Free Microsoft AI-900 Practice Exam with Questions & Answers | Set: 10

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of computer vision workloads on Azure”, computer vision models can perform different types of image analysis depending on the goal of the task. The main types include image classification, object detection, and semantic segmentation. Each method analyzes images at a different level of granularity.

Image Classification → Separate images of polar bears and brown bearsImage classification assigns an entire image to a specific category or label. The model analyzes the image as a whole and determines which predefined class it belongs to. For example, in this case, the model would look at the features of each image and decide whether it shows a polar bear or a brown bear. The Microsoft Learn materials define classification as “assigning an image to a specific category.”

Object Detection → Determine the location of a bear in a photoObject detection identifies where objects appear within an image by drawing bounding boxes around them. This type of model not only classifies what object is present but also provides its location. Microsoft Learn explains that object detection “detects and locates individual objects within an image.” For instance, the model can detect a bear in a forest scene and highlight its position.

Semantic Segmentation → Determine which pixels in an image are part of a bearSemantic segmentation is the most detailed form of image analysis. It classifies each pixel in an image according to the object it belongs to. In this scenario, the model identifies every pixel corresponding to the bear’s body. The AI-900 content defines this as “classifying every pixel in an image into a category.”

To summarize:

Image classification → Categorizes entire images.

Object detection → Locates and labels objects within images.

Semantic segmentation → Labels each pixel for precise object boundaries.

https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore natural language processing (NLP) in Azure”, Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP is used to extract meaning and intent from text or speech, perform sentiment analysis, identify entities, and classify content based on context.

One of the primary applications of NLP is text classification, where an AI model automatically categorizes text documents or messages into predefined classes. Classifying emails as work-related or personal is a textbook example of this NLP capability. It involves analyzing the words, phrases, and structure of the text to determine the email’s category. Microsoft Learn highlights this type of problem as document classification, an essential NLP use case often implemented through Azure Cognitive Services such as Text Analytics or Language Studio.

Let’s examine why the other options are incorrect:

Predict the number of future car rentals – This is a time series forecasting or regression task, not NLP.

Predict which website visitors will make a transaction – This is a predictive analytics or machine learning classification problem based on behavioral data, not language understanding.

Stop a process in a factory when extremely high temperatures are registered – This relates to IoT automation or sensor-based anomaly detection, not NLP.

Therefore, based on Microsoft’s AI-900 materials, Natural Language Processing is best used for tasks involving understanding and classifying text, such as classifying email messages as work-related or personal. This example perfectly aligns with NLP’s goal—to enable machines to process and derive insights from human language inputs.

Statements

Yes

No

A bot that responds to queries by internal users is an example of a conversational AI workload.

✅ Yes

An application that displays images relating to an entered search term is an example of a conversational AI workload.

✅ No

A web form used to submit a request to reset a password is an example of a conversational AI workload.

✅ No

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, conversational AI workloads are those that enable interaction between humans and AI systems through natural language conversation, either by text or speech. These workloads are typically implemented using Azure Bot Service, Azure Cognitive Services for Language, and Azure OpenAI Service. The key characteristic of a conversational AI workload is the presence of dialogue—the AI interprets user intent and provides a meaningful, contextual response in a conversation-like manner.

“A bot that responds to queries by internal users is an example of a conversational AI workload.” → YESThis fits the definition perfectly. A chatbot that helps employees (internal users) by answering questions about policies, IT issues, or HR procedures is a typical example of conversational AI. It uses natural language understanding to interpret questions and provide automated responses. Microsoft Learn explicitly identifies chatbots as conversational AI solutions designed for both internal and external interactions.

“An application that displays images relating to an entered search term is an example of a conversational AI workload.” → NOThis is not conversational AI because there is no dialogue or language understanding involved. It is an example of information retrieval or computer vision if it uses image recognition, but not conversation.

“A web form used to submit a request to reset a password is an example of a conversational AI workload.” → NOA password reset form is a simple UI-driven process that doesn’t require AI or conversational logic. It performs a fixed function based on user input but does not understand or respond to natural language.

Therefore, based on the AI-900 study guide, only the first statement is an example of a conversational AI workload, while the second and third statements are not.

https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data

The correct answer is B. Randomly split the data into rows for training and rows for evaluation.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe fundamental principles of machine learning on Azure”, the process of developing a machine learning model involves dividing the available dataset into two or more parts—commonly training data and evaluation (or testing) data. The goal is to ensure that the model can learn patterns from one subset of the data (training set) and then be objectively tested on unseen data (evaluation set) to measure how well it generalizes to new situations.

The training dataset contains both features (the measurable inputs) and labels (the target outputs). The model learns from the patterns and relationships between these features and labels. The evaluation dataset also contains features and labels, but it is kept separate during the training phase. Once the model has been trained, it is tested on this unseen evaluation data to calculate metrics like accuracy, precision, recall, or F1 score.

Microsoft emphasizes that the data split should be random and based on rows, not columns. Each row represents a complete observation (for example, one customer record, one transaction, or one image). Randomly splitting ensures that both subsets represent the same distribution of data, avoiding bias. Splitting by columns would separate features themselves, which would make the model training invalid.

The AI-900 materials often illustrate this using Azure Machine Learning’s data preparation workflow, where data is randomly divided (commonly 70% for training and 30% for testing). This ensures the model learns from diverse examples and is fairly evaluated.

Therefore, the verified and correct approach, as per Microsoft’s official guidance, is B. Randomly split the data into rows for training and rows for evaluation.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common AI workloads”, OCR (Optical Character Recognition) is a Computer Vision technology that detects and extracts printed or handwritten text from images and scanned documents. OCR allows organizations and individuals to convert physical or image-based text into machine-readable, editable, and searchable digital text.

In the context of this question, a historian working with old newspaper articles or archival documents would use OCR to digitize printed content. For instance, the historian can scan or photograph old newspaper pages, and then use an OCR tool—such as Azure Computer Vision’s OCR API—to automatically recognize and extract the textual content from those images. This process enables the historian to store, edit, and analyze the content digitally without manually typing everything.

OCR works by using deep learning algorithms trained on thousands of text samples. The system analyzes patterns, shapes, and spatial relationships of characters to identify text accurately, even from low-quality or aged paper documents. Once extracted, the digital text can be indexed, translated, or processed further using Natural Language Processing (NLP) tools for content analysis.

Now, addressing the other options:

Facial analysis is used to detect emotions, age, or gender from human faces—irrelevant to text digitization.

Image classification identifies entire images by categories (e.g., cat, car, flower).

Object detection identifies and locates multiple objects within an image but doesn’t extract text.

Therefore, per the AI-900 learning objectives under the Computer Vision workload, the correct and verified completion is:

Box 1: No

Box 2: Yes

Box 3: Yes

Anomaly detection encompasses many important tasks in machine learning:

Identifying transactions that are potentially fraudulent.

Learning patterns that indicate that a network intrusion has occurred.

Finding abnormal clusters of patients.

Checking values entered into a system.

In Azure OpenAI, grounding refers to the process of connecting the model to external data sources (for example, a database, search index, or API) so that it can retrieve accurate and up-to-date information before generating a response. This is particularly important for scenarios requiring current facts or events, since OpenAI models like GPT-3.5 and GPT-4 are trained on data available only up to a certain cutoff date.

By adding grounding data, the model’s responses are “anchored” to factual sources retrieved at runtime, improving reliability and factual accuracy. Grounding is commonly implemented in Azure OpenAI + Azure Cognitive Search solutions (Retrieval-Augmented Generation or RAG).

Option review:

A. Modify the system message: Changes model tone or behavior but doesn’t supply real-time data.

B. Add grounding data: ✅ Correct — allows access to recent and domain-specific information.

C. Add few-shot learning: Provides examples in the prompt to improve context understanding but not factual accuracy.

D. Add training data: Refers to fine-tuning; this requires retraining and doesn’t update the model’s awareness of current events.

Hence, the best method to ensure accurate and current responses from an Azure OpenAI model is to add grounding data, enabling the model to reference real, updated sources dynamically.