AI-900 Exam Dumps - Microsoft Azure AI Fundamentals

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore computer vision in Microsoft Azure,” computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world — such as images or videos.

In this scenario, the task is to count the number of animals in an area based on a video feed. This requires the system to:

Detect the presence of animals in each frame of the video (object detection).

Track and count them across multiple frames as they move.

These are classic computer vision tasks, as they involve analyzing visual inputs (video or image data) and identifying objects (in this case, animals). Azure provides services such as Azure Computer Vision, Custom Vision, and Video Indexer, which can perform object detection, counting, and activity recognition using AI models trained on visual datasets.

Why the other options are incorrect:

Forecasting: Involves predicting future values based on historical data (e.g., predicting sales or weather), not analyzing video feeds.

Knowledge mining: Focuses on extracting insights from large text-based document repositories, not images or videos.

Anomaly detection: Identifies unusual patterns in numeric or time-series data, not visual objects.

Therefore, identifying and counting animals in video footage falls under computer vision, since it uses AI to visually detect, classify, and quantify objects in real-time or recorded feeds.

This question is derived from the Microsoft Azure AI Fundamentals (AI-900) learning module, particularly under “Describe features of conversational AI workloads on Azure.” It tests understanding of chatbot capabilities and design principles within the context of Azure Bot Service and Conversational AI.

Chatbots can support voice input – YesAccording to the AI-900 official materials, conversational AI systems such as chatbots can interact with users through text or voice. Using speech recognition services like Azure Cognitive Services Speech-to-Text, bots can interpret spoken input, and with Text-to-Speech, they can respond verbally. This enables voice-based chatbots used in virtual assistants, call centers, and customer support. Hence, voice input is fully supported by conversational AI solutions in Azure.

A separate chatbot is required for each communication channel – NoThe Azure Bot Service is designed to provide multi-channel communication from a single bot instance. A single chatbot can communicate across several channels such as Microsoft Teams, Web Chat, Slack, Facebook Messenger, and email without needing separate bots for each platform. This centralized design allows developers to create, deploy, and manage one bot while configuring multiple channel connections through the Azure portal. Therefore, the statement is false.

Chatbots manage conversation flows by using a combination of natural language and constrained option responses – YesIn Microsoft’s AI-900 training, chatbots are described as using Natural Language Processing (NLP) to understand free-form user input while also guiding interactions with predefined options such as buttons or quick replies. This hybrid approach ensures both flexibility and control, improving user experience and accuracy. Bots can interpret natural language via services like Language Understanding (LUIS) and also present structured options to guide conversations efficiently.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of classification machine learning”, classification is a type of supervised machine learning used when the goal is to predict a categorical outcome. That means the output variable represents discrete labels such as Yes/No, True/False, or Category A/B/C.

In this example, the model is predicting whether a person uses a bicycle (Yes or No) — a binary categorical outcome. The input (distance from home to work) is numeric, but the prediction is a class or category, which makes it a classification problem.

To compare:

A and D (predicting how many cups of coffee or race minutes) involve numeric predictions, which are regression tasks.

B (grouping images by similar colors) involves clustering, an unsupervised learning method used to find natural groupings in data.

Thus, the use case that fits classification is predicting whether someone uses a bicycle, since the answer is categorical.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of regression machine learning”, regression is the machine learning technique used when the goal is to predict a continuous numeric value based on historical data. In this question, predicting the number of gift cards that will be sold next month involves forecasting a quantity—a numeric outcome—which is the hallmark of a regression problem.

Regression models learn patterns from past data (for example, previous months’ gift card sales, seasonality, holidays, and marketing spend) and use that information to predict future sales. Common algorithms used for regression include linear regression, decision tree regression, and boosted regression trees. The output is a continuous value such as “2,450 gift cards expected next month.”

In contrast:

A. Classification is used when the output is categorical, such as predicting whether a transaction is “fraud” or “not fraud,” or whether a customer will “renew” or “cancel.” It answers questions with discrete classes rather than numeric values.

C. Clustering is an unsupervised learning technique used to group similar data points together based on their characteristics—for example, segmenting customers into behavior-based clusters. Clustering doesn’t predict future numeric outcomes.

The AI-900 curriculum explicitly explains that regression predicts numeric values, classification predicts categories, and clustering finds natural groupings in data.

Therefore, to predict the number of gift cards to be sold, the correct and verified machine learning type is Regression.

Final Answer: B. Regression

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.

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

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.