Microsoft Azure AI Fundamentals - AI-900 Exam Practice Test
To complete the sentence, select the appropriate option in the answer area.
Correct Answer:
Explanation:
Reliability and safety: To build trust, it ' s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles AI systems should perform reliably and safely. For example, consider an AI-based software system for an autonomous vehicle; or a machine learning model that diagnoses patient symptoms and recommends prescriptions. Unreliability in these kinds of system can result in substantial risk to human life.
https://docs.microsoft.com/en-us/learn/modules/get-started-ai-fundamentals/7-understand-responsible-ai
Which scenario is an example of a webchat bot?
Correct Answer: A
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Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", regression is a supervised machine learning technique used to predict continuous numerical values based on one or more input features. In this scenario, the task is to predict a vehicle's miles per gallon (MPG)-a continuous numeric value-based on several measurable factors such as weight, engine power, and other specifications.
Regression models learn the mathematical relationship between input variables (independent features) and a numeric target variable (dependent outcome). Common regression algorithms include linear regression, decision tree regression, and support vector regression. In the example, the model would analyze historical data of vehicles and learn patterns that map characteristics (like engine size, horsepower, and weight) to fuel efficiency. Once trained, it can predict the MPG for a new vehicle configuration.
The other options describe different problem types:
* Classification predicts discrete categories (for example, whether a car is "fuel efficient" or "not fuel efficient"), not continuous values.
* Clustering is an unsupervised learning method that groups data points based on similarities without predefined labels, not predictive modeling.
* Anomaly detection identifies data points that significantly deviate from normal patterns, such as detecting engine sensor failures or fraudulent transactions.
Since predicting MPG involves estimating a numeric value within a continuous range, regression is the most appropriate model type.
In summary, per AI-900 training content, regression models are used when the output variable is numeric, classification for categorical outputs, and clustering for pattern discovery. Therefore, predicting miles per gallon based on vehicle features is a textbook example of a regression problem in Azure Machine Learning.
You are building a knowledge base by using QnA Maker. Which file format can you use to populate the knowledge base?
Correct Answer: C
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You need to identify harmful content in a generative Al solution that uses Azure OpenAI Service.
What should you use?
What should you use?
Correct Answer: D
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Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
Safety system.
According to the Microsoft Learn documentation and the AI-900: Microsoft Azure AI Fundamentals official study guide, the safety system layer in generative AI architecture plays a crucial role in monitoring, filtering, and mitigating harmful or unsafe model outputs. This layer works alongside the model and user experience layers to ensure that generative AI systems-such as those powered by Azure OpenAI-produce responses that are safe, aligned, and responsible.
The safety system layer uses various techniques including content filtering, prompt moderation, and policy enforcement to prevent outputs that could be harmful, biased, misleading, or inappropriate. It evaluates both user inputs (prompts) and model-generated outputs to identify and block unsafe or unethical content. The system might use predefined rules, classifiers, or human feedback signals to decide whether to allow, modify, or stop a response.
In contrast, the other layers serve different purposes:
* The model layer contains the core large language or generative model (e.g., GPT or DALL-E) that processes inputs and produces outputs.
* The metaprompt and grounding layer ensures the model's responses are contextually relevant and factually supported, often linking to organizational data sources or system prompts.
* The user experience layer defines how users interact with the AI system, including the interface and conversational flow, but does not manage safety enforcement.
Therefore, the layer that uses system inputs and context to mitigate harmful outputs from a generative AI model is the Safety system layer.
This aligns with Microsoft's responsible AI principles-Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability-ensuring generative AI operates ethically and safely.
What is an example of the Microsoft responsible Al principle of transparency?
Correct Answer: D
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Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE; Each correct selection is worth one point.
NOTE; Each correct selection is worth one point.
Correct Answer: B,D
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To complete the sentence, select the appropriate option in the answer area.
Correct Answer:
Explanation:
Features
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore fundamental principles of machine learning," data values that influence the prediction of a model are called features. In the context of machine learning, a feature is an individual measurable property, attribute, or input variable used by the model to make predictions.
Features are the independent variables that describe the characteristics of the data. For example, in a housing price prediction model, features might include square footage, location, number of bedrooms, and year built.
These inputs help the model understand relationships in the data so it can predict the target outcome (the house price).
Microsoft Learn explains that features are the input variables that the algorithm uses to identify patterns and relationships in the training data. During training, the model learns how changes in these features influence the label (also known as the dependent variable or target variable). The label is the value the model tries to predict-such as "price," "category," or "yes/no." Here's how the other options differ:
* Dependent variables (labels): These are the outcomes or target values the model predicts, not the inputs.
* Identifiers: These are unique keys (like customer ID or transaction ID) used to distinguish records but not to influence predictions.
* Labels: As mentioned, labels are the results the model tries to predict.
Therefore, based on the AI-900 learning objectives and Microsoft's official explanation, the data values that influence the prediction of a model-that is, the input variables that guide the model's learning-are called features. These features form the foundation of the model's predictive capabilities and directly impact its accuracy and performance.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
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 classification technique is a type of supervised machine learning used to predict which category or class a new observation belongs to, based on patterns learned from labeled training data.
In this scenario, a banking system that predicts whether a loan will be repaid is dealing with a binary outcome-either the loan will be repaid or will not be repaid. These two possible results represent distinct classes, making this problem a classic example of binary classification. During training, the model learns from historical data containing features such as customer income, credit score, loan amount, and repayment history, along with labeled outcomes (repaid or defaulted). After training, it can classify new applications into one of these two categories.
The AI-900 curriculum distinguishes between three key supervised and unsupervised learning approaches:
* Classification: Predicts discrete categories (e.g., spam/not spam, fraud/not fraud, will repay/won't repay).
* Regression: Predicts continuous numerical values (e.g., house prices, sales forecast, temperature).
* Clustering: Groups data based on similarity without predefined labels (e.g., customer segmentation).
Since the banking problem focuses on predicting a categorical outcome rather than a continuous numeric value, it fits squarely into the classification domain. In Azure Machine Learning, such tasks can be performed using algorithms like Logistic Regression, Decision Trees, or Support Vector Machines (SVMs), all configured for categorical prediction.
Therefore, per Microsoft's official AI-900 learning objectives, a banking system predicting whether a loan will be repaid represents a classification type of machine learning problem.