- A
Reduce the number of features by using PCA before training.
Why wrong: Reducing features with PCA can lower memory usage but may not be enough for 100 GB data and can degrade model quality. It addresses the symptom, not the root cause.
- B
Use SageMaker Pipe mode to stream data directly from S3 instead of downloading it.
Why wrong: Pipe mode streams data in chunks, but XGBoost still accumulates data in memory for training. Without distributed training, memory may still be exhausted.
- C
Increase the number of training instances to use distributed training with XGBoost.
Distributed training across multiple instances splits the dataset and model, significantly reducing per-instance memory footprint, directly solving the memory error.
- D
Use SageMaker BlazingText algorithm with negative sampling.
Why wrong: BlazingText is for text data and not applicable to gradient boosting; it does not solve the memory issue for XGBoost.
- E
Switch to SageMaker Linear Learner algorithm, which requires less memory.
Why wrong: Switching to Linear Learner does not guarantee lower memory usage; for large datasets, it may still encounter memory errors. It is not a robust fix.
Resolve XGBoost Memory Error with Distributed Training on SageMaker
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 training a gradient boosting model on a large dataset (100 GB) stored in Amazon S3. The training job uses a SageMaker built-in XGBoost algorithm with a single ml.p3.2xlarge instance. The job fails with a memory error. Which solution should the data scientist adopt to resolve the memory issue?
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
Increase the number of training instances to use distributed training with XGBoost.
The memory error occurs because a single ml.p3.2xlarge instance cannot hold the entire 100 GB dataset in memory during training. Option C resolves this by distributing the data across multiple instances, reducing the per-instance memory load. Option B (Pipe mode) streams data from S3 without downloading it fully, but XGBoost still requires the entire dataset to be loaded into memory if not using distributed training, so it may not fully resolve the memory issue. Option A (PCA) reduces features but may not sufficiently reduce memory usage and risks information loss. Option D (BlazingText) is a different algorithm not suited for gradient boosting. Option E (Linear Learner) also requires significant memory for large datasets and is not a direct fix for memory errors in XGBoost.
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.
- ✗
Reduce the number of features by using PCA before training.
Why it's wrong here
Reducing features with PCA can lower memory usage but may not be enough for 100 GB data and can degrade model quality. It addresses the symptom, not the root cause.
- ✗
Use SageMaker Pipe mode to stream data directly from S3 instead of downloading it.
Why it's wrong here
Pipe mode streams data in chunks, but XGBoost still accumulates data in memory for training. Without distributed training, memory may still be exhausted.
- ✓
Increase the number of training instances to use distributed training with XGBoost.
Why this is correct
Distributed training across multiple instances splits the dataset and model, significantly reducing per-instance memory footprint, directly solving the memory error.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker BlazingText algorithm with negative sampling.
Why it's wrong here
BlazingText is for text data and not applicable to gradient boosting; it does not solve the memory issue for XGBoost.
- ✗
Switch to SageMaker Linear Learner algorithm, which requires less memory.
Why it's wrong here
Switching to Linear Learner does not guarantee lower memory usage; for large datasets, it may still encounter memory errors. It is not a robust fix.
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.
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
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.
Visual reference
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Increase the number of training instances to use distributed training with XGBoost. — The memory error occurs because a single ml.p3.2xlarge instance cannot hold the entire 100 GB dataset in memory during training. Option C resolves this by distributing the data across multiple instances, reducing the per-instance memory load. Option B (Pipe mode) streams data from S3 without downloading it fully, but XGBoost still requires the entire dataset to be loaded into memory if not using distributed training, so it may not fully resolve the memory issue. Option A (PCA) reduces features but may not sufficiently reduce memory usage and risks information loss. Option D (BlazingText) is a different algorithm not suited for gradient boosting. Option E (Linear Learner) also requires significant memory for large datasets and is not a direct fix for memory errors in XGBoost.
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.
About these practice questions
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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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