The correct answer is to modify the training script to use `torch.nn.parallel.DistributedDataParallel` and set `instance_count` to 2 in the SageMaker PyTorch estimator. This is required because PyTorch distributed data parallelism on SageMaker relies on DistributedDataParallel (DDP) to synchronize gradients across multiple nodes, while the `instance_count` parameter tells SageMaker to launch multiple training instances—each with its own GPU—enabling true multi-node parallelism rather than just multi-GPU on a single machine. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how SageMaker abstracts distributed training infrastructure: a common trap is confusing `DataParallel` (single-node, multi-GPU) with `DistributedDataParallel` (multi-node), or forgetting that SageMaker requires you to increase `instance_count` explicitly. Remember the mnemonic: “DDP for distributed, count up for clusters”—if you see multiple instances in the question, your script needs DDP and your estimator needs a higher instance count.
MLS-C01 Modeling Practice Question
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 data scientist is training a deep learning model on Amazon SageMaker using a PyTorch estimator. The training job runs on a single ml.p3.2xlarge instance but is taking too long. The scientist wants to reduce training time by using distributed data parallelism across multiple GPUs. Which change to the training script and SageMaker estimator is required?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Modify the script to use torch.nn.parallel.DistributedDataParallel and set instance_count to 2 in the estimator.
Option C is correct because to achieve distributed data parallelism across multiple GPUs on multiple instances with PyTorch, you must modify the training script to use `torch.nn.parallel.DistributedDataParallel` (DDP), which handles gradient synchronization across nodes. Additionally, you must set `instance_count` to 2 (or more) in the SageMaker PyTorch estimator to launch multiple instances, each with its own GPU, enabling true multi-node distributed training.
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.
✗
Add the SageMaker distributed data parallelism configuration in the estimator and modify the script to use the SageMaker distributed library.
Why it's wrong here
That is for SageMaker's own distributed library, not PyTorch DDP.
✗
Change the framework to TensorFlow and use tf.distribute.MirroredStrategy with instance_count=2.
Why it's wrong here
Changing framework is unnecessary and may introduce compatibility issues.
✓
Modify the script to use torch.nn.parallel.DistributedDataParallel and set instance_count to 2 in the estimator.
Why this is correct
DDP is efficient for multi-node training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Modify the script to use torch.nn.DataParallel and keep instance_count as 1.
Why it's wrong here
DataParallel is single-node multi-GPU; it won't help with multiple instances.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between `DataParallel` (single-node, multi-GPU) and `DistributedDataParallel` (multi-node, multi-GPU), leading candidates to incorrectly choose `DataParallel` because they overlook the requirement for multiple instances.
Detailed technical explanation
How to think about this question
Under the hood, `DistributedDataParallel` uses the NCCL backend for efficient all-reduce operations across GPUs, leveraging the `torch.distributed` package which initializes the process group via environment variables set by SageMaker (e.g., `MASTER_ADDR`, `MASTER_PORT`, `WORLD_SIZE`, `RANK`). A subtle behavior is that DDP requires the training script to be launched with `torch.distributed.launch` or `torchrun`, but SageMaker's PyTorch estimator automatically handles this when `instance_count > 1` and the script uses DDP. In a real-world scenario, using DDP with multiple `ml.p3.2xlarge` instances (each with 1 GPU) can achieve near-linear speedup for large models, whereas `DataParallel` would be limited to a single instance's GPU memory and compute.
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.
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: Modify the script to use torch.nn.parallel.DistributedDataParallel and set instance_count to 2 in the estimator. — Option C is correct because to achieve distributed data parallelism across multiple GPUs on multiple instances with PyTorch, you must modify the training script to use `torch.nn.parallel.DistributedDataParallel` (DDP), which handles gradient synchronization across nodes. Additionally, you must set `instance_count` to 2 (or more) in the SageMaker PyTorch estimator to launch multiple instances, each with its own GPU, enabling true multi-node distributed training.
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.
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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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. A data scientist is training a deep learning model on Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single GPU instance. The model is a large transformer with millions of parameters. Which change would most likely reduce training time?
hard
A.Reduce the batch size to fit in memory
B.Use a smaller instance type
C.Switch to a CPU instance
✓ D.Use SageMaker's distributed data parallelism with multiple GPU instances
Why D: Using data parallelism with multiple GPU instances can significantly reduce training time for large models by distributing the workload across multiple GPUs. Model parallelism is also possible but data parallelism is more common and easier to implement.
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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.
Question Discussion
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