MLS-C01 Modeling Practice Question
Which THREE of the following are best practices for training a deep learning model on Amazon SageMaker?
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 Pipe mode for large datasets to reduce I/O overhead
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Pipe mode for large datasets to reduce I/O overhead
Why this is correct
Pipe mode streams data directly, reducing disk I/O.
- ✗
Use SageMaker Debugger to automatically fix training errors
Why it's wrong here
Debugger monitors but does not automatically fix errors.
- ✓
Set up automatic model tuning (hyperparameter optimization)
Why this is correct
Automatic tuning helps find optimal hyperparameters efficiently.
- ✓
Use SageMaker Debugger to profile GPU utilization
Why this is correct
Debugger can profile hardware metrics to identify bottlenecks.
- ✗
Train on a single instance to avoid distributed training overhead
Why it's wrong here
For large models, distributed training is often necessary.
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Same concept, more angles
2 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: 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 deep learning models on Amazon SageMaker?
medium- A.Disable automatic scaling to avoid interruptions
- ✓ B.Use SageMaker Debugger to profile system bottlenecks
- ✓ C.Use Pipe mode for training data stored in S3 to reduce startup time
- D.Always use the largest instance type available for faster training
- ✓ E.Use managed spot training to reduce cost
Why B: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.