A bank wants to build a model that predicts whether a loan applicant will default based on historical data that includes the outcome (defaulted or not) for each past applicant. Which supervised learning task is this?

Dimensionality reduction, because the model reduces the number of features to improve prediction accuracy.

Regression, because the model predicts a continuous value such as the probability of default.

Clustering, because the model groups similar applicants together based on their features.

Classification, because the model predicts a discrete category (default or not) from labeled historical data.

Which scenario is an example of a reinforcement learning application?

Controlling a robotic arm to pick up objects by learning from trial and error.

Predicting customer churn based on labeled customer data.

Identifying fraudulent transactions using labeled historical data.

Reducing the dimensionality of a dataset for visualization.

A data scientist has a dataset with thousands of features (e.g., pixel values from images) and wants to visualize the data in 2D to check for natural groupings. The dataset has no labels. Which unsupervised learning technique is most appropriate for this task?

Dimensionality reduction, such as PCA or t-SNE, to project the high-dimensional data into 2D for visualization.

Classification, by training a model to assign each data point to a known class.

Regression, by fitting a line to the data to predict a continuous value.

Reinforcement learning, by having an agent explore the feature space to find the best projection.

A retail chain has a dataset of customer transactions but no labels. They want to identify groups of products that are frequently purchased together to improve store layout. Which unsupervised learning technique is most appropriate?

Regression, by predicting the number of items in a transaction.

Reinforcement learning, by having an agent explore different store layouts to maximize sales.

Clustering, such as market basket analysis, to find groups of products that are often purchased together.

Classification, by training a model to predict which products are in a transaction.

A cybersecurity team has a large log of network traffic data but no labels indicating whether each connection is normal or malicious. They want to identify unusual patterns that might indicate a new type of attack. Which algorithm type is most appropriate?

Reinforcement learning, by having an agent explore the network to find vulnerabilities.

Unsupervised learning, using anomaly detection to find unusual patterns in the unlabeled traffic data.

Regression, by predicting the number of connections per minute.

Supervised learning, by training a classifier on known attack signatures.

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Domain 1Objective 3

Differentiate the Use Between Different Algorithm Types (supervised, Unsupervised, Reinforcement) CERTIFIED-AI-EXPERT Practice Questions (Page 1)

Part of the AI Overview domain, which accounts for 15% of the CERTIFIED-AI-EXPERT exam. NetApp does not publish an official question count, but from its 90-minute exam (~35–60 total, ~5–9 in this domain), expect 1–2 from this objective — we provide 24 practice questions to prepare you well beyond it. (estimate)

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Questions 1–5

1application · medium
A bank wants to build a model that predicts whether a loan applicant will default based on historical data that includes the outcome (defaulted or not) for each past applicant. Which supervised learning task is this?
Select an answer first
2foundation · easy
Which scenario is an example of a reinforcement learning application?
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3application · medium
A data scientist has a dataset with thousands of features (e.g., pixel values from images) and wants to visualize the data in 2D to check for natural groupings. The dataset has no labels. Which unsupervised learning technique is most appropriate for this task?
Select an answer first
4application · medium
A retail chain has a dataset of customer transactions but no labels. They want to identify groups of products that are frequently purchased together to improve store layout. Which unsupervised learning technique is most appropriate?
Select an answer first
5application · medium
A cybersecurity team has a large log of network traffic data but no labels indicating whether each connection is normal or malicious. They want to identify unusual patterns that might indicate a new type of attack. Which algorithm type is most appropriate?
Select an answer first

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