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

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common machine learning types”, a regression model is a type of supervised machine learning model that is used to predict continuous numeric values based on one or more input variables (features).

In supervised learning, models are trained using labeled data, where each input record has a known target value (label). For regression specifically, the label represents a numeric quantity — such as price, age, temperature, or sales figures. The model learns to approximate a function that maps input variables to numeric outputs.

For example:

Predicting house prices based on size, location, and number of rooms.

Predicting monthly sales revenue from marketing spend and seasonality.

Forecasting temperature based on historical weather data.

In Azure Machine Learning Designer and AutoML, when building a regression model, the label column must therefore be of numeric data type (integer or float). If the label were categorical (for example, “yes/no” or “approved/denied”), the appropriate model type would be classification, not regression.

To contrast with other options:

Boolean – used in binary classification problems (true/false outcomes).

Datetime – used for time series forecasting, not standard regression labels.

Text – used as input features for NLP models, not as regression targets.

Hence, when configuring a regression task in Azure, ensuring the target variable (label) is numeric is a fundamental requirement. The model’s performance metrics—such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R²—also rely on numeric computations.

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 Azure AI Document Intelligence service (formerly Form Recognizer) is designed to analyze, extract, and structure data from scanned or digital documents such as invoices, receipts, contracts, and forms. According to the Microsoft Learn module “Extract data from documents with Azure AI Document Intelligence”, the service uses optical character recognition (OCR) and pretrained machine learning models to automatically extract key information.

A. Extract the invoice number from an invoice – YESThe prebuilt invoice model in Document Intelligence can detect and extract key fields such as invoice number, date, total amount, tax, and vendor details from scanned or digital invoices.

B. Identify the retailer from a receipt – YESThe prebuilt receipt model can recognize fields like merchant name (retailer), transaction date, total spent, and tax amount, making this option correct as well.

C. Find images of products in a catalog – NOThis is a computer vision or Custom Vision use case, not a document data extraction task.

D. Translate a form from French to English – NOTranslation involves Azure AI Translator, part of the Language service, not Document Intelligence.

Hence, the correct and Microsoft-verified answers are:

✅ A. Extract the invoice number from an invoice

✅ B. Identify the retailer from a receipt

Full Detailed Explanation (250–300 words):

The correct answer is A. AI systems should treat people fairly.

This statement aligns with one of Microsoft’s six Responsible AI principles, which are:

Fairness – AI systems should treat all people fairly and avoid bias.

Reliability and Safety

Privacy and Security

Inclusiveness

Transparency

Accountability

The principle of Fairness ensures that AI models do not discriminate based on factors such as race, gender, age, or socioeconomic background. For example, a loan approval or hiring model must provide equal opportunity to all qualified applicants regardless of demographic differences.

B (Not revealing design details) contradicts Transparency, which promotes openness about AI functionality.

C (Black-box models) goes against Microsoft’s push for Explainable AI.

D (Protect developers’ interests) is not part of Microsoft’s Responsible AI framework.

Therefore, the verified correct answer is A. AI systems should treat people fairly.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, object detection is a type of computer vision workload that not only identifies objects within an image but also determines their location by drawing bounding boxes around them. This functionality is clearly described in the Microsoft Learn module “Identify features of computer vision workloads.”

In this scenario, the AI system analyzes an image to find a vehicle and then returns a bounding box showing where that vehicle is located within the image frame. That ability — to detect, classify, and localize multiple objects — perfectly defines object detection.

Microsoft’s study content contrasts object detection with other computer vision workloads as follows:

Image classification: Determines what object or scene is present in an image as a whole but does not locate it (e.g., “this is a car”).

Object detection: Identifies what objects are present and where they are, usually returning coordinates for bounding boxes (e.g., “car detected at position X, Y”).

Optical Character Recognition (OCR): Extracts text content from images or scanned documents.

Facial detection: Specifically locates human faces within an image or video feed, often as part of face recognition systems.

In Azure, object detection capabilities are available through services such as Azure Computer Vision, Custom Vision, and Azure Cognitive Services for Vision, which can be trained to detect vehicles, products, or other objects in various image datasets.

Therefore, based on the AI-900 study guide and Microsoft Learn materials, the verified and correct answer is Object detection, as it accurately describes the process of returning a bounding box indicating an object’s position in an image.

The scenario requires two checks on each photo: (1) there is at least one face, and (2) at least one detected face is wearing sunglasses. The Azure AI Face service – Detect operation is purpose-built for this combination. It detects faces and returns per-face attributes, including glasses type, so you can enforce both rules in a single pass. From the official guidance, the Detect API “detects human faces in an image and returns the rectangle coordinates of their locations” and exposes face attributes such as glasses. A concise attribute extract states: “Glasses: NoGlasses, ReadingGlasses, Sunglasses, Swimming Goggles.” With this, you can count faces (requirement 1) and then verify that at least one face’s glasses attribute equals sunglasses (requirement 2).

By contrast, other options don’t align as precisely:

A. Verify (Face service) compares whether two detected faces belong to the same person. It does not provide content attributes like sunglasses; it requires face inputs for identity/one-to-one scenarios, which doesn’t meet your content-filter goal.

C. Describe Image (Computer Vision) returns a natural-language caption of the whole image. While a caption might mention “a person wearing sunglasses,” it’s not guaranteed, is not face-scoped, and offers less deterministic filtering than a structured attribute on a detected face.

D. Analyze Image (Computer Vision) can return tags such as “person” or sometimes “sunglasses,” but those tags are image-level and not bound to specific faces. You need to ensure that a detected face (not just any region) is wearing sunglasses. Face-scoped attributes from Face Detect are more reliable for this logic.

Therefore, the most accurate and exam-aligned choice is B. the Detect operation in the Face service, because it allows you to programmatically confirm face presence and per-face sunglasses in a precise, rule-driven workflow.

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.

In unsupervised machine learning, the algorithm learns patterns or structure within data without pre-labeled outputs or target values. The primary goal is to discover hidden relationships or group similar data points automatically. The Microsoft Azure AI Fundamentals (AI-900) study materials identify clustering as the key example of unsupervised learning.

In clustering, algorithms such as K-means, hierarchical clustering, or DBSCAN group data based on feature similarity. For example, a business may cluster customers by purchase behavior to discover natural customer segments without prior category labels. The model finds inherent patterns within the data rather than being told what to predict.

By contrast, classification and regression are supervised learning techniques. In supervised learning, the algorithm is trained using labeled data where correct outputs are already known. Therefore, the correct answer is B. Clustering, as it best represents unsupervised learning in Azure AI-900 principles.

App1: “Understands the public perception of a brand or topic” → Sentiment analysis

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn’s Natural Language Processing (NLP) documentation, Sentiment analysis is a feature of the Azure AI Language Service that determines the emotional tone or attitude expressed in text. It classifies text as positive, negative, neutral, or mixed, which makes it ideal for analyzing customer opinions, brand perception, or product feedback.

For example, an organization can use sentiment analysis to process customer reviews or social media posts to determine how people feel about a particular brand or topic. This insight helps companies assess customer satisfaction, public perception, and marketing impact.

App2: “Applies profanity filters to speech-to-text” → Language detection

The task of applying profanity filters occurs during or after speech-to-text transcription, which involves identifying the language used so that the correct filter can be applied. Language detection is an NLP feature that determines which language is being spoken or written. Once the language is detected, appropriate profanity filtering rules are automatically applied to remove or mask offensive words from transcribed text.

Other options such as Captioning or Named Entity Recognition (NER) are not relevant:

Captioning describes images or videos, not speech filtering.

NER identifies people, locations, or organizations but does not handle profanity or language detection.

Therefore, based on Azure AI NLP features:

App1 uses Sentiment analysis

App2 uses Language detection