The answer is to use SageMaker’s model parallelism strategy with the SageMaker distributed training library. This approach is correct because when a large language model with hundreds of billions of parameters exceeds the memory of a single GPU, model parallelism partitions the model’s layers across multiple GPUs, allowing each device to handle a portion of the computation while overlapping communication and training steps. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your understanding of distributed training techniques for scaling LLMs—specifically distinguishing model parallelism (splitting the model itself) from data parallelism (splitting the data). A common trap is to confuse these two: data parallelism still requires the full model to fit on one GPU, whereas model parallelism is the only viable option for truly massive models. Memory tip: think “model layers across GPUs” for model parallelism, versus “data batches across GPUs” for data parallelism.
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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 company is using Amazon SageMaker to train a large language model with hundreds of billions of parameters. The model does not fit into the memory of a single GPU. Which approach should they use to train the model efficiently?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker's model parallelism strategy with the SageMaker distributed training library
Option C is correct because SageMaker's model parallelism strategy with the SageMaker distributed training library is specifically designed for training large models that do not fit into the memory of a single GPU. It partitions the model layers across multiple GPUs, enabling efficient training of models with hundreds of billions of parameters by overlapping computation and communication.
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 a larger instance with more GPU memory, such as p4d.24xlarge
Why it's wrong here
Even with the largest instance, the model may still exceed GPU memory.
✗
Use SageMaker's data parallelism strategy
Why it's wrong here
Data parallelism replicates the model and splits data; model must fit in each GPU.
✓
Use SageMaker's model parallelism strategy with the SageMaker distributed training library
Why this is correct
Model parallelism splits the model across GPUs, enabling training of very large models.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the model size by pruning layers until it fits into memory
Why it's wrong here
Reducing model size changes the architecture and may negatively impact accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between data parallelism and model parallelism, and the trap here is that candidates may confuse data parallelism (which splits data, not the model) as a solution for models that don't fit in memory, when in fact model parallelism is required for such cases.
Detailed technical explanation
How to think about this question
SageMaker's model parallelism uses techniques like pipeline parallelism and tensor parallelism to split model layers and operations across multiple GPUs, with automatic partitioning and communication optimization via the SageMaker distributed training library. Under the hood, it leverages NVIDIA's NCCL for efficient GPU-to-GPU communication and supports automatic mixed precision (AMP) to further reduce memory usage. In real-world scenarios, this approach is critical for training models like GPT-3 or BLOOM, which require hundreds of GPUs working in concert.
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.
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker's model parallelism strategy with the SageMaker distributed training library — Option C is correct because SageMaker's model parallelism strategy with the SageMaker distributed training library is specifically designed for training large models that do not fit into the memory of a single GPU. It partitions the model layers across multiple GPUs, enabling efficient training of models with hundreds of billions of parameters by overlapping computation and communication.
What should I do if I get this AIF-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.
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Question Discussion
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