Free Practice Questions for Microsoft AI-900 Exam

✅ Yes – Extract key phrases

❌ No – Generate press releases

✅ Yes – Detect sentiment

The Azure AI Language service is a powerful set of natural language processing (NLP) tools within Azure Cognitive Services, designed to analyze, understand, and interpret human language in text form. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, this service includes several capabilities such as key phrase extraction, sentiment analysis, language detection, named entity recognition (NER), and question answering.

Extract key phrases from documents → YesThe Key Phrase Extraction feature identifies the most relevant words or short phrases within a document, helping summarize important topics. This is useful for indexing, summarizing, or organizing content. For instance, from “Azure AI Language helps analyze customer feedback,” it may extract “Azure AI Language” and “customer feedback” as key phrases.

Generate press releases based on user prompts → NoThis functionality falls under generative AI, specifically within Azure OpenAI Service, which uses models such as GPT-4 for text creation. The Azure AI Language service focuses on analyzing and understanding existing text, not generating new content like press releases or articles.

Build a social media feed analyzer to detect sentiment → YesThe Sentiment Analysis capability determines the emotional tone (positive, neutral, negative, or mixed) of text data, making it ideal for analyzing social media posts, reviews, or feedback. Businesses often use this to gauge customer satisfaction or brand reputation.

In summary, the Azure AI Language service analyzes text to extract insights and detect sentiment but does not generate new textual content.

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According to the Microsoft Azure AI Fundamentals study guide and Microsoft Learn module “Identify features of computer vision workloads”, computer vision is an AI workload that allows systems to interpret and understand visual information from the world, such as images and videos.

Computer vision tasks typically include:

Object detection and image classification (e.g., detecting brands, logos, or items in images)

Image analysis (e.g., identifying colors, patterns, or visual features)

Face detection and recognition

Optical Character Recognition (OCR) for reading text in images

Therefore, both detecting brands and detecting color schemes in an image are clear examples of computer vision tasks because they involve analyzing visual content.

In contrast:

A. Predict stock prices → Regression task, not vision-based.

D. Translate text between languages → Natural language processing (NLP).

E. Extract key phrases → NLP as well.

Thus, the correct computer vision tasks are B and C.

Reliability & Safety

https://en.wikipedia.org/wiki/Tay_(bot)

“To build trust, it ' s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It ' s also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing. We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don ' t have unexpected performance failures, and don ' t evolve in ways that are inconsistent with original expectations”

<  A webchat bot can interact with users visiting a website → Yes

 Automatically generating captions for pre-recorded videos is an example of conversational AI → No

 A smart device in the home that responds to questions such as “What will the weather be like today?” is an example of conversational AI → Yes

\ These answers are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore conversational AI in Microsoft Azure.”

1. A webchat bot can interact with users visiting a website → Yes

This statement is true. A webchat bot is a key example of conversational AI, which allows users to communicate with an intelligent system through natural language. The Azure Bot Service supports a webchat channel, enabling website visitors to ask questions or get assistance directly through a chat interface embedded on a webpage. This allows businesses to provide 24/7 automated support and interactive engagement without human intervention.

2. Automatically generating captions for pre-recorded videos is an example of conversational AI → No

This is incorrect because automatically generating captions involves speech-to-text transcription, which falls under speech recognition and not conversational AI. While it uses AI to convert audio into text, it does not involve interactive communication or natural language dialogue. This task would be handled by Azure AI’s Speech service, not the conversational AI framework.

3. A smart device in the home that responds to questions such as “What will the weather be like today?” is an example of conversational AI → Yes

This is true. Smart assistants like those found in home devices (e.g., voice-activated systems) use conversational AI technologies to process spoken language (using natural language processing and speech recognition) and generate appropriate responses. This interaction represents a classic example of conversational AI, as it allows human-like dialogue between a user and an AI system.

✅ Final Answers:

Webchat bot interacting with users → Yes

Auto-captioning videos → No

Smart home device answering questions → Yes

Microsoft’s Responsible AI Principles, as outlined in the AI-900 certification materials and official Microsoft documentation, emphasize six guiding principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. The principle of transparency means that AI systems should be designed so their decisions and processes are understandable and explainable to users and stakeholders.

In this scenario, the AI system is being developed to decide whether a loan should be approved. Such a decision directly affects people’s lives and finances, so it is essential that the system can explain which factors influenced its decision—for example, credit score, income, or payment history. Microsoft’s Responsible AI framework stresses that transparency helps ensure trust between humans and AI systems. When decisions are explainable, users can understand and contest the reasoning if necessary.

The other options do not align precisely with this scenario:

B. Inclusiveness focuses on making AI accessible to all people, regardless of ability or background.

C. Fairness ensures that AI systems treat all individuals equally and do not discriminate. While fairness is important for loan assessment, the question specifically highlights the need for explainability, not equality.

D. Privacy and Security deals with safeguarding user data, which is separate from explaining decisions.

Therefore, the principle demonstrated here is transparency, as it ensures decision-making processes are clear, explainable, and traceable—directly aligning with Microsoft’s responsible AI guidance.

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.

According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft’s Responsible AI guidelines, customers must obtain approval based on their intended usage before accessing and deploying Azure OpenAI Service. This requirement ensures that Microsoft upholds its commitment to Responsible AI principles, which include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

The Azure OpenAI Service provides access to powerful language models such as GPT series and Codex, which can generate, summarize, and understand natural language and code. Because of the potential for misuse—such as generating harmful content, misinformation, or unethical automation—Microsoft enforces a use case review and approval process before granting customers access to the service. This process involves submitting an application describing the intended purpose, deployment method, and compliance measures. Only after Microsoft validates that the proposed use aligns with responsible AI practices will access be approved.

This aligns with Microsoft’s documented commitment that “customers are required to submit an application that describes their intended use of the Azure OpenAI Service,” ensuring that all deployments follow ethical and legal standards. This approval step helps maintain transparency and prevent harmful or non-compliant use cases such as deepfake generation, biased automation, or malicious chatbot deployment.

Other options listed in the question are incorrect:

Commit to a minimum level of expenditure – Microsoft does not require financial commitments for ethical approval.

Pay an upfront fee – Payment is handled through normal Azure billing, not a special fee.

Provide credit card details – Not a responsible AI requirement; this is standard for any Azure subscription.

Therefore, the correct and verified answer per Microsoft’s Responsible AI framework and Azure AI-900 study

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of computer vision workloads on Azure,” the “Describe Image” feature within Azure’s Computer Vision service automatically generates textual captions that describe the contents of an image.

This feature uses a deep learning model trained on millions of labeled images to identify objects, people, and scenes, then formulates a natural language sentence summarizing what it sees. For example, given a photograph showing “two people sitting at a table with a laptop,” the service might generate the caption: “A man and a woman sitting at a desk using a laptop.”

Here’s how the other options differ:

A. Recognize text: Refers to Optical Character Recognition (OCR), which extracts written text from images, not generates descriptive captions.

C. Identify the areas of interest: Refers to detecting regions of an image that stand out visually, such as hotspots or significant features, not descriptive captioning.

D. Detect objects: Identifies and classifies objects in an image (e.g., cars, chairs, people) but doesn’t produce a sentence or caption summarizing them.

Thus, only “Describe the images” generates automatic, human-readable captions that summarize photo content, a core computer vision workload taught in AI-900.