The answer is to switch to the SageMaker model parallel library with pipeline parallelism because the bottleneck is communication overhead, not compute speed. With 1.5 billion parameters, the frequent all-reduce operations in data parallelism saturate network throughput while leaving GPUs idle at only 45% utilization. Model parallelism splits the model layers across GPUs, drastically reducing the size and frequency of gradient synchronization, which allows each GPU to work continuously on its assigned portion of the model. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to distinguish between scaling strategies: data parallelism excels for smaller models that fit on a single GPU, while model parallelism is essential for large models where communication becomes the choke point. A common trap is assuming more instances or faster networking will help, but when network is already maxed out, the fix is to reduce communication, not speed it up. Remember: high network, low GPU = split the model, not the data.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 financial services company is training a large natural language processing (NLP) model using PyTorch on a SageMaker distributed training job. The cluster consists of 4 ml.p3.16xlarge instances (8 GPUs each). The training job runs successfully but takes 72 hours, exceeding the allotted 48-hour window. The team must reduce training time without sacrificing model quality. The model architecture has 1.5 billion parameters and currently uses the SageMaker data parallel library with Horovod for all-reduce. Observing CloudWatch metrics, the team notices that GPU utilization averages only 45% and network throughput is near maximum. Which action will most effectively reduce training time?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Switch to SageMaker model parallel library with pipeline parallelism to reduce communication overhead.
Option C is correct because with low GPU utilization and high network bandwidth consumption, the bottleneck is likely communication overhead. Model parallelism splits the model across GPUs, reducing the need for frequent all-reduce of large gradients, thus improving GPU utilization. Option A is wrong because increasing instance count would increase communication overhead and likely not improve utilization. Option B is wrong because data parallelism already uses GPUs; increasing batch size may cause memory overflow. Option D is wrong because enabling EFA improves network, but network is already near maximum; the bottleneck is not network speed but the frequency of communication.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Enable Elastic Fabric Adapter (EFA) for faster inter-node connectivity.
Why it's wrong here
Network is already near max; EFA may help but the root cause is communication frequency, not bandwidth.
✗
Increase the batch size to improve GPU utilization.
Why it's wrong here
Larger batch sizes may not fit into GPU memory and can degrade model quality.
✗
Increase the number of instances from 4 to 8 to add more GPUs.
Why it's wrong here
More instances increase all-reduce overhead and may not improve GPU utilization if communication is bottleneck.
✓
Switch to SageMaker model parallel library with pipeline parallelism to reduce communication overhead.
Why this is correct
Model parallelism partitions the model across devices, reducing communication volume and improving utilization.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
ML Model Development — This question tests ML Model Development — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Switch to SageMaker model parallel library with pipeline parallelism to reduce communication overhead. — Option C is correct because with low GPU utilization and high network bandwidth consumption, the bottleneck is likely communication overhead. Model parallelism splits the model across GPUs, reducing the need for frequent all-reduce of large gradients, thus improving GPU utilization. Option A is wrong because increasing instance count would increase communication overhead and likely not improve utilization. Option B is wrong because data parallelism already uses GPUs; increasing batch size may cause memory overflow. Option D is wrong because enabling EFA improves network, but network is already near maximum; the bottleneck is not network speed but the frequency of communication.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This MLA-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 MLA-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.