- A
Use SageMaker's distributed data parallelism (SMDDP) library to shard the model across GPUs.
Why wrong: SMDDP is for data parallelism, not model parallelism. It replicates the model on each GPU and splits the data, which does not reduce memory footprint per GPU for large models.
- B
Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism.
SMP allows splitting the model across multiple GPUs and instances, reducing memory footprint per GPU and enabling training of large models that would otherwise not fit. This complements data parallelism.
- C
Use SageMaker's managed training with a single instance containing multiple GPUs and enable data parallelism.
Why wrong: This approach uses only one instance, so it does not distribute across multiple instances. While multiple GPUs on one instance help, the question asks for actions to reduce training time by distributing across both GPUs and instances.
- D
Use Horovod for data parallelism across multiple instances.
Horovod is a distributed training framework that supports data parallelism across multiple instances, reducing training time by processing more data in parallel.
- E
Set the instance type to a single GPU instance and rely on automatic model parallelism.
Why wrong: A single GPU instance limits parallelism. Automatic model parallelism is not a built-in SageMaker feature; training on one GPU would be slower.
Quick Answer
The correct answer is to use SageMaker’s model parallelism library to split the large deep learning model across multiple GPUs on a single instance, and Horovod for data parallelism across multiple instances. Model parallelism is necessary here because the model has many layers that cannot fit into a single GPU’s memory; SageMaker’s library handles this by partitioning the model across devices using pipeline or tensor parallelism, allowing each GPU to compute a portion of the forward and backward pass. Data parallelism with Horovod then replicates the model across instances, synchronizing gradients via allreduce to process larger batches of data in parallel. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to distinguish between scaling within an instance (model parallelism) and across instances (data parallelism)—a common trap is choosing only one approach when the question explicitly requires both. Memory tip: think “model splits layers, data splits data”—if the model is too big, use model parallelism; if the dataset is too big, use data parallelism.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 science team is training a large deep learning model using Amazon SageMaker. The training job is taking a long time because the model has many layers and the dataset is large. The team wants to reduce training time by distributing the training across multiple GPUs on a single instance, as well as across multiple instances. Which TWO actions should the team take? (Choose two.)
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
Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism.
Option B is correct because the SageMaker model parallelism (SMP) library is specifically designed to split large deep learning models across multiple GPUs using pipeline or tensor parallelism. This allows the team to train models that are too large to fit on a single GPU and to reduce training time by parallelizing computation across devices within and across instances.
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.
- ✗
Use SageMaker's distributed data parallelism (SMDDP) library to shard the model across GPUs.
Why it's wrong here
SMDDP is for data parallelism, not model parallelism. It replicates the model on each GPU and splits the data, which does not reduce memory footprint per GPU for large models.
- ✓
Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism.
Why this is correct
SMP allows splitting the model across multiple GPUs and instances, reducing memory footprint per GPU and enabling training of large models that would otherwise not fit. This complements data parallelism.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker's managed training with a single instance containing multiple GPUs and enable data parallelism.
Why it's wrong here
This approach uses only one instance, so it does not distribute across multiple instances. While multiple GPUs on one instance help, the question asks for actions to reduce training time by distributing across both GPUs and instances.
- ✓
Use Horovod for data parallelism across multiple instances.
Why this is correct
Horovod is a distributed training framework that supports data parallelism across multiple instances, reducing training time by processing more data in parallel.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the instance type to a single GPU instance and rely on automatic model parallelism.
Why it's wrong here
A single GPU instance limits parallelism. Automatic model parallelism is not a built-in SageMaker feature; training on one GPU would be slower.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse data parallelism (which shards data) with model parallelism (which shards the model), and assume that simply using multiple GPUs on a single instance automatically distributes the model, when in fact explicit model parallelism libraries like SMP are required for large models that do not fit in GPU memory.
Detailed technical explanation
How to think about this question
SageMaker model parallelism (SMP) partitions the model layers across GPUs using pipeline parallelism (where different GPUs handle different sequential stages) or tensor parallelism (where individual layers are split across GPUs). This is critical for models with hundreds of layers, such as GPT-3 or BERT-large, where the parameter count exceeds the memory of a single GPU. SMP also integrates with SageMaker's distributed training launcher to automatically handle communication between GPUs using NCCL, reducing the engineering overhead of manual sharding.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism. — Option B is correct because the SageMaker model parallelism (SMP) library is specifically designed to split large deep learning models across multiple GPUs using pipeline or tensor parallelism. This allows the team to train models that are too large to fit on a single GPU and to reduce training time by parallelizing computation across devices within and across instances.
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
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
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. A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long and the scientist wants to reduce the training time by distributing the workload across multiple GPUs. Which SageMaker feature should be used to achieve this?
easy- ✓ A.Use SageMaker's distributed training libraries
- B.Use Amazon EMR to distribute the training
- C.Use SageMaker Automatic Model Tuning
- D.Use SageMaker Hyperparameter Tuning
Why A: Option D is correct because SageMaker's distributed training libraries enable efficient distribution across multiple GPUs. Option A is wrong because Hyperparameter Tuning is for optimizing hyperparameters, not for distributed training. Option B is wrong because Automatic Model Tuning is for hyperparameter optimization. Option C is wrong because Amazon EMR is for big data processing, not for deep learning training.
Variation 2. A data scientist is using SageMaker to train a deep learning model. The training script uses TensorFlow and runs on a single p3.2xlarge instance. The scientist wants to reduce training time by using multiple GPUs. What should the scientist do?
medium- A.Increase the instance count to 4 without changing the script.
- ✓ B.Modify the training script to use Horovod for distributed training.
- C.Switch to PyTorch framework.
- D.Use SageMaker Managed Spot Training.
Why B: Modifying the training script to use Horovod (Option B) is required for distributed training across multiple GPUs. Option A (increasing instance type) only uses more GPUs on one machine if the script supports it. Option C (using Spot) does not add GPUs. Option D (changing to PyTorch) is unnecessary.
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Last reviewed: Jun 11, 2026
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