This MLS-C01 practice question tests your understanding of modeling. 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 company uses Amazon SageMaker to train a linear regression model on a dataset with 10 million rows and 50 features. The training job takes 8 hours to complete. A data scientist wants to reduce the training time to under 2 hours without changing the dataset size or the model algorithm. The SageMaker instance type currently used is ml.m5.2xlarge. Which action should the data scientist take to achieve the desired training time?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker's distributed training with 4 ml.m5.2xlarge instances.
Option A is correct because using a distributed training approach with multiple ml.m5.2xlarge instances will parallelize the computation, reducing wall-clock time. Option B (increasing to ml.m5.4xlarge) provides more compute but not enough to reduce time from 8 to 2 hours (only 2x improvement). Option C (changing to ml.p3.2xlarge with GPU) is not optimal for linear regression, which is CPU-bound. Option D (reducing features) changes the dataset and is not allowed.
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.
✗
Change the instance type to ml.p3.2xlarge (GPU instance).
Why it's wrong here
Linear regression does not benefit from GPU acceleration; GPUs are for deep learning.
✗
Change the instance type to ml.m5.4xlarge (double the vCPUs and memory).
Why it's wrong here
Doubling instance size yields at most 2x speedup, insufficient to go from 8 to 2 hours.
✗
Reduce the number of features from 50 to 25.
Why it's wrong here
Reducing features alters the dataset and is not allowed per the scenario.
✓
Use SageMaker's distributed training with 4 ml.m5.2xlarge instances.
Why this is correct
Distributed training parallelizes computation across instances, significantly reducing training time.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Scenario analysis trap
Reducing features alters the dataset and is not allowed per the scenario.
Detailed technical explanation
How to think about this question
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker's distributed training with 4 ml.m5.2xlarge instances. — Option A is correct because using a distributed training approach with multiple ml.m5.2xlarge instances will parallelize the computation, reducing wall-clock time. Option B (increasing to ml.m5.4xlarge) provides more compute but not enough to reduce time from 8 to 2 hours (only 2x improvement). Option C (changing to ml.p3.2xlarge with GPU) is not optimal for linear regression, which is CPU-bound. Option D (reducing features) changes the dataset and is not allowed.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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