AI-900 Exam Dumps - Microsoft Certified: Azure AI Fundamentals Questions and Answers

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore fundamental principles of machine learning,” a regression model is used when the goal is to predict a continuous numerical value based on historical data.

In this question, the task is to predict the sale price of auctioned items, which is a numeric output that can take on a wide range of values (for example, $50.25, $199.99, etc.). This makes it a regression problem because the output is continuous rather than categorical.

Regression models analyze the relationship between input features (such as item type, condition, age, bidding history, or demand) and a numerical target variable (the sale price). Common regression algorithms include linear regression, decision tree regression, and neural network regression. In Azure Machine Learning, these models are trained using labeled datasets containing known outcomes to learn patterns and make future predictions.

Let’s review the incorrect options:

Classification: Used to predict discrete categories or labels, such as “sold” vs. “unsold” or “low,” “medium,” “high.” It cannot output continuous numeric predictions.

Clustering: An unsupervised technique used to group similar data points based on shared characteristics, not to predict specific numeric outcomes.

Therefore, because predicting a sale price involves forecasting a continuous numerical value, the correct model type is Regression.

This aligns with Microsoft’s AI-900 teaching that regression is used for tasks such as:

Predicting house prices

Forecasting sales revenue

Estimating car values or auction prices

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).

The correct mapping is based on how each Azure Cognitive Service functions within the Microsoft AI ecosystem, as detailed in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Cognitive Services documentation.

Convert spoken requests into text → Azure AI SpeechThe Azure AI Speech service provides speech-to-text (STT) capabilities, which enable an application to recognize spoken language and convert it into written text. This functionality is foundational in voice-enabled applications like digital assistants or transcription services. When a user speaks, this service captures the audio signal and produces an accurate textual representation that can then be processed by other AI services.

Identify the intent of a user’s requests → Azure AI LanguageThe Azure AI Language service (which includes Conversational Language Understanding, formerly LUIS) is designed to extract meaning from text. It identifies intents—the goals or actions a user wants to perform—and entities, which are key details within that request. For example, in the command “Book a flight to Paris,” the intent is “book a flight,” and the entity is “Paris.”

Apply intent to entities and utterances → Azure AI LanguageAgain, the Language service performs this deeper contextual analysis. It not only identifies what the user wants (intent) but also applies it to utterances (specific user expressions) and entities (data elements extracted from text). This helps conversational AI systems take meaningful actions, such as fulfilling user requests.

In summary, Azure AI Speech handles audio-to-text conversion, while Azure AI Language performs natural language understanding, mapping intents and entities—a workflow essential in intelligent conversational applications.

The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.

Example:

The provided data set contains the following columns:

vendor_id: The ID of the taxi vendor is a feature.

rate_code: The rate type of the taxi trip is a feature.

passenger_count: The number of passengers on the trip is a feature.

trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don ' t know how long the trip would take. Thus, the trip time is not a feature and you ' ll exclude this column from the model.

trip_distance: The distance of the trip is a feature.

payment_type: The payment method (cash or credit card) is a feature.

fare_amount: The total taxi fare paid is the label.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore fundamental principles of machine learning,” regression is a supervised machine learning technique used to predict continuous numeric values based on input data.

In this scenario, the goal is to predict how many hours of overtime a delivery person will work depending on the number of orders received. The output — the number of overtime hours — is a continuous variable (for example, 1.5 hours, 3.2 hours, etc.), not a category. This makes it a regression problem, where the model learns patterns from historical data and uses those patterns to estimate a continuous numeric outcome.

Why Regression Applies Here:

Regression models work by finding the mathematical relationship between input features (independent variables) and output values (dependent variables). In this case:

Input (feature): Number of orders received

Output (label): Predicted overtime hours

Azure Machine Learning supports several regression algorithms, including Linear Regression, Decision Tree Regression, and Neural Network Regression, all of which can handle scenarios where a numeric prediction is required.

Why Not the Other Options:

Classification: Used for predicting discrete categories or labels (e.g., “on-time” vs. “late”). It does not output continuous numbers.

Clustering: An unsupervised learning technique used to group data points with similar characteristics, not to make numeric predictions.

Thus, when the output variable is a numeric prediction (such as hours, prices, quantities, or time), the correct machine learning task is Regression.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore conversational AI in Microsoft Azure,” conversational bots are AI applications that can understand and respond to natural language inputs through text or speech. Building such a bot typically involves two key Azure services:

Azure Bot Service (Option B):This service provides the framework and infrastructure needed to create, test, and deploy intelligent chatbots that interact with users across multiple channels (webchat, Teams, email, etc.). It handles conversation flow, integration, and user message management.

Azure Language Service (Option A):This service powers the natural language understanding (NLU) capability of the bot. It enables the bot to interpret user input, extract intent, and query a knowledge base using Question Answering (formerly QnA Maker). This allows the bot to respond intelligently to user questions by finding the most relevant answers.

The other options are incorrect:

C. Form Recognizer is used for extracting structured data from documents like invoices or forms.

D. Anomaly Detector is used for identifying unusual patterns in time-series data.

Hence, to build a bot that understands and answers user questions in natural language, the solution must combine Azure Bot Service for conversation management and Azure Language Service for knowledge-based question answering and natural language understanding.

Microsoft’s Responsible AI principles are the foundation for developing and deploying ethical and trustworthy AI systems. The six key principles are Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle guides specific practices for ensuring AI systems operate responsibly in real-world applications like automated insurance quoting systems.

Transparency – This principle ensures that the AI’s decisions can be understood and explained. Recording the decision-making process and enabling staff to trace how a quote was generated aligns with transparency. It allows stakeholders to interpret the reasoning behind model outputs, ensuring that the AI behaves predictably and ethically.

Privacy and Security – This principle focuses on protecting personal data and ensuring that sensitive information is handled responsibly. Limiting access to customer data only to authorized personnel maintains compliance with privacy laws (like GDPR) and safeguards against misuse. Microsoft emphasizes that AI systems should maintain strict control over data visibility and integrity.

Inclusiveness – This principle ensures that AI systems are accessible to all users, including people with disabilities. By supporting screen readers and assistive technologies, the system ensures equal access to information and services for every customer. Inclusiveness prevents discrimination and promotes accessibility, both of which are central to Microsoft’s Responsible AI strategy.

Thus, the correct mapping of principles is:

Decision process → Transparency

Personal information visibility → Privacy and Security

Accessibility via screen readers → Inclusiveness.

Model Explain Ability.

Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.

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.

A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.

C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.