- A
Disable automatic scaling to avoid interruptions
Why wrong: Automatic scaling helps manage resources efficiently; disabling it may cause performance issues.
- B
Use SageMaker Debugger to profile system bottlenecks
Debugger provides insights into GPU utilization and I/O bottlenecks.
- C
Use Pipe mode for training data stored in S3 to reduce startup time
Pipe mode streams data directly from S3, reducing download time and disk space.
- D
Always use the largest instance type available for faster training
Why wrong: Larger instances may not always be cost-effective or necessary; choose based on workload.
- E
Use managed spot training to reduce cost
Managed spot training can significantly reduce costs by using spare EC2 capacity.
Question 469 of 1,755
Best Practices for Training Deep Learning Models on SageMaker
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.
Which THREE of the following are best practices for training deep learning models on Amazon SageMaker?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Use SageMaker Debugger to profile system bottlenecks
SageMaker Debugger is a best practice because it provides real-time profiling of system bottlenecks such as CPU/GPU utilization, memory I/O, and network throughput during training. This allows you to identify and resolve performance issues early, optimizing training efficiency and cost. It integrates directly with SageMaker's training jobs without requiring code changes.
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.
- ✗
Disable automatic scaling to avoid interruptions
Why it's wrong here
Automatic scaling helps manage resources efficiently; disabling it may cause performance issues.
- ✓
Use SageMaker Debugger to profile system bottlenecks
Why this is correct
Debugger provides insights into GPU utilization and I/O bottlenecks.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Pipe mode for training data stored in S3 to reduce startup time
Why this is correct
Pipe mode streams data directly from S3, reducing download time and disk space.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Always use the largest instance type available for faster training
Why it's wrong here
Larger instances may not always be cost-effective or necessary; choose based on workload.
- ✓
Use managed spot training to reduce cost
Why this is correct
Managed spot training can significantly reduce costs by using spare EC2 capacity.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'avoiding interruptions' with disabling automatic scaling, when in fact automatic scaling is designed to prevent interruptions by dynamically adjusting capacity, and disabling it increases the risk of failures.
Detailed technical explanation
How to think about this question
SageMaker Debugger uses hooks to capture tensors and metrics from the training framework (e.g., TensorFlow, PyTorch) and streams them to Amazon S3 or CloudWatch for analysis. It can detect issues like vanishing gradients, overfitting, or hardware bottlenecks by comparing against built-in rules. In a real-world scenario, profiling with Debugger might reveal that a GPU-bound model is actually I/O-bound due to slow data loading, prompting a switch to Pipe mode or a larger instance with higher network throughput.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Visual reference
What to study next
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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: Use SageMaker Debugger to profile system bottlenecks — SageMaker Debugger is a best practice because it provides real-time profiling of system bottlenecks such as CPU/GPU utilization, memory I/O, and network throughput during training. This allows you to identify and resolve performance issues early, optimizing training efficiency and cost. It integrates directly with SageMaker's training jobs without requiring code changes.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
3 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO of the following are best practices for training deep learning models on Amazon SageMaker? (Select TWO.)
medium- A.Use SageMaker Processing to perform data augmentation before training.
- ✓ B.Use Pipe input mode to stream data directly from S3 to the algorithm.
- C.Store training data on Amazon EBS volumes attached to the training instance.
- ✓ D.Use managed spot training to reduce costs.
- E.Disable checkpointing to improve training speed.
Why B: Option B is correct because SageMaker's Pipe input mode streams training data directly from Amazon S3 to the algorithm without writing it to disk, reducing I/O latency and eliminating the need for large local storage. This is especially beneficial for deep learning models that iterate over large datasets, as it allows training to start faster and avoids the overhead of downloading data to EBS volumes.
Variation 2. Which THREE of the following are best practices for training a deep learning model on Amazon SageMaker?
hard- ✓ A.Use Pipe mode for large datasets to reduce I/O overhead
- B.Use SageMaker Debugger to automatically fix training errors
- ✓ C.Set up automatic model tuning (hyperparameter optimization)
- ✓ D.Use SageMaker Debugger to profile GPU utilization
- E.Train on a single instance to avoid distributed training overhead
Why A: Profiling GPU utilization helps identify bottlenecks. Using Pipe mode for large datasets reduces I/O. Setting up automatic model tuning (hyperparameter optimization) is a best practice. Training on a single instance is not a best practice for large models. Debugger is for monitoring, not for training acceleration.
Variation 3. Which TWO options are best practices for training machine learning models using SageMaker? (Choose TWO.)
medium- A.Train the final model on the combined training and test sets to maximize data usage
- ✓ B.Use incremental training when you have new data that is similar to the original training data
- ✓ C.Use SageMaker Managed Spot Training to reduce training costs
- D.Always use the largest possible instance type to minimize training time
- E.Always enable checkpointing to save the model after every epoch
Why B: Option B is correct because SageMaker's incremental training allows you to continue training an existing model with new data that shares the same schema and feature space, without retraining from scratch. This is a best practice when you have a steady stream of similar data, as it saves time and compute resources while preserving previously learned patterns.
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Last reviewed: Jun 24, 2026
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