AI-900 Exam Dumps - Microsoft Azure AI Fundamentals

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.

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.

Features

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Explore fundamental principles of machine learning,” data values that influence the prediction of a model are called features. In the context of machine learning, a feature is an individual measurable property, attribute, or input variable used by the model to make predictions.

Features are the independent variables that describe the characteristics of the data. For example, in a housing price prediction model, features might include square footage, location, number of bedrooms, and year built. These inputs help the model understand relationships in the data so it can predict the target outcome (the house price).

Microsoft Learn explains that features are the input variables that the algorithm uses to identify patterns and relationships in the training data. During training, the model learns how changes in these features influence the label (also known as the dependent variable or target variable). The label is the value the model tries to predict—such as “price,” “category,” or “yes/no.”

Here’s how the other options differ:

Dependent variables (labels): These are the outcomes or target values the model predicts, not the inputs.

Identifiers: These are unique keys (like customer ID or transaction ID) used to distinguish records but not to influence predictions.

Labels: As mentioned, labels are the results the model tries to predict.

Therefore, based on the AI-900 learning objectives and Microsoft’s official explanation, the data values that influence the prediction of a model—that is, the input variables that guide the model’s learning—are called features. These features form the foundation of the model’s predictive capabilities and directly impact its accuracy and performance.

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.

During model training, a portion of the dataset (commonly 70–80%) is used to teach the machine learning algorithm to identify patterns and relationships between input features and the output label. The remaining data (usually 20–30%) is held back to evaluate the model’s performance and verify its accuracy on unseen data. This ensures the model is not overfitted (too tightly fitted to training data) and can generalize well to new inputs.

Key steps highlighted in Microsoft Learn materials:

Model Training: Use the training data to fit the model — the algorithm learns relationships between input features and labels.

Model Evaluation: Use the test or validation data to assess the accuracy, precision, recall, or other metrics of the trained model.

Model Deployment: Once validated, the model is deployed to make real-world predictions.

Other options explained:

Feature engineering: Involves preparing and transforming input data, not splitting datasets for training and testing.

Time constraints: Not a machine learning process step.

Feature stripping: Not a recognized ML concept.

MLflow models: Refers to an open-source tool for tracking and managing models, not dataset splitting or training.

Thus, when you use a portion of the dataset to prepare and train a machine learning model, and retain the rest to verify results, the process is known as model training.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore computer vision in Microsoft Azure,” object detection is a computer vision technique used to locate and identify objects within an image. It not only determines what objects are present but also where they appear in the image by returning bounding box coordinates around each detected item.

In this scenario, the goal is to identify the location of widgets within digital photos. This requires both recognition (knowing that the object is a widget) and localization (determining its position). The Custom Vision service in Azure allows you to train a model specifically for your own images, making it ideal for recognizing company-specific products such as widgets. By selecting the Object Detection domain in Custom Vision, you can label regions of interest in your training images. The model then learns to detect and locate those objects in new photos.

Let’s examine the other options:

A. Computer Vision Spatial Analysis: Used for people tracking, movement detection, and occupancy analytics in video streams — not for locating products in still images.

C. Custom Vision classification: This model categorizes an image as a whole (e.g., “contains a widget” or “does not contain a widget”) but does not locate objects within the image.

D. Computer Vision Image Analysis: Provides general image tagging, description, and OCR capabilities but does not pinpoint object locations.

Let’s analyze each application in the context of Microsoft Azure AI Fundamentals (AI-900) and computer vision model types.

App1 – Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment → Image classificationThis application is performing image classification, where each image (of a tumor) is assigned to a single predefined category — benign or malignant. Image classification models learn patterns from labeled training images and predict the correct class for new ones. In this case, the model identifies the type of tumor, a classic binary classification scenario.

App2 – Uses images from cameras to track individual livestock as they move around a farm → Object detectionThis scenario describes object detection, which not only identifies what objects (in this case, animals) are in an image but also locates them by drawing bounding boxes. Tracking movement requires detecting the position of each animal frame by frame. Object detection models are well-suited for use cases involving counting, tracking, or monitoring objects in a visual scene.

App3 – Identifies brands in photographs of billboards → Optical character recognition (OCR)This app involves reading and interpreting text (brand names, slogans, or logos) from images of billboards. Optical Character Recognition (OCR), part of Azure AI Vision, extracts textual information from images or scanned documents. Once extracted, that text can be analyzed to identify brand names or keywords.

Summary:

App1 → Image classification

App2 → Object detection

App3 → Optical character recognition (OCR)

In Microsoft Azure OpenAI Service and the AI-900/AI-102 study materials, grounding data is the correct term used to describe the process of providing contextual or external information to improve the accuracy, relevance, and quality of responses generated by a generative AI model such as GPT-3.5 or GPT-4.

Grounding is a prompt engineering technique where the AI model is supplemented with relevant background data, such as company documents, knowledge bases, or user context, that helps the model generate factually correct and context-aware responses. Microsoft Learn defines grounding as a way to connect the model’s general knowledge to specific, real-world information. For example, if you ask a GPT-3.5 model about your organization’s HR policies, the base model will not know them unless that policy information is provided (grounded) in the prompt. By embedding this contextual data, the AI becomes “grounded” in the facts it needs to respond reliably.

This technique differs from other prompt engineering concepts:

A. Providing examples (few-shot prompting) shows the model sample inputs and outputs to guide formatting or style, not factual context.

B. Fine-tuning involves retraining the model with labeled data to permanently adjust its behavior — it’s not a prompt-based technique.

D. System messages define the model’s role, tone, or style (for example, “You are a helpful assistant”) but do not add factual context.

Therefore, when you provide contextual information (like product details, policy documents, or reference text) within a prompt to enhance the quality and factual reliability of the model’s responses, you are applying the grounding data technique.