Quick Answer
The answer is stratified random sampling, as it is the correct method to minimize bias and ensure the sample reflects the population distribution when dealing with imbalanced data. This technique divides the dataset into distinct subgroups, or strata, based on key characteristics—such as class labels—and then randomly samples from each stratum in proportion to its size in the original population. By doing so, it guarantees that rare or minority classes are represented in the sample, preventing the model from being trained on a skewed subset that overlooks critical patterns. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of sampling strategies for imbalanced datasets, a common scenario in real-world ML pipelines. A frequent trap is choosing simple random sampling, which can miss rare subgroups entirely, or systematic sampling, which may introduce bias from hidden periodicity. For a quick memory tip: think of stratified sampling as “proportional representation for every class”—it ensures no subgroup is left behind.
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
A team is performing exploratory data analysis on a dataset containing 10 million records stored in Amazon S3. They want to sample the data efficiently to build a representative subset for initial modeling. Which sampling method should they use to minimize bias and ensure the sample reflects the population distribution?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Stratified random sampling
Stratified sampling ensures representation from all strata, reducing bias.
Simple random sampling
Why wrong: Simple random sampling may not represent small subgroups well.
Systematic sampling
Why wrong: Systematic sampling can be biased if the data has patterns.
Reservoir sampling
Why wrong: Reservoir sampling is used for streaming data, not for static datasets.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Stratified random sampling
Option D is correct because stratified random sampling ensures that each subgroup (stratum) is proportionally represented, which is important for imbalanced data. Option A is wrong because simple random sampling may miss rare subgroups. Option B is wrong because systematic sampling can introduce bias if there is periodicity. Option C is wrong because reservoir sampling is for streaming data, not for static datasets.
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.
Stratified random sampling
Why this is correct
Stratified sampling ensures representation from all strata, reducing bias.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Simple random sampling
Why it's wrong here
Simple random sampling may not represent small subgroups well.
Systematic sampling
Why it's wrong here
Systematic sampling can be biased if the data has patterns.
Reservoir sampling
Why it's wrong here
Reservoir sampling is used for streaming data, not for static datasets.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — study guide chapter
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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: Stratified random sampling — Option D is correct because stratified random sampling ensures that each subgroup (stratum) is proportionally represented, which is important for imbalanced data. Option A is wrong because simple random sampling may miss rare subgroups. Option B is wrong because systematic sampling can introduce bias if there is periodicity. Option C is wrong because reservoir sampling is for streaming data, not for static datasets.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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