Pass the Microsoft Certified: Azure AI Fundamentals AI-900 Questions and answers with Dumpstech

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.

In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision workloads are grouped into distinct types, each serving a specific purpose. The three major workloads illustrated here are image classification, object detection, and optical character recognition (OCR). Understanding their use cases is essential for correctly mapping them to real-world scenarios.

Generate captions for images → Image classificationThe image classification workload is used to identify the main subject or context of an image and assign descriptive labels. In Microsoft Learn’s “Describe features of computer vision workloads,” image classification models are trained to recognize content (e.g., a cat, a beach, or a city). Caption generation expands on classification results by describing the image’s contents in human-readable language—based on what the model identifies as key visual features.

Extract movie title names from movie poster images → Optical character recognition (OCR)OCR is a vision workload that detects and extracts text from images. Azure AI Vision’s Read API or Document Intelligence OCR models can identify printed or handwritten text within posters, signs, or documents. In this case, the movie title text from a poster is best extracted using OCR.

Locate vehicles in images → Object detectionThe object detection workload identifies multiple objects within an image and provides their locations using bounding boxes. It’s ideal for tasks like counting cars in a parking lot or tracking objects in traffic images.

This question is drawn from the Microsoft Azure AI Fundamentals (AI-900) syllabus section “Describe features of natural language processing (NLP) workloads on Azure.” According to the Microsoft Learn materials, Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to analyze, understand, and generate human language. NLP enables machines to work with text or speech data in a way that extracts meaning, sentiment, and intent.

Microsoft defines NLP as enabling scenarios such as language detection, text classification, key phrase extraction, sentiment analysis, and named entity recognition. The example given—classifying emails as “work-related” or “personal”—is a text classification task, which falls under NLP capabilities. The AI model processes the textual content of emails, identifies linguistic patterns, and categorizes them based on the detected topic or context.

Let’s analyze the other options:

Predict the number of future car rentals → This is a forecasting task, handled by machine learning regression models, not NLP.

Predict which website visitors will make a transaction → This is a classification or prediction problem in machine learning, not NLP, since it deals with behavioral or numerical data rather than language.

Stop a process in a factory when extremely high temperatures are registered → This is an IoT or anomaly detection scenario, focusing on sensor data, not language understanding.

Therefore, only classifying email messages as work-related or personal correctly represents an NLP use case. It illustrates how NLP can analyze written text and make intelligent categorizations—a key capability covered in AI-900’s natural language workloads section.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module “Describe features of Computer Vision workloads on Azure”, the Azure AI Custom Vision service allows users to build, train, and deploy custom image classification or object detection models. It is specifically designed for scenarios where you need a model tailored to your unique image dataset — in this case, personal digital photographs.

Custom Vision lets you upload and label your own images (for example, “family,” “friends,” “vacations,” or “pets”) and then train a model that learns to recognize those categories. The system automatically extracts relevant features from the training images and creates a model capable of classifying new images into the predefined labels. You can iteratively refine your model by adding more images or re-training to improve accuracy.

The other options do not fit this requirement:

A. Azure AI Language deals with text-based tasks such as language understanding, sentiment analysis, and key phrase extraction — not image processing.

B. Azure AI Computer Vision provides prebuilt image analysis models (e.g., object detection, tag generation, scene description), but it cannot learn custom categories unique to your dataset. It’s great for general image recognition but not for specialized labeling tasks.

C. Azure AI Document Intelligence (Form Recognizer) is used to extract information from structured or semi-structured documents such as forms, invoices, or receipts — not photographs.

Therefore, when you need to label or categorize personal photos with custom-defined tags, the right service is Azure AI Custom Vision, because it allows you to build a model trained specifically on your own collection of images.

Statements

Yes

No

A webchat bot can interact with users visiting a website.

Yes

Automatically generating captions for pre-recorded videos is an example of natural language processing.

No

A smart device in the home that responds to questions such as “What will the weather be like today?” is an example of natural language processing.

Yes

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn modules on AI workloads, each of these statements maps to a distinct area of artificial intelligence — namely Conversational AI, Speech AI, and Natural Language Processing (NLP).

“A webchat bot can interact with users visiting a website.” – YesThis is true. A webchat bot represents an example of Conversational AI. It leverages natural language understanding (NLU) to interpret user input and generate appropriate responses. These bots can be created using Azure services such as Azure AI Bot Service and Language Understanding (LUIS). They enable automated interactions with users through text-based communication on websites, applications, or messaging platforms.

“Automatically generating captions for pre-recorded videos is an example of natural language processing.” – NoThis is false. Generating captions from audio involves speech recognition, not NLP. Specifically, it uses speech-to-text technology to transcribe spoken words into written text. This function is typically performed by Azure’s Speech service, which is part of the Speech AI workload, not the language-processing workload.

“A smart device in the home that responds to questions such as ‘What will the weather be like today?’ is an example of natural language processing.” – YesThis is true. Smart assistants like Alexa or Cortana use NLP to interpret spoken queries, extract meaning, and generate appropriate responses. NLP allows these devices to understand human language, retrieve relevant information, and respond conversationally.

Within Azure AI Document Intelligence (formerly Form Recognizer), the Contract prebuilt model is designed to extract key information from legal and business contracts, including parties, jurisdictions, dates, and terms. According to Microsoft Learn, this prebuilt model identifies structured entities such as contracting parties, effective dates, governing jurisdictions, and termination clauses.

Layout (B) extracts text, tables, and structure but does not identify semantic information such as parties or jurisdictions.

General document (C) extracts key-value pairs and entities but lacks domain-specific contract analysis.

Read (D) performs OCR (optical character recognition) to extract raw text but not contextual metadata.

Thus, when the requirement is to extract parties and jurisdictions from a legal document, the Contract model is the correct Azure AI Document Intelligence choice.

To generate cartoons or images from text descriptions, the correct Azure OpenAI model is DALL-E. As described in Microsoft’s OpenAI integration documentation, DALL-E is a generative image model that converts natural language prompts into images, illustrations, and artwork.

Codex is for code generation, GPT-3.5 and GPT-4 are for text and reasoning tasks, not image creation. Therefore, B. DALL-E is correct.

When using generative AI models such as those in Azure OpenAI, it’s critical to monitor and control the model’s outputs to prevent the generation of harmful, hateful, or unsafe content. According to Microsoft’s Responsible AI guidelines and Azure OpenAI safety systems, this is achieved through content filtering mechanisms.

Content filtering automatically analyzes generated text and blocks or flags responses containing hate speech, harassment, sexual content, or violent language. Azure OpenAI applies these filters by default through its built-in content moderation system, which categorizes responses into safety levels (safe, review, block).

Option review:

A. Prompt engineering: Helps guide responses but does not detect hate content automatically.

B. Abuse monitoring: Refers to manual or post-processing review, not real-time automated filtering.

C. Content filtering: ✅ Correct — actively detects and prevents hateful or unsafe responses.

D. Fine-tuning: Adjusts model behavior using additional training data but doesn’t ensure safety monitoring.

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.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and the Microsoft Learn module: “Describe features of common AI workloads”, conversational AI solutions like chatbots can be created using various methods—not only through custom code. Azure provides both no- code/low-code and developer-focused approaches. For instance, users can design chatbots using Power Virtual Agents, which requires no programming knowledge, or they can use Azure Bot Service with the Bot Framework SDK for fully customized scenarios. Hence, the statement “Chatbots can only be built by using custom code” is False (No) because Azure supports multiple levels of technical involvement for building bots.

The second statement is True (Yes) because the Azure Bot Service is designed specifically to host, manage, and connect conversational bots to users across different channels. Microsoft Learn explicitly explains that the service provides integrated hosting, connection management, and telemetry for bots built using the Bot Framework or Power Virtual Agents. It acts as the foundation for deploying, scaling, and managing chatbot workloads in Azure.

The third statement is also True (Yes) because Azure Bot Service supports integration with Microsoft Teams, among many other channels such as Skype, Facebook Messenger, Slack, and web chat. Microsoft documentation states that Azure-hosted bots can communicate directly with Teams users through the Teams channel, enabling intelligent virtual assistants within the Teams environment.