Microsoft Updated AI-900 Exam Questions and Answers by camilla

As outlined in the AI-900 study guide and Microsoft Learn’s “Explore fundamental principles of machine learning” module, clustering is a core example of unsupervised machine learning.

In unsupervised learning, the model is trained on data without labeled outcomes. The goal is to discover patterns or groupings naturally present in the data. Clustering algorithms, such as K-means, DBSCAN, or Hierarchical clustering, analyze similarities among data points and group them into clusters. For example, clustering can group customers by purchasing behavior or segment products by shared characteristics — all without predefined labels.

Supervised learning, by contrast, uses labeled data (input-output pairs) to train a model that predicts outcomes. This includes:

A. Multiclass classification – Predicts more than two categories (e.g., classifying images as dog, cat, or bird).

C. Binary classification – Predicts two categories (e.g., spam vs. not spam).

D. Regression – Predicts continuous numeric values (e.g., price prediction).

Therefore, the only option representing unsupervised learning is clustering, which enables data discovery without predefined labels.

In a chat solution using the Azure OpenAI GPT-3.5 model, the temperature parameter controls the creativity and variability of generated responses. According to the Microsoft Learn documentation for Azure OpenAI Service, temperature is a float value typically between 0 and 2, determining how deterministic or random the model’s output is. A lower temperature (e.g., 0–0.3) makes responses more focused and deterministic, while a higher temperature (e.g., 0.8–1.2) produces more verbose, creative, and diverse responses.

When you want the chat model to generate more detailed or expressive output, increasing the temperature encourages the model to explore a broader range of possible continuations, leading to longer and more varied text. This parameter directly affects how “verbose” or elaborate the model’s responses can be, which is why it is the correct answer.

The other options are not appropriate for this scenario:

A. Presence penalty reduces repetition by discouraging reuse of the same phrases but does not control verbosity.

C. Stop sequence defines tokens where generation should stop, limiting rather than extending response length.

D. Max response (max tokens) controls the maximum length of the response but does not inherently make answers more verbose or expressive.

Thus, to encourage more elaborate and detailed output from the Azure OpenAI GPT-3.5 model, the correct configuration parameter to adjust is Temperature (B).

Yes, Yes, No.

According to the Microsoft Azure AI Fundamentals (AI-900) study materials, conversational AI enables applications, websites, and digital assistants to interact with users via natural language. A chatbot is a key conversational AI workload and can be integrated into multiple channels such as web pages, Microsoft Teams, Facebook Messenger, and Cortana using Azure Bot Service and Bot Framework.

“A restaurant can use a chatbot to answer queries through Cortana” — Yes.Azure Bot Service supports multi-channel deployment, which includes Cortana integration. This means the same bot can respond to voice or text input via Cortana, making it a valid use case for a restaurant to provide menu details, reservations, or order tracking through voice-based AI assistants.

“A restaurant can use a chatbot to answer inquiries about business hours from a webpage” — Yes.This is a standard scenario for chatbots embedded on a company website. As per Microsoft Learn’s Describe features of conversational AI module, a chatbot can be added to a website to handle FAQs such as business hours, location, or menu details, thereby improving response time and reducing repetitive human workload.

“A restaurant can use a chatbot to automate responses to customer reviews on an external website” — No.Azure bots and other conversational AI tools cannot automatically interact with or post on external third-party platforms where the business does not control the data or API integration. Automated posting or replying to reviews on external review sites (e.g., Yelp or Google Reviews) would violate both ethical and technical boundaries of responsible AI usage outlined by Microsoft.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Explore fundamental principles of machine learning”, feature engineering is the process used to generate additional features or transform existing data into forms that improve model performance. Features are individual measurable properties or characteristics used as input for machine learning algorithms. The goal of feature engineering is to create new informative variables that better represent the underlying patterns in the data.

Feature engineering may include tasks such as:

Combining or transforming raw data columns (e.g., creating a “total purchase amount” from price × quantity).

Extracting time-based components (e.g., year, month, day, hour) from datetime values.

Encoding categorical variables (e.g., one-hot encoding or label encoding).

Scaling or normalizing numerical features.

Creating polynomial or interaction terms to capture complex relationships.

Microsoft’s AI-900 learning material emphasizes that the process of preparing data for machine learning involves data cleaning, feature engineering, and feature selection. While feature selection is about choosing the most relevant features from the existing dataset, feature engineering focuses on creating or generating new features to enhance model accuracy and generalization.

The other options do not fit this definition:

Feature selection is about removing redundant or irrelevant features, not generating new ones.

Model evaluation involves assessing the model’s performance using metrics like accuracy or F1 score.

Model training is the phase where the algorithm learns patterns from the data, not when features are created.

Therefore, based on the AI-900 official concepts and Microsoft’s documentation, the correct answer is Feature engineering, as it is the process specifically used to generate additional features that improve machine learning model performance and predictive capability.