AI-900 Practice Questions

Box 1: Yes

Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

Box 2: No

Box 3: Yes

During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to " fit " your data. It will stop once it hits the exit criteria defined in the experiment.

Box 4: No

Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.

The label is the column you want to predict.

The Azure AI Language service (part of Azure Cognitive Services) provides a set of natural language processing (NLP) capabilities designed to analyze and interpret text data. Its core features include language detection, key phrase extraction, sentiment analysis, and named entity recognition (NER).

Language Identification – YESAccording to the Microsoft Learn module “Analyze text with Azure AI Language,” one of the service’s built-in capabilities is language detection, which determines the language of a given text string (e.g., English, Spanish, or French). This allows applications to automatically adapt to multilingual input.

Handwritten Signature Detection – NOThe Azure AI Language service only processes text-based data; it does not analyze images or handwriting. Detecting handwritten signatures requires computer vision capabilities, specifically Azure AI Vision or Azure AI Document Intelligence, which can extract and interpret visual content from scanned documents or images.

Identifying Companies and Organizations – YESThe Named Entity Recognition (NER) feature within Azure AI Language can identify entities such as people, locations, dates, organizations, and companies mentioned in text. It tags these entities with categories, enabling structured analysis of unstructured data.

✅ Summary:

Language detection → Yes (supported by AI Language).

Handwritten signatures → No (requires Computer Vision).

Entity recognition for companies/organizations → Yes (supported by AI Language NER).

This question is derived from the Microsoft Azure AI Fundamentals (AI-900) learning module, particularly under “Describe features of conversational AI workloads on Azure.” It tests understanding of chatbot capabilities and design principles within the context of Azure Bot Service and Conversational AI.

Chatbots can support voice input – YesAccording to the AI-900 official materials, conversational AI systems such as chatbots can interact with users through text or voice. Using speech recognition services like Azure Cognitive Services Speech-to-Text, bots can interpret spoken input, and with Text-to-Speech, they can respond verbally. This enables voice-based chatbots used in virtual assistants, call centers, and customer support. Hence, voice input is fully supported by conversational AI solutions in Azure.

A separate chatbot is required for each communication channel – NoThe Azure Bot Service is designed to provide multi-channel communication from a single bot instance. A single chatbot can communicate across several channels such as Microsoft Teams, Web Chat, Slack, Facebook Messenger, and email without needing separate bots for each platform. This centralized design allows developers to create, deploy, and manage one bot while configuring multiple channel connections through the Azure portal. Therefore, the statement is false.

Chatbots manage conversation flows by using a combination of natural language and constrained option responses – YesIn Microsoft’s AI-900 training, chatbots are described as using Natural Language Processing (NLP) to understand free-form user input while also guiding interactions with predefined options such as buttons or quick replies. This hybrid approach ensures both flexibility and control, improving user experience and accuracy. Bots can interpret natural language via services like Language Understanding (LUIS) and also present structured options to guide conversations efficiently.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules on machine learning concepts, ensuring that the accuracy of a predictive model can be proven requires data partitioning—specifically splitting the available data into training and testing datasets. This is a foundational concept in supervised machine learning.

When you split the data, typically about 70–80% of the dataset is used for training the model, while the remaining 20–30% is used for testing (or validation). The reason behind this approach is to ensure that the model’s performance metrics—such as accuracy, precision, recall, and F1-score—are evaluated on data the model has never seen before. This prevents overfitting and allows you to demonstrate that the model generalizes well to new, unseen data.

In the AI-900 Microsoft Learn content under “Describe the machine learning process”, it is explained that after cleaning and transforming the data, the next essential step is data splitting to “evaluate model performance objectively.” By keeping training and testing data separate, you can prove the reliability and accuracy of the model’s predictions, which is particularly crucial in sensitive domains like clinical or healthcare analytics, where decision transparency and validation are vital.

Option A (Train the model by using the clinical data) is incorrect because you should not train and evaluate on the same data—it would lead to biased results.

Option C (Train the model using automated ML) is incorrect because automated ML is a method for training and tuning, but it doesn’t inherently prove accuracy.

Option D (Validate the model by using the clinical data) is also incorrect if you use the same dataset for validation and training—it would not prove true accuracy.

Therefore, per Microsoft’s official AI-900 study content, the verified correct answer is

B.  

Box 1: Select Columns in Dataset

For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.

Example:

The task is to build a machine learning model in Azure Machine Learning designer to predict automobile prices, which is a regression problem since the output (price) is a continuous numeric value. The pipeline must follow the logical data preparation, training, and evaluation flow as outlined in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn module “Create a machine learning model with Azure Machine Learning designer.”

Here’s the correct sequence and reasoning:

Select Columns in Dataset:The first step after loading the raw automobile dataset is to choose the relevant columns that will be used as features (inputs) and the label (output). This module ensures that only necessary fields (for example, horsepower, engine size, mileage, etc.) are used to train the model while excluding irrelevant columns like vehicle ID or serial number.

Split Data:Next, the cleaned and filtered dataset must be split into two subsets: training data and testing data (often 70/30 or 80/20). This allows the model to be trained on one portion and evaluated on the other to measure predictive accuracy.

Linear Regression:Since automobile price prediction is a numeric prediction task, the appropriate learning algorithm is Linear Regression. This supervised algorithm learns relationships between numeric features and the target (price).

Finally, the workflow connects the training data and Linear Regression module to the Train Model module, which outputs a trained regression model. The trained model is then linked to the Score Model module to compare predicted vs. actual prices.

This pipeline fully aligns with Microsoft’s recommended process for regression in Azure ML Designer.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore conversational AI in Microsoft Azure,” the QnA Maker (now integrated into the Azure AI Language Service as Custom Question Answering) is used to create, train, and publish a knowledge base of question-and-answer pairs that can power a chatbot.

There are three primary methods to create Q & A content:

Generate questions and answers from an existing webpage (Option A):QnA Maker can automatically extract question–answer pairs from structured or semi-structured data sources like FAQs, product manuals, or support webpages.

Manually enter questions and answers (Option C):Users can create Q & A pairs directly in the QnA Maker portal or Azure Language Studio, enabling custom answers to be crafted manually.

Import chit-chat content from a predefined data source (Option E):QnA Maker provides predefined “chit-chat” datasets that let a bot handle casual conversation (e.g., greetings or small talk) naturally.

The other options are incorrect:

B.  

Use automated machine learning – AutoML is for predictive modeling, not knowledge extraction.

D.  

Connect the bot to Cortana – This is a channel integration, not a method of content creation.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of common AI workloads and considerations”, Microsoft has defined six guiding principles for responsible AI. These principles are intended to ensure that AI systems are developed and deployed in ways that are ethical, transparent, and beneficial to all. The six principles are: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability.

Let’s break down the three correct options:

Fairness – Microsoft emphasizes that AI systems should treat all individuals fairly and avoid discrimination against people based on gender, race, age, or other characteristics. Fairness ensures that outcomes and decisions from AI systems are equitable across diverse user groups. In the AI-900 learning materials, fairness is explained as a foundational value that ensures algorithms and models do not introduce or amplify societal bias.

Reliability and Safety – This principle ensures that AI systems function as intended under all expected conditions and that they can handle unexpected inputs safely. Microsoft states that AI should be tested rigorously and validated for reliability before deployment. AI systems must perform consistently and avoid causing harm due to errors or failures.

Inclusiveness – Inclusiveness focuses on empowering everyone and engaging people of all backgrounds. Microsoft’s responsible AI guidance stresses designing AI systems that understand and respect cultural, linguistic, and ability differences to make technology accessible and beneficial to all users.

Options A (knowledgeability), B (decisiveness), and E (opinionatedness) are not part of Microsoft’s Responsible AI principles. These terms do not appear in any Microsoft Learn AI-900 curriculum or official responsible AI documentation.

Thus, based on the verified AI-900 study content and Microsoft’s Responsible AI framework, the correct answer is

C.  

D.  

F.