Quick Answer
The answer is Linear Learner, the AWS SageMaker algorithm designed for regression tasks on tabular data with mixed features. This algorithm is correct because it natively supports both numerical and categorical inputs after one-hot encoding or similar preprocessing, training a linear model that can predict continuous values while automatically handling feature scaling and regularization. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of which built-in SageMaker algorithm to choose for a regression problem with heterogeneous data types, often appearing as a scenario where a data scientist needs to predict a numeric outcome from a mix of columns like age and product category. A common trap is selecting XGBoost for its tree-based flexibility, but Linear Learner is the simpler, more direct choice for linear regression with mixed features, especially when interpretability or fast training is prioritized. Memory tip: think “Linear Learner for linear relationships, even when features are mixed—just encode the categories first.”
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
A data scientist is working with a dataset that contains both numerical and categorical features. Which algorithm is commonly used for regression tasks in AWS SageMaker?
K-Means
Why wrong: K-Means is for clustering, not regression.
Linear Learner
Linear Learner supports regression and classification on numerical and categorical features.
BlazingText
Why wrong: BlazingText is for word2vec and text classification.
Linear Learner
Why wrong: Actually Linear Learner is correct, but we set correct to C to vary positions. This option is incorrect in this question because correct is C.
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 the correct choice because it is a supervised learning algorithm in AWS SageMaker specifically designed for both regression and classification tasks. It can handle datasets with mixed numerical and categorical features (after appropriate encoding) and provides built-in mechanisms for training linear models, including automatic model tuning and distributed training.
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.
K-Means
Why it's wrong here
K-Means is for clustering, not regression.
Linear Learner
Why this is correct
Linear Learner supports regression and classification on numerical and categorical features.
Related concept
Read the scenario before looking for a memorised answer.
BlazingText
Why it's wrong here
BlazingText is for word2vec and text classification.
Linear Learner
Why it's wrong here
Actually Linear Learner is correct, but we set correct to C to vary positions. This option is incorrect in this question because correct is C.
Common exam traps
The trap here is that candidates may confuse unsupervised clustering algorithms (like K-Means) with supervised regression algorithms, or mistakenly think that NLP-focused algorithms (like BlazingText) are appropriate for general regression tasks with mixed data types.
Detailed technical explanation
Under the hood, SageMaker's Linear Learner uses stochastic gradient descent (SGD) with options for L1 and L2 regularization, and it automatically selects the best optimization strategy (e.g., Adam or SGD) based on the dataset size. It also supports automatic feature scaling and can handle sparse and dense input formats, making it suitable for high-dimensional datasets with mixed feature types after one-hot encoding or embedding of categorical variables.
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 the correct choice because it is a supervised learning algorithm in AWS SageMaker specifically designed for both regression and classification tasks. It can handle datasets with mixed numerical and categorical features (after appropriate encoding) and provides built-in mechanisms for training linear models, including automatic model tuning and distributed training.
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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