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”, the standard workflow for creating and deploying a machine learning model — especially within Azure Machine Learning Designer — follows a structured sequence of steps to ensure that the model is trained effectively and evaluated correctly.

Here’s the detailed breakdown of the correct order:

Import and prepare a dataset:This is always the first step in the machine learning lifecycle. The dataset is imported into Azure Machine Learning and cleaned or preprocessed. Preparation might include handling missing values, normalizing data, removing outliers, and encoding categorical variables. This ensures the dataset is ready for modeling.

Split the data randomly into training data and validation data:The dataset is then divided into two parts — the training set and the validation (or testing) set. Typically, around 70–80% of the data is used for training and 20–30% for validation. This step ensures that the model can be evaluated on unseen data later, preventing overfitting.

Train the model:During this stage, the machine learning algorithm learns patterns from the training data. Azure Machine Learning Designer provides multiple algorithms (classification, regression, clustering, etc.) that can be applied using “Train Model” components.

Evaluate the model against the validation dataset:Finally, the trained model’s performance is tested using the validation dataset. Evaluation metrics such as accuracy, precision, recall, or RMSE (depending on the model type) are calculated to assess how well the model generalizes to new data.

The incorrect option — “Evaluate the model against the original dataset” — is not used in proper ML workflows, because evaluating on the same data used for training would give misleadingly high accuracy due to overfitting.

According to the Microsoft Azure AI Fundamentals study guide and Microsoft Learn module “Identify features of computer vision workloads”, computer vision is an AI workload that allows systems to interpret and understand visual information from the world, such as images and videos.

Computer vision tasks typically include:

Object detection and image classification (e.g., detecting brands, logos, or items in images)

Image analysis (e.g., identifying colors, patterns, or visual features)

Face detection and recognition

Optical Character Recognition (OCR) for reading text in images

Therefore, both detecting brands and detecting color schemes in an image are clear examples of computer vision tasks because they involve analyzing visual content.

In contrast:

A. Predict stock prices → Regression task, not vision-based.

D. Translate text between languages → Natural language processing (NLP).

E. Extract key phrases → NLP as well.

Thus, the correct computer vision tasks are B and C.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of regression machine learning”, regression is the machine learning technique used when the goal is to predict a continuous numeric value based on historical data. In this question, predicting the number of gift cards that will be sold next month involves forecasting a quantity—a numeric outcome—which is the hallmark of a regression problem.

Regression models learn patterns from past data (for example, previous months’ gift card sales, seasonality, holidays, and marketing spend) and use that information to predict future sales. Common algorithms used for regression include linear regression, decision tree regression, and boosted regression trees. The output is a continuous value such as “2,450 gift cards expected next month.”

In contrast:

A. Classification is used when the output is categorical, such as predicting whether a transaction is “fraud” or “not fraud,” or whether a customer will “renew” or “cancel.” It answers questions with discrete classes rather than numeric values.

C. Clustering is an unsupervised learning technique used to group similar data points together based on their characteristics—for example, segmenting customers into behavior-based clusters. Clustering doesn’t predict future numeric outcomes.

The AI-900 curriculum explicitly explains that regression predicts numeric values, classification predicts categories, and clustering finds natural groupings in data.

Therefore, to predict the number of gift cards to be sold, the correct and verified machine learning type is Regression.

Final Answer: B. Regression

The correct answer is B. Optical character recognition (OCR).

OCR is a key capability within the Computer Vision and Document Intelligence services in Azure AI that enables systems to detect and extract printed or handwritten text from images and scanned documents.

When capturing text from images, OCR technology analyzes visual patterns (shapes of letters and numbers) and converts them into machine-readable text. For example, a photo of a receipt, street sign, or printed report can be processed to extract textual content programmatically.

A (Text analysis): Applies to NLP tasks such as sentiment detection or key phrase extraction, not image processing.

C (Image description): Generates captions describing the scene or objects in an image.

D (Object detection): Identifies and locates objects but does not extract text.

According to the AI-900 official study materials, regression is a type of supervised machine learning used to predict continuous numeric values. Predicting the animal population of an area involves estimating a numeric quantity, which makes regression the appropriate model type.

Microsoft Learn defines regression workloads as predicting real-valued outputs, such as:

Forecasting sales or demand.

Predicting housing prices.

Estimating resource usage or population sizes.

In contrast:

Classification predicts discrete categories (e.g., “cat” or “dog”).

Clustering groups data into similar clusters but doesn’t produce numeric predictions.

Therefore, because the task requires predicting a numerical population size, the verified answer is C. Regression, as per Microsoft’s AI-900 official guidelines.

The correct answers are B. Azure REST API and C. Azure SDK for Python.

The Azure OpenAI Service can be accessed using multiple development interfaces. According to Microsoft Learn documentation, developers can call the service via the Azure REST API, which provides direct HTTPS-based access to the model endpoints for tasks like completions, chat, embeddings, and image generation. This interface is platform-independent and supports integration with any system capable of making HTTP requests.

Additionally, Azure SDKs offer higher-level libraries for convenient integration into applications. The Azure SDK for Python and Azure SDK for JavaScript are both supported for Azure OpenAI interaction, allowing developers to authenticate with Azure credentials, send prompts, and receive model responses programmatically.

However, among the listed options, the REST API (B) and SDK for Python (C) are most explicitly referenced in the AI-900 learning modules and Microsoft documentation as standard tools to call Azure OpenAI services.

Option A (Azure CLI) is incorrect because the CLI is used primarily for provisioning and managing Azure resources, not for directly calling OpenAI model endpoints.

Therefore, based on the Azure AI-900 and OpenAI integration guidance, the correct answers are B. Azure REST API and C. Azure SDK for Python.

In Microsoft Azure AI Language Service, both Named Entity Recognition (NER) and Key Phrase Extraction are core features for text analytics. They serve distinct purposes in analyzing and structuring unstructured text data.

Named Entity Recognition (NER):NER is used to identify and categorize specific entities within text, such as people, organizations, locations, dates, times, and quantities. According to Microsoft Learn’s “Analyze text with Azure AI Language” module, NER scans text to extract these entities along with their types. Therefore, the statement “Named entity recognition can be used to retrieve dates and times in a text string” is True (Yes).

Key Phrase Extraction:This feature identifies the most important phrases or main topics in a block of text. It is useful for summarization or highlighting central ideas without classifying them into specific categories. Therefore, the statement “Key phrase extraction can be used to retrieve important phrases in a text string” is also True (Yes).

City Name Retrieval:While key phrase extraction highlights major phrases, it does not extract specific entities like cities or dates. Extracting such details requires Named Entity Recognition, which is designed to find named entities such as city names, people, or organizations. Hence, the statement “Key phrase extraction can be used to retrieve all the city names in a text string” is False (No).

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft’s Responsible AI Principles, one of the core principles is “Privacy and Security.” This principle ensures that AI systems protect personal and sensitive information, maintaining compliance with privacy laws, data protection regulations, and ethical data-handling practices.

If a Natural Language Processing (NLP) model is created using data obtained without permission, it directly violates this principle. Data collected without proper consent breaches user privacy and potentially violates regulations such as GDPR (General Data Protection Regulation) or other global privacy frameworks.

The Privacy and Security principle emphasizes the following:

AI systems must ensure data collection and usage transparency.

Data must be lawfully acquired and used with consent.

Systems should protect data against unauthorized access or misuse.

In contrast:

Inclusiveness promotes accessibility and fairness for all users.

Transparency focuses on explaining how AI systems make decisions.

Reliability and safety ensure systems function as intended and minimize harm.

Therefore, using unapproved data clearly breaches Privacy and Security, as it involves unethical data sourcing and endangers user trust.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and the Microsoft Learn module “Describe features of Natural Language Processing (NLP) workloads on Azure”, this scenario combines two major capabilities of AI: speech recognition and natural language understanding.

Speech-to-Text (E) – This is the first step in processing verbal commands. The Azure Speech service converts the spoken words of a user into textual data that can be understood and processed by downstream components. This workload is commonly referred to as speech recognition, and it falls under the speech capabilities of Azure Cognitive Services. Without this transcription process, the system could not interpret the user’s voice input.

Language Modeling (C) – After the speech input is converted into text, the next step is to interpret the meaning of the text so the system can take appropriate action. Language modeling, also known as language understanding, is responsible for identifying the user’s intent (for example, “turn on the lights” or “set the thermostat to 72 degrees”) and extracting entities (such as device name or temperature value). In Azure, this function is handled by Language Understanding (LUIS) or Conversational Language Understanding (CLU). These models allow smart systems to process commands and map them to defined actions.

Other options are not correct:

A. Text-to-speech converts text output into spoken language, which is not mentioned as a requirement.

B. Translation converts text from one language to another, irrelevant to this scenario.

D. Key phrase extraction identifies important terms in text but doesn’t interpret or execute commands.

Therefore, the solution uses speech-to-text to transcribe verbal commands and language modeling to understand and act upon them — the two key NLP workloads enabling voice-controlled smart devices.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe features of common AI workloads”, there are three primary supervised and unsupervised machine learning types: Regression, Classification, and Clustering. Each type of learning addresses a different kind of problem depending on the data and desired prediction output.

Regression – Regression models are used to predict numeric, continuous values. The study guide specifies that “regression predicts a number.” In the scenario “Predict how many minutes late a flight will arrive based on the amount of snowfall,” the output (minutes late) is a continuous numeric value. Therefore, this is a regression problem. Regression algorithms like linear regression or decision tree regression estimate relationships between variables and predict measurable quantities.

Clustering – Clustering falls under unsupervised learning, where the model identifies natural groupings or patterns in unlabeled data. The official AI-900 training material states that “clustering is used to find groups or segments of data that share similar characteristics.” The scenario “Segment customers into different groups to support a marketing department” fits this description because the goal is to group customers based on behavior or demographics without predefined labels. Thus, it is a clustering problem.

Classification – Classification is a supervised learning method used to predict discrete categories or labels. The AI-900 content defines classification as “predicting which category an item belongs to.” The scenario “Predict whether a student will complete a university course” requires a yes/no (binary) outcome, which is a classic classification problem. Examples include logistic regression, decision trees, or neural networks trained for categorical prediction.

In summary:

Regression → Predicts continuous numeric outcomes.

Clustering → Groups data by similarities without predefined labels.

Classification → Predicts discrete or categorical outcomes.

Hence, the correct and verified mappings based on the official AI-900 study material are:

Regression → Flight delay prediction

Clustering → Customer segmentation

Classification → Course completion prediction