents false matches, thereby enhancing user experience.

Other options are incorrect:

A. Test the model by using new utterances: Testing is important but does not define how to detect out-of-scope inputs.

C. Create a prebuilt task entity: Entities extract specific data but are unrelated to intent classification.

D. Create a new model: Unnecessary; handling out-of-scope utterances is done within the same model via the None intent.

✅ Final Answer: B. Add utterances to the None intent

The Azure AI Document Intelligence service (formerly Form Recognizer) is designed to analyze, extract, and structure data from scanned or digital documents such as invoices, receipts, contracts, and forms. According to the Microsoft Learn module “Extract data from documents with Azure AI Document Intelligence”, the service uses optical character recognition (OCR) and pretrained machine learning models to automatically extract key information.

A. Extract the invoice number from an invoice – YESThe prebuilt invoice model in Document Intelligence can detect and extract key fields such as invoice number, date, total amount, tax, and vendor details from scanned or digital invoices.

B. Identify the retailer from a receipt – YESThe prebuilt receipt model can recognize fields like merchant name (retailer), transaction date, total spent, and tax amount, making this option correct as well.

C. Find images of products in a catalog – NOThis is a computer vision or Custom Vision use case, not a document data extraction task.

D. Translate a form from French to English – NOTranslation involves Azure AI Translator, part of the Language service, not Document Intelligence.

Hence, the correct and Microsoft-verified answers are:

✅ A. Extract the invoice number from an invoice

✅ B. Identify the retailer from a receipt