Microsoft Azure AI Fundamentals AI-900 Exam Questions

Answer : C

When training a machine learning model, it is standard practice to randomly split the dataset into training and testing subsets. The purpose of this is to evaluate how well the model generalizes to unseen data. According to the AI-900 study guide and Microsoft Learn module ''Split data for training and evaluation'', this ensures that the model is trained on one portion of the data (training set) and evaluated on another (test or validation set).

The correct answer is C. to test the model by using data that was not used to train the model.

Random splitting prevents data leakage and overfitting, which occur when a model memorizes patterns from the training data instead of learning generalizable relationships. By testing on unseen data, developers can assess true performance, ensuring that predictions will be accurate on future, real-world data.

Options A and B are incorrect because:

A . Train the model twice does not improve accuracy; model accuracy depends on data quality, feature engineering, and algorithm choice.

B . Train multiple models simultaneously refers to model comparison, not the purpose of splitting data.

Thus, the correct reasoning is that random splitting provides a reliable estimate of the model's predictive power on new data.

Answer : D

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.

Answer : B

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Answer : D

According to the Microsoft Learn module ''Build a QnA Maker knowledge base'', QnA Maker allows developers to create bots that answer user queries based on documents like FAQs or manuals. To make a bot more natural and conversational, Microsoft provides a ''chit-chat'' feature --- a prebuilt, professionally written set of responses to common conversational phrases such as greetings (''Hello''), small talk (''How are you?''), and polite phrases (''Thank you'').

Adding chit-chat improves user experience by making the bot sound friendlier and more human-like. It doesn't alter the main Q&A logic but enhances the bot's tone and responsiveness.

The other options are not correct:

A . Increase the confidence threshold makes the bot more selective in responses but doesn't add new conversational features.

B . Enable active learning improves knowledge base accuracy over time through user feedback.

C . Create multi-turn questions adds conversational flow for related topics but doesn't add greetings or casual dialogue.

Thus, to make the bot more personable, the correct action is to Add chit-chat.

Answer : B, C, D

A transformer model is the foundational architecture behind many modern natural language processing systems such as GPT and BERT. It processes text data through multiple key stages. According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn materials, the major stages of a transformer-based large language model are tokenization, embedding calculation, and next token prediction.

Tokenization (C) -- The first step converts raw text into smaller units called tokens (words, subwords, or characters). This process allows the model to handle text in a structured numerical form rather than as raw language.

Embedding Calculation (B) -- After tokenization, the tokens are mapped into high-dimensional numeric vectors, known as embeddings. These embeddings capture semantic relationships between words and phrases so that the model can understand context and meaning.

Next Token Prediction (D) -- This stage is the heart of transformer operation, where the model predicts the next likely token in a sequence based on prior tokens. Repeated next-token predictions enable text generation, summarization, or translation.

Options A (object detection) and E (anonymization) are incorrect because they relate to vision and privacy workflows, not language modeling.

Answer : D

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's ''Explore Automated Machine Learning in Azure Machine Learning'' module, Automated ML (AutoML) is the Azure Machine Learning capability that allows users to quickly build, train, and deploy predictive models with minimal or no coding experience.

Automated ML automatically performs tasks that would normally require expert data science knowledge, such as:

Selecting appropriate algorithms (e.g., decision trees, logistic regression, random forests)

Performing hyperparameter tuning to optimize model accuracy

Handling missing data and feature scaling automatically

Generating performance metrics and best model recommendations

This feature is especially useful for business analysts, developers, or beginners who want to leverage machine learning for predictions (like sales forecasting, churn analysis, or demand prediction) without having to write complex Python code.

Other options explained:

A . ML pipelines automate and organize workflows for model training and deployment but still require pre-built models.

B . Copilot is an AI-powered assistant embedded in Microsoft tools for productivity, not a model training feature.

C . DALL-E is an image generation model under Azure OpenAI, not a predictive modeling tool.

Thus, per official Microsoft Learn content, Automated Machine Learning is the correct capability to quickly build and deploy predictive models with minimal coding.

Answer : C

rrect answer is C. Predicting the sale price of a house based on historical data, the size of the house, and the number of bedrooms.

In machine learning, regression is a supervised learning technique used to predict continuous numeric values. Microsoft's AI-900 study guide defines regression models as those that estimate relationships between variables---predicting a continuous outcome variable from one or more input features.

In this case, the house sale price is a continuous numeric value, and inputs such as size, location, and number of bedrooms are the features. Common regression algorithms include linear regression, decision tree regression, and boosted regression.

Other options represent different ML workloads:

A involves segmentation by categories (classification or clustering).

B represents clustering, grouping similar items without predefined labels.

D represents computer vision, counting animals in images rather than predicting a numeric value.

Hence, the verified answer is C. Regression.