Quick Answer
The answer is Linear Learner, XGBoost, and DeepAR, as these are all SageMaker built-in algorithms suitable for regression tasks. Linear Learner directly models a continuous target variable as a linear combination of input features, optimizing for metrics like mean squared error, making it a natural fit for regression. XGBoost, while often associated with classification, is a gradient boosting framework that excels at regression by sequentially correcting prediction errors, outputting continuous values. DeepAR is a recurrent neural network algorithm specifically designed for time-series forecasting, which is a specialized form of regression predicting future numeric values. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your ability to distinguish between built-in algorithms and their primary use cases; a common trap is assuming XGBoost is only for classification or forgetting that DeepAR is a regression algorithm for time series. Memory tip: think "LXD" — Linear, XGBoost, DeepAR — all three output numbers, not categories.
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE are SageMaker built-in algorithms suitable for regression tasks?
Linear Learner
Linear Learner supports regression.
K-Means
Why wrong: K-Means is clustering, not regression.
PCA
Why wrong: PCA is dimensionality reduction.
DeepAR
DeepAR is for time series forecasting, which is a regression task.
XGBoost
XGBoost supports regression.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Linear Learner
Linear Learner is a SageMaker built-in algorithm that supports both regression and classification tasks. For regression, it models the target variable as a linear combination of input features, optimizing for metrics like mean squared error. It is suitable for regression because it directly outputs continuous values.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Linear Learner
Why this is correct
Linear Learner supports regression.
Related concept
Read the scenario before looking for a memorised answer.
K-Means
Why it's wrong here
K-Means is clustering, not regression.
PCA
Why it's wrong here
PCA is dimensionality reduction.
DeepAR
Why this is correct
DeepAR is for time series forecasting, which is a regression task.
Related concept
Read the scenario before looking for a memorised answer.
XGBoost
Why this is correct
XGBoost supports regression.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the distinction between supervised and unsupervised algorithms, and the trap here is that candidates may confuse dimensionality reduction (PCA) or clustering (K-Means) with regression tasks, assuming any algorithm that processes numeric data can perform regression.
Detailed technical explanation
Linear Learner uses stochastic gradient descent (SGD) with automatic model tuning, supporting L1 and L2 regularization to prevent overfitting. It can handle both dense and sparse data, and under the hood, it automatically selects the best linear model variant (e.g., logistic regression for classification or linear regression for regression) based on the target type. In real-world scenarios, it is often used for predicting house prices or sales figures where relationships are approximately linear.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: Linear Learner — Linear Learner is a SageMaker built-in algorithm that supports both regression and classification tasks. For regression, it models the target variable as a linear combination of input features, optimizing for metrics like mean squared error. It is suitable for regression because it directly outputs continuous values.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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