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

Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.

The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like " indoor " , which can ' t be localized with bounding boxes.

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation on Azure AI Vision, OCR is a computer vision technology that detects and extracts printed or handwritten text from images, scanned documents, or photographs. The OCR feature in Azure AI Vision can analyze images containing handwritten notes, recognize the characters, and convert them into machine-readable digital text.

This process is ideal for digitizing handwritten meeting notes, forms, or classroom materials. OCR works by identifying text regions in an image, segmenting characters or words, and then applying language models to interpret them correctly. Azure’s OCR capabilities support multiple languages and can handle varied handwriting styles.

Other options are incorrect because:

B. Object detection identifies and locates objects (like cars, animals, or furniture) within an image, not text.

C. Image classification assigns an image to a predefined category (e.g., “dog” or “cat”) rather than extracting text.

D. Facial detection detects or recognizes human faces, not written text.

Therefore, to convert handwritten notes into digital text, the correct computer vision technique is Optical Character Recognition (OCR).

The correct answer is A. Personally Identifiable Information (PII) detection.

In the Azure AI Language service, PII detection is a built-in feature designed to automatically identify and redact sensitive or confidential information from text data. According to the Microsoft Learn module “Identify capabilities of Azure AI Language” and the AI-900 study guide, this capability can detect personal data such as names, phone numbers, email addresses, credit card numbers, and other identifiers.

When applied, the service scans input text and either masks or removes these PII elements based on configurable parameters, ensuring compliance with data privacy regulations like GDPR or HIPAA.

For example, if a document contains “John Doe’s phone number is 555-123-4567,” PII detection can return “******’s phone number is ***********,” thereby preventing exposure of sensitive personal details.

Option analysis:

A. Personally Identifiable Information (PII) detection: ✅ Correct. It identifies and masks sensitive data in text.

B. Entity linking: Connects recognized entities to known data sources like Wikipedia; not used for redaction.

C. Custom text classification: Classifies text into predefined categories; not designed for masking personal data.

D. Custom named entity recognition (NER): Detects domain-specific entities you define but doesn’t automatically mask them.

Therefore, to automate masking of names and phone numbers, the appropriate Azure AI Language feature is PII detection.

The correct answer is Azure AI Custom Vision.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Explore computer vision in Azure”, the Azure AI Custom Vision service is specifically designed to allow users to train, test, and deploy custom image classification and object detection models using their own images.

The Azure AI Custom Vision service extends the capabilities of Azure’s general-purpose Computer Vision API by enabling organizations to upload their own labeled datasets, define custom tags (labels), and train models that are optimized for their specific use cases. This makes it ideal for object detection scenarios—such as detecting equipment in a manufacturing line, identifying products on store shelves, or recognizing medical images—where general-purpose models may not suffice.

By contrast, the Azure AI Computer Vision service provides pre-built models for tasks such as image description, tagging, face detection, and OCR (optical character recognition). It does not allow users to train their own models with custom data. Similarly, Azure AI Document Intelligence is used for extracting structured information from documents (forms, receipts, invoices), and Azure Video Analyzer for Media focuses on analyzing video content for insights and metadata extraction.

The AI-900 study guide emphasizes that the Custom Vision service supports two key model types:

Image Classification – categorizing entire images based on predefined tags.

Object Detection – identifying and locating multiple objects within an image by drawing bounding boxes.

Therefore, when the question specifies “train an object detection model by using your own images,” the correct Azure service is Azure AI Custom Vision, as it provides the necessary tools for training, evaluating, and deploying custom computer vision models tailored to a user’s dataset.

Hence, the verified correct answer is: Azure AI Custom Vision.

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)

This question evaluates understanding of fundamental machine learning concepts as covered in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore the machine learning process.” These statements relate to data labeling, model evaluation practices, and performance metrics—three essential parts of building and assessing a machine learning model.

Labelling is the process of tagging training data with known values → YesAccording to Microsoft Learn, “Labeling is the process of tagging data with the correct output value so the model can learn relationships between inputs and outputs.” This is essential for supervised learning, where models require historical data with known outcomes. For example, if training a model to recognize fruit images, each image is labeled as “apple,” “banana,” or “orange.” Hence, this statement is true.

You should evaluate a model by using the same data used to train the model → NoThe AI-900 guide stresses that using the same data for both training and evaluation can cause overfitting, where the model performs well on training data but poorly on unseen data. Instead, the dataset is split into training and testing (or validation) subsets. Evaluation must use test data that the model has never seen before to ensure an unbiased measure of performance. Therefore, this statement is false.

Accuracy is always the primary metric used to measure a model’s performance → NoMicrosoft Learn emphasizes that accuracy is only one metric and not always the best choice. Depending on the problem type, other metrics such as precision, recall, F1-score, or AUC (Area Under the Curve) may be more appropriate—especially in cases with imbalanced datasets. For example, in fraud detection, recall may be more important than accuracy. Thus, this statement is false.

 “Generative AI enables software applications to generate new content, such as language dialogs and images.” — YES

This statement is true. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, Generative AI refers to systems capable of creating new content such as text, audio, images, video, and code. Models like GPT, DALL·E, and Codex use deep learning to generate human-like responses, natural conversations, or creative media. This is a key differentiator between generative and discriminative AI — generative AI produces new data, while discriminative AI categorizes or analyzes existing data.

 “The difference between a large language model (LLM) and a small language model (SLM) is the number of variables in the model.” — YES

This statement is true. The primary distinction between an LLM and an SLM lies in the scale of parameters (variables) within the neural network. LLMs contain billions or even trillions of parameters, which enable them to capture complex linguistic patterns and perform broader tasks. SLMs have fewer parameters, making them faster but less capable of handling complex, context-rich tasks.

 “Generative AI is a type of supervised learning.” — NO

This statement is false. Generative AI models are typically trained using unsupervised or self-supervised learning methods. They learn by predicting missing or next elements in large text or image datasets rather than relying on labeled input-output pairs, which are used in supervised learning.

QnA Maker is an Azure Cognitive Service used to build question-and-answer knowledge bases from structured and unstructured documents, such as FAQs, product manuals, or webpages. According to the AI-900 study guide and Microsoft Learn module “Build a knowledge base with QnA Maker”, this service allows you to extract question-answer pairs from existing data sources like FAQ pages, PDF files, or Word documents.

In this scenario, you have:

A product troubleshooting guide (Word document)

A FAQ webpage

QnA Maker can automatically read both sources, extract relevant Q & A pairs, and create a knowledge base that your chatbot can use to respond to user queries intelligently.

To clarify the other options:

A. Azure Bot Service provides the chatbot interface and conversation logic but doesn’t extract knowledge from documents.

B. Language Understanding (LUIS) identifies intents and entities in natural language input, but it’s not used to read document content.

C. Text Analytics is used for key phrase extraction and sentiment analysis, not Q & A creation.

Therefore, the correct service for processing FAQ-style and document-based content into a question-answering bot is QnA Maker.

Box 1: Yes

Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

Box 2: No

Box 3: Yes

During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to " fit " your data. It will stop once it hits the exit criteria defined in the experiment.

Box 4: No

Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.

The label is the column you want to predict.