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 conversational AI in Microsoft Azure,” to create a chatbot that can automatically answer simple, predefined user questions, you need two main Azure AI components — one to handle the conversation interface and another to manage the knowledge and language understanding aspect.

Azure Bot Service (A)This service is used to create, manage, and deploy chatbots that interact with users through text or voice. The Bot Service provides the framework for conversation management, user interaction, and channel integration (e.g., webchat, Microsoft Teams, Skype). It serves as the backbone of conversational AI applications and supports integration with other cognitive services like the Language Service.

Language Service (D)The Azure AI Language Service (which now includes Question Answering, formerly QnA Maker) is used to build and manage the knowledge base of predefined questions and answers. This service enables the chatbot to understand user queries and return appropriate responses automatically. The QnA capability allows you to import documents, FAQs, or structured data to create a searchable database of responses for the bot.

Why the other options are incorrect:

B. Azure Machine Learning: This service is used for building, training, and deploying custom machine learning models, not for chatbot Q & A automation.

C. Translator: This service performs language translation, which is not required for answering predefined questions unless multilingual support is specifically needed.

Therefore, to implement a chatbot that can answer simple, repetitive user questions and reduce the load on human operators, you combine Azure Bot Service (for interaction) with the Language Service (for question-answering intelligence).

What is a Predicted vs. True chart?

Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.

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,” the “Describe Image” feature within Azure’s Computer Vision service automatically generates textual captions that describe the contents of an image.

This feature uses a deep learning model trained on millions of labeled images to identify objects, people, and scenes, then formulates a natural language sentence summarizing what it sees. For example, given a photograph showing “two people sitting at a table with a laptop,” the service might generate the caption: “A man and a woman sitting at a desk using a laptop.”

Here’s how the other options differ:

A. Recognize text: Refers to Optical Character Recognition (OCR), which extracts written text from images, not generates descriptive captions.

C. Identify the areas of interest: Refers to detecting regions of an image that stand out visually, such as hotspots or significant features, not descriptive captioning.

D. Detect objects: Identifies and classifies objects in an image (e.g., cars, chairs, people) but doesn’t produce a sentence or caption summarizing them.

Thus, only “Describe the images” generates automatic, human-readable captions that summarize photo content, a core computer vision workload taught in AI-900.

The correct completion of the sentence is:

“The interactive answering of questions entered by a user as part of an application is an example of natural language processing.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, Natural Language Processing (NLP) is a branch of Artificial Intelligence that focuses on enabling computers to understand, interpret, and respond to human language in a way that is both meaningful and useful. It is one of the key AI workloads described in the “Describe features of common AI workloads” module on Microsoft Learn.

When a user types a question into an application and the system responds interactively — such as in a chatbot, Q & A system, or virtual assistant — this process requires language understanding. NLP allows the system to process the input text, determine user intent, extract relevant entities, and generate an appropriate response. This is the foundational capability behind services such as Azure Cognitive Service for Language, Language Understanding (LUIS), and QnA Maker (now integrated as Question Answering in the Language service).

Microsoft’s study guide explains that NLP workloads include the following key scenarios:

Language understanding: Determining intent and context from text or speech.

Text analytics: Extracting meaning, key phrases, sentiment, or named entities.

Conversational AI: Powering bots and virtual agents to interact using natural language.These systems rely on NLP models to analyze user inputs and respond accordingly.

In contrast:

Anomaly detection identifies data irregularities.

Computer vision analyzes images or video.

Forecasting predicts future values based on historical data.

Therefore, based on the AI-900 official materials, the interactive answering of user questions through an application clearly falls under Natural Language Processing (NLP).

The correct answer is B. Azure AI Document Intelligence (formerly Form Recognizer).

This Azure service uses AI and OCR technologies to analyze and extract structured data from documents such as invoices, receipts, and purchase orders. It identifies key fields like billing address, invoice number, total amount due, and line items. The service supports prebuilt models for common document types and custom models for specialized layouts.

Option review:

A. Azure AI Search: Used for knowledge mining and semantic search, not document data extraction.

B. Azure AI Document Intelligence — ✅ Correct. Designed for form and invoice extraction.

C. Azure AI Custom Vision: Used for image classification and object detection, not text extraction.

D. Azure OpenAI: Generates or processes language but not structured document data.

Therefore, Azure AI Document Intelligence is the right service to extract data from scanned invoices.

In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision capabilities refer to artificial intelligence systems that can analyze and interpret visual content such as images and videos. The Azure AI Vision and Face API services provide pretrained models for detecting, recognizing, and analyzing visual information, enabling developers to build intelligent applications that understand what they " see. "

When asked how computer vision capabilities can be deployed, the correct answer is to integrate a face detection feature into an app. This aligns with Microsoft Learn’s module “Describe features of computer vision workloads,” which explains that computer vision can identify objects, classify images, detect faces, and extract text (OCR). The Face API, a part of Azure AI Vision, specifically provides face detection, verification, and emotion recognition capabilities.

Integrating these services into an application allows it to perform actions such as:

Detecting human faces in photos or video streams.

Recognizing facial attributes like age, emotion, or head pose.

Enabling secure authentication based on face recognition.

The other options are incorrect because they relate to different AI workloads:

Develop a text-based chatbot for a website: This falls under Conversational AI, implemented with Azure Bot Service or Conversational Language Understanding (CLU).

Identify anomalous customer behavior on an online store: This task relates to machine learning and anomaly detection models, not computer vision.

Suggest automated responses to incoming email: This uses Natural Language Processing (NLP) capabilities, not visual analysis.

Therefore, the correct and Microsoft-verified completion of the statement is:

“Computer vision capabilities can be deployed to integrate a face detection feature into an app.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Prepare data for machine learning”, feature engineering refers to the process of transforming raw data into meaningful features that can be effectively used by machine learning algorithms. This includes steps such as scaling, normalization, encoding categorical variables, handling missing values, and creating new features derived from existing ones.

The question states:

“Ensuring that the numeric variables in training data are on a similar scale.”

This directly describes a data normalization or standardization step, which is a core component of feature engineering. The purpose of scaling numeric variables is to ensure that all features contribute equally to the model’s learning process. Without normalization, features with large numeric ranges (such as “income in dollars”) could dominate smaller-scale features (like “age in years”), leading to biased model performance.

In Azure Machine Learning, this is typically done using the Normalize Data module or transformations in the data preparation stage. Microsoft Learn explains that normalization and feature scaling are applied before model training to ensure that gradient-based algorithms (such as regression or neural networks) converge more efficiently and produce more accurate results.

The other options are not correct:

Data ingestion refers to collecting and importing data into a system.

Feature selection involves choosing the most relevant features, not scaling them.

Model training is the phase where the algorithm learns patterns from the processed data, which occurs after feature engineering.

Therefore, ensuring that numeric variables are on a similar scale is a step in Feature Engineering.

This question evaluates understanding of the different Azure AI Computer Vision services and their distinct functionalities, as covered in the Microsoft AI-900 study guide and Microsoft Learn modules under “Describe features of common AI workloads” and “Identify Azure services for computer vision.”

Azure AI Document Intelligence (formerly known as Form Recognizer):This service is designed to extract structured information from documents, such as forms, receipts, and invoices. It uses optical character recognition (OCR) combined with AI models to detect key-value pairs, tables, and handwritten text. This makes it ideal for automating data entry and digitizing scanned documents. Hence, it matches “Extract information from scanned forms and invoices.”

Azure AI Vision (formerly Computer Vision):This service provides image and video analysis capabilities. It can detect objects, people, text, and scenes; generate image captions; and extract descriptive tags. It also supports OCR for printed and handwritten text within images. Therefore, it matches “Analyze images and video, and extract descriptions, tags, objects, and text.”

Azure AI Custom Vision:Custom Vision allows you to train your own image classification and object detection models using your own labeled images. Unlike the general Vision service, Custom Vision lets you build domain-specific models—for example, detecting your company’s products or identifying manufacturing defects. Hence, it matches “Train custom image classification and object detection models by using your own images.”

These three services complement each other within Azure’s computer vision ecosystem, collectively supporting both general-purpose and specialized AI solutions for visual data analysis.