Quick Answer
The answer is Poisson regression, as it is the most appropriate model for a Poisson distributed target variable. This is correct because Poisson regression is a generalized linear model (GLM) specifically designed for count data where the target consists of non-negative integers and the variance equals the mean, which are the defining characteristics of a Poisson distribution. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of matching model types to target distributions, often appearing in scenario-based questions about count data like website visits or defect rates. A common trap is confusing Poisson regression with linear regression or logistic regression—linear regression assumes a normal distribution, while logistic regression handles binary outcomes. Remember the memory tip: "Poisson for counts, variance equals mean."
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 analyst is exploring a dataset and notices that the target variable has a Poisson distribution. Which type of model is most appropriate for this target?
Poisson regression
Poisson regression models count data with Poisson distribution.
Linear regression
Why wrong: Linear regression assumes normally distributed errors.
Cox proportional hazards model
Why wrong: Cox model is for time-to-event data.
Logistic regression
Why wrong: Logistic regression is for binary classification.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Poisson regression
Poisson regression is the correct choice because it is specifically designed for modeling count data where the target variable follows a Poisson distribution, which is characterized by non-negative integer values and a variance equal to the mean. This aligns directly with the data analyst's observation of a Poisson-distributed target, making Poisson regression the most appropriate generalized linear model (GLM) for this scenario.
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.
Poisson regression
Why this is correct
Poisson regression models count data with Poisson distribution.
Related concept
Read the scenario before looking for a memorised answer.
Linear regression
Why it's wrong here
Linear regression assumes normally distributed errors.
Cox proportional hazards model
Why it's wrong here
Cox model is for time-to-event data.
Logistic regression
Why it's wrong here
Logistic regression is for binary classification.
Common exam traps
The trap here is that candidates may confuse Poisson regression with logistic regression or linear regression, mistakenly applying a model for binary outcomes or continuous data to count data, without recognizing that the Poisson distribution's unique properties require a specialized GLM.
Detailed technical explanation
Poisson regression uses a log link function to model the log of the expected count as a linear combination of predictors, ensuring predictions are non-negative. A key subtlety is that Poisson regression assumes equidispersion (mean = variance); in real-world scenarios with overdispersion (variance > mean), a negative binomial regression or quasi-Poisson model may be more appropriate. This distinction is critical in fields like epidemiology, where count data (e.g., disease incidence) often exhibit overdispersion due to unobserved heterogeneity.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Poisson regression — Poisson regression is the correct choice because it is specifically designed for modeling count data where the target variable follows a Poisson distribution, which is characterized by non-negative integer values and a variance equal to the mean. This aligns directly with the data analyst's observation of a Poisson-distributed target, making Poisson regression the most appropriate generalized linear model (GLM) for this scenario.
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 24, 2026
This MLS-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 MLS-C01 exam.
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