Free Microsoft AI-900 Practice Exam with Questions & Answers | Set: 3

According to Microsoft’s Responsible AI Principles, transparency refers to ensuring that AI systems are understandable and that their decisions or predictions can be explained clearly to users. When building an AI-based loan approval app, documenting the reasons for approving or rejecting a loan and making this information available to the applicant ensures that the system’s decision-making process is transparent and easily interpretable.

Transparency in AI involves making the model’s inputs, features, and reasoning process visible and comprehensible to both developers and users. For a loan approval application, this could mean showing which factors—such as income, credit score, or debt ratio—influenced the outcome. Microsoft emphasizes that users should be aware when AI is making decisions that affect them and should have access to an explanation of how those decisions were made.

Fairness (A) ensures that AI systems treat all individuals equitably without bias.

Inclusiveness (B) focuses on accessibility and ensuring AI benefits all groups.

Accountability (D) ensures that humans remain responsible for the outcomes of AI systems.

While accountability supports ethical oversight, the specific act of explaining and documenting the AI’s decision process aligns directly with transparency.

Therefore, the verified answer is C. Transparency.

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.

The question describes a process where an AI system generates text that describes an image — for example, “A dog playing with a ball in the park.” This process is an example of image classification, a core workload in computer vision that allows a system to recognize and categorize the content of an image.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify Azure services for computer vision,” image classification involves analyzing the pixels of an image and assigning one or more predefined categories or labels to it. In more advanced implementations, image classification models are combined with caption generation algorithms to produce descriptive text. For example, Azure AI Vision can generate captions and tags that describe an image’s content, such as “outdoor scene,” “a person riding a bicycle,” or “a group of people smiling.”

Let’s review the other options to clarify why they are incorrect:

Facial detection: Identifies the presence and location of human faces in an image, but does not generate descriptive text.

Object detection: Identifies and locates multiple objects within an image by drawing bounding boxes, not by describing the overall scene.

Optical character recognition (OCR): Extracts text from images or scanned documents (for example, reading a street sign), but it doesn’t create descriptive language about what’s depicted.

Therefore, the correct answer is Image classification, as it aligns with the AI-900 learning objective that describes this task as recognizing and categorizing the main content of an image, often leading to caption generation in modern vision models such as those in Azure AI Vision.

Yes, Yes, No.

According to the Microsoft Azure AI Fundamentals (AI-900) study materials, conversational AI enables applications, websites, and digital assistants to interact with users via natural language. A chatbot is a key conversational AI workload and can be integrated into multiple channels such as web pages, Microsoft Teams, Facebook Messenger, and Cortana using Azure Bot Service and Bot Framework.

“A restaurant can use a chatbot to answer queries through Cortana” — Yes.Azure Bot Service supports multi-channel deployment, which includes Cortana integration. This means the same bot can respond to voice or text input via Cortana, making it a valid use case for a restaurant to provide menu details, reservations, or order tracking through voice-based AI assistants.

“A restaurant can use a chatbot to answer inquiries about business hours from a webpage” — Yes.This is a standard scenario for chatbots embedded on a company website. As per Microsoft Learn’s Describe features of conversational AI module, a chatbot can be added to a website to handle FAQs such as business hours, location, or menu details, thereby improving response time and reducing repetitive human workload.

“A restaurant can use a chatbot to automate responses to customer reviews on an external website” — No.Azure bots and other conversational AI tools cannot automatically interact with or post on external third-party platforms where the business does not control the data or API integration. Automated posting or replying to reviews on external review sites (e.g., Yelp or Google Reviews) would violate both ethical and technical boundaries of responsible AI usage outlined by Microsoft.

The Azure Text Analytics service, a component of Azure Cognitive Services, provides natural language processing (NLP) capabilities to analyze and understand text-based data. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features and uses for Natural Language Processing (NLP)”, the Text Analytics service supports multiple text understanding tasks, such as language detection, key phrase extraction, sentiment analysis, and entity recognition.

Language Identification – Yes:Text Analytics can automatically detect the language in which text is written. This feature analyzes linguistic patterns and assigns a language code (for example, “en” for English, “es” for Spanish). It is one of the primary features described in Microsoft Learn as part of the service’s Language Detection API.

Detect Handwritten Signatures – No:Detecting handwritten signatures is not a text-based NLP task. Instead, it belongs to the computer vision domain, specifically Optical Character Recognition (OCR). The Text Analytics service only processes digital text, not handwritten or image-based data. To detect handwriting or signatures, you would use the Computer Vision OCR API, not Text Analytics.

Entity Recognition – Yes:The Text Analytics service can identify named entities—such as people, locations, organizations, dates, and quantities—within documents. This is known as Named Entity Recognition (NER), which helps extract structured information from unstructured text.

facial analysis.

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,” facial analysis is a computer vision capability that detects faces and extracts attributes such as facial expressions, emotions, pose, occlusion, and image quality factors like exposure and noise. It does not identify or verify individual identities; rather, it interprets facial features and image characteristics to analyze conditions in an image.

In this question, the AI solution helps photographers take better portrait photos by providing feedback on exposure, noise, and occlusion — tasks directly linked to facial analysis. The model analyzes the detected face to determine if the image is well-lit, clear, and unobstructed, thereby improving photo quality. These capabilities are part of the Azure Face service in Azure Cognitive Services, which includes both facial detection and facial analysis functionalities.

Here’s how the other options differ:

Facial detection only identifies that a face exists in an image and provides its location using bounding boxes, without further interpretation.

Facial recognition goes a step further — it attempts to identify or verify a person’s identity by comparing the detected face with stored images. This is not what the scenario describes.

Thus, when an AI solution evaluates image quality aspects like exposure, noise, and occlusion, it’s performing facial analysis, which focuses on understanding image and facial characteristics rather than identification.

In summary, based on Microsoft’s AI-900 study material, this scenario demonstrates facial analysis, a subcategory of computer vision tasks within Azure Cognitive Services.

To improve the quality and relevance of responses generated by a generative AI solution using GPT-3.5, the following three actions are emphasized in the Microsoft Learn: Azure OpenAI Service best practices and AI-900/AI-102 training materials:

(A) Add grounding data to prompts: Grounding ensures that the model’s output is based on factual, domain-specific information. By adding context or external knowledge sources, responses become more accurate and aligned with the organization’s data rather than relying on the model’s general training corpus.

(B) Provide additional examples to prompts: Also known as few-shot prompting, this method demonstrates desired response patterns by including examples in the prompt. This significantly improves output quality, consistency, and adherence to desired formats.

(E) Modify system messages: In Azure OpenAI chat completions, system messages define the model’s behavior, style, and tone. Adjusting system messages allows fine-tuning of the model’s response quality, ensuring it follows context or persona guidelines.

The remaining options are incorrect:

(C) Modify tokenization is a low-level text-processing technique not used to improve model response quality directly.

(D) Add training data to prompts is not possible at runtime since GPT-3.5 models are pre-trained; only prompt engineering can influence output behavior.

Therefore, based on Azure OpenAI and AI-900 guidance, the three best ways to enhance generative

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.

✅ 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.