QnA Maker supports automatic extraction of question-and-answer pairs from structured files such as PDF, Microsoft Word, or Excel documents, as well as from public webpages. This makes PDF the correct file format for populating a knowledge base.

Other options are invalid:

B. PPTX – Not supported.

C. XML – Not a recognized input for QnA Maker.

D. ZIP – Used for packaging, not Q & A content.

This question evaluates your understanding of how different Azure AI workloads correspond to specific tasks in image, text, and content generation scenarios, as explained in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn modules covering common AI workloads and Azure services.

Generate a caption from a given image → Computer VisionThis is a computer vision task because it involves analyzing the visual elements of an image and producing descriptive text (a caption). Azure AI Vision provides image analysis and captioning capabilities through its Describe Image API, which uses deep learning models to recognize objects, scenes, and actions in an image and automatically generate natural-language descriptions (e.g., “A cat sitting on a sofa”).

Generate an image from a given caption → Generative AIThis task belongs to Generative AI, which focuses on creating new content such as text, code, or images based on prompts. Tools like Azure OpenAI Service with DALL-E can interpret text descriptions and generate realistic images that match the given caption. Generative AI is capable of creative synthesis, not just analysis, making it the appropriate category.

Generate a 200-word summary from a 2,000-word article → Text AnalyticsText analytics (a subset of natural language processing) allows summarization, sentiment analysis, and entity recognition from large text corpora. Azure AI Language includes text summarization capabilities that condense long documents into concise summaries while preserving meaning and key information.

Before building or training any model in Azure Machine Learning Studio—including when using Automated ML (AutoML)—you must first create a Machine Learning workspace.

A workspace serves as the central environment for all machine learning assets such as datasets, compute targets, models, pipelines, and experiments. According to the AI-900 study guide and Microsoft Learn module “Describe features and tools for machine learning in Azure,” a workspace is the foundational setup required to organize and manage all ML-related resources.

The sequence typically follows these steps:

Create a Machine Learning workspace.

Configure compute resources (e.g., compute instance or cluster).

Upload or register datasets.

Use Automated ML or Designer to train models.

Deploy and manage the trained models.

Option A (Jupyter notebook) is an optional tool for coding experiments.

Option C (Registered dataset) is created after the workspace exists.

Option D (Designer pipeline) is a visual tool used within the workspace.

Hence, B. a Machine Learning workspace is the correct answer because it is the first and mandatory step before using Automated ML or any training component in Azure Machine Learning Studio.

Microsoft’s Responsible AI principles are the foundation for developing and deploying ethical and trustworthy AI systems. The six key principles are Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle guides specific practices for ensuring AI systems operate responsibly in real-world applications like automated insurance quoting systems.

Transparency – This principle ensures that the AI’s decisions can be understood and explained. Recording the decision-making process and enabling staff to trace how a quote was generated aligns with transparency. It allows stakeholders to interpret the reasoning behind model outputs, ensuring that the AI behaves predictably and ethically.

Privacy and Security – This principle focuses on protecting personal data and ensuring that sensitive information is handled responsibly. Limiting access to customer data only to authorized personnel maintains compliance with privacy laws (like GDPR) and safeguards against misuse. Microsoft emphasizes that AI systems should maintain strict control over data visibility and integrity.

Inclusiveness – This principle ensures that AI systems are accessible to all users, including people with disabilities. By supporting screen readers and assistive technologies, the system ensures equal access to information and services for every customer. Inclusiveness prevents discrimination and promotes accessibility, both of which are central to Microsoft’s Responsible AI strategy.

Thus, the correct mapping of principles is:

Decision process → Transparency

Personal information visibility → Privacy and Security

Accessibility via screen readers → Inclusiveness.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify Azure services for conversational AI,” the Azure AI Bot Service is specifically designed to create intelligent conversational agents (chatbots) that can interact with users across multiple communication channels, such as web chat, social media, phone calls, Microsoft Teams, and digital assistants.

In this scenario, customers need the ability to query the status of their orders through various interfaces — including voice and text platforms. Azure AI Bot Service enables this by integrating with Azure AI Language (for understanding natural language), Azure Speech (for speech-to-text and text-to-speech capabilities), and Azure Communication Services (for telephony or chat integration).

The bot can interpret user input like “Where is my order?” or “Check my delivery status,” call backend systems (such as an order database or API), and then respond appropriately to the user through the same communication channel.

Let’s analyze the incorrect options:

B. Azure AI Translator Service: Used for real-time text translation between languages; it doesn’t handle conversation logic or database queries.

C. Azure AI Document Intelligence model: Extracts data from structured and semi-structured documents (e.g., invoices, receipts), not user queries.

D. Azure Machine Learning model: Builds and deploys predictive models, but doesn’t provide conversational or multi-channel interaction capabilities.

Thus, for enabling multi-channel conversational experiences where customers can inquire about order statuses using voice, chat, or digital assistants, the most appropriate solution is Azure AI Bot Service, as outlined in Azure’s AI conversational workload documentation.

The correct answers are A. Create images and D. Modify images.

The Azure OpenAI DALL-E model is a text-to-image generative AI model that can create original images and modify existing ones based on text prompts. According to Microsoft Learn and Azure OpenAI documentation, DALL-E interprets natural language descriptions to produce unique and creative visual content, making it useful for design, illustration, marketing, and educational applications.

Create images (A) – DALL-E can generate new images entirely from textual input. For example, the prompt “a futuristic city skyline at sunrise” would result in a custom-generated artwork that visually represents that description.

Modify images (D) – DALL-E also supports inpainting and outpainting, allowing users to edit or expand existing images. You can replace parts of an image (for example, changing a background or object) or add new elements consistent with the visual style of the original.

The remaining options are incorrect:

B. OCR is performed by Azure AI Vision, not DALL-E.

C. Detect objects in images is also an Azure AI Vision (Image Analysis) feature.

E. Generate captions for images is handled by Azure AI Vision, not DALL-E, since DALL-E generates—not interprets—visuals.

In this scenario, analyzing vehicle images and measuring the distance between them requires first detecting each vehicle’s position in the image. Object detection models can locate and identify multiple objects (such as cars, trucks, or motorcycles) by assigning bounding boxes. Once detected, their coordinates can be used to calculate distances or spacing.

Image classification only assigns a single label per image, not per object. Facial recognition is human-focused, and OCR deals with text extraction. Thus, object detection is the correct model type for this task.

A FAQ PDF file contains structured Q & A content. The QnA Maker (now part of Azure Language Service) can automatically extract questions and answers from such a document and build a knowledge base for conversational bots. This allows users to interact naturally with the content via chat interfaces.

Other options:

B. Text Analytics → Extracts insights, not conversational content.

C. Computer Vision → Used for image analysis.

D. LUIS → Handles intent detection, not static question–answer responses.

The correct completion of the sentence is:

“You can use the Custom Vision service to train an object detection model by using your own images.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of computer vision workloads,” the Azure Custom Vision service is a specialized component of Azure Cognitive Services for Vision that enables developers to train custom image classification or object detection models using their own labeled image datasets.

The Custom Vision service differs from the Computer Vision service in that it allows full customization — meaning you can upload your own images, tag them manually, and train the model to recognize objects specific to your use case (for example, detecting your company’s products, tools, or vehicles). Once trained, the model can identify and localize these objects in new images by returning bounding boxes and confidence scores, which is precisely what defines an object detection workload.

Microsoft’s AI-900 materials describe object detection as the process of identifying objects in an image and determining their position, typically represented by bounding boxes. Custom Vision supports two main project types:

Image Classification: Determines what is present in the image (e.g., “dog,” “cat,” “car”).

Object Detection: Identifies what is present and where it is located in the image.

In contrast:

Computer Vision provides prebuilt models for general image analysis but doesn’t allow custom model training.

Form Recognizer is used for extracting text and data from structured or semi-structured documents.

Azure Video Analyzer for Media focuses on video content analysis, not custom object detection.

Therefore, based on the official Microsoft AI-900 study guide and Microsoft Learn content, the verified and correct answer is Custom Vision, as it specifically allows training of a custom object detection model using your own images.

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.