- A
Shuffle the data before splitting
Correct. Shuffling ensures randomness and prevents ordering bias, making the split representative of the overall distribution.
- B
Use stratified sampling for classification to preserve class proportions
Why wrong: Incorrect. Stratified sampling is a specialized technique for class imbalance, not a general practice for every split; the core principle is representativeness, not necessarily stratification.
- C
Use a 50/50 split to maximize test data
Why wrong: Incorrect. A 50/50 split is not recommended because it reduces training data too much; typical splits are 80/20 or 70/30, but the exact ratio depends on dataset size.
- D
Ensure the test set is representative of real-world distribution
Correct. The test set must mirror the real-world distribution to provide a reliable estimate of model performance on unseen data.
- E
Use a 80/20 split for large datasets
Why wrong: Incorrect. While 80/20 is a common rule of thumb, it is not a strict requirement; the key is ensuring the test set is representative and sufficiently large.
MLA-C01 Practice Question: A data scientist is splitting a dataset into…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 data scientist is splitting a dataset into training and test sets. Which two practices should they follow? (Select TWO.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Shuffle the data before splitting
Shuffling the data before splitting (Option A) is critical to avoid ordering bias, such as when the dataset is sorted by a target variable or time. Without shuffling, the training and test sets may have different distributions, leading to unreliable model evaluation. This ensures that the split is random and representative of the overall data distribution.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Shuffle the data before splitting
Why this is correct
Correct. Shuffling ensures randomness and prevents ordering bias, making the split representative of the overall distribution.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use stratified sampling for classification to preserve class proportions
Why it's wrong here
Incorrect. Stratified sampling is a specialized technique for class imbalance, not a general practice for every split; the core principle is representativeness, not necessarily stratification.
- ✗
Use a 50/50 split to maximize test data
Why it's wrong here
Incorrect. A 50/50 split is not recommended because it reduces training data too much; typical splits are 80/20 or 70/30, but the exact ratio depends on dataset size.
- ✓
Ensure the test set is representative of real-world distribution
Why this is correct
Correct. The test set must mirror the real-world distribution to provide a reliable estimate of model performance on unseen data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a 80/20 split for large datasets
Why it's wrong here
Incorrect. While 80/20 is a common rule of thumb, it is not a strict requirement; the key is ensuring the test set is representative and sufficiently large.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse a specific split ratio (like 80/20) or a technique (like stratified sampling) as a universal best practice, when the core principles are randomization and representativeness of the test set.
Detailed technical explanation
How to think about this question
Under the hood, shuffling ensures that the empirical distribution of the training set approximates the true data distribution, which is a key assumption for stochastic gradient descent and other iterative algorithms. In real-world scenarios, such as time-series data, shuffling may be inappropriate, but for i.i.d. data, it prevents temporal or ordering artifacts from biasing the split. The test set's representativeness is crucial for unbiased generalization error estimation, as any skew can lead to overconfident or misleading performance metrics.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Shuffle the data before splitting — Shuffling the data before splitting (Option A) is critical to avoid ordering bias, such as when the dataset is sorted by a target variable or time. Without shuffling, the training and test sets may have different distributions, leading to unreliable model evaluation. This ensures that the split is random and representative of the overall data distribution.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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