- A
Attach multiple EBS volumes with throughput provisioning.
Why wrong: EBS enhancements affect storage I/O, not network communication.
- B
Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).
GPU instances are necessary for large model training.
- C
Enable batch transform for offline inference after training.
Why wrong: Batch transform is for inference, not training optimization.
- D
Use Elastic Fabric Adapter (EFA) for low-latency inter-node communication.
EFA improves network performance for distributed deep learning.
- E
Select the appropriate distributed training strategy (e.g., Horovod, SageMaker data parallel, or model parallel).
Choosing the right strategy maximizes efficiency.
Question 704 of 1,000
hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: Which THREE steps should be taken to optimize a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
Which THREE steps should be taken to optimize a large-scale distributed training job on SageMaker? (Choose 3.)
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 GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).
Option B is correct because GPU instances like ml.p4d.24xlarge provide high-bandwidth GPU memory and NVLink inter-GPU connectivity, which are essential for large-scale distributed training. These instances reduce communication bottlenecks and allow larger batch sizes, directly improving throughput and model convergence speed.
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.
- ✗
Attach multiple EBS volumes with throughput provisioning.
Why it's wrong here
EBS enhancements affect storage I/O, not network communication.
- ✓
Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).
Why this is correct
GPU instances are necessary for large model training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable batch transform for offline inference after training.
Why it's wrong here
Batch transform is for inference, not training optimization.
- ✓
Use Elastic Fabric Adapter (EFA) for low-latency inter-node communication.
Why this is correct
EFA improves network performance for distributed deep learning.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Select the appropriate distributed training strategy (e.g., Horovod, SageMaker data parallel, or model parallel).
Why this is correct
Choosing the right strategy maximizes efficiency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse storage optimization (EBS) or inference features (batch transform) with training optimization, failing to recognize that distributed training performance hinges on compute, memory, and inter-node communication, not disk I/O or post-training steps.
Detailed technical explanation
How to think about this question
Under the hood, distributed training relies on gradient synchronization across nodes using algorithms like all-reduce. EFA bypasses the OS kernel to provide user-space direct memory access (RDMA) over the Elastic Fabric Adapter, achieving microsecond-level latency and up to 100 Gbps throughput, which is critical for scaling across hundreds of GPUs. In real-world scenarios, without EFA, network congestion can cause training to stall, especially with large models like GPT-3 or BERT-large.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge). — Option B is correct because GPU instances like ml.p4d.24xlarge provide high-bandwidth GPU memory and NVLink inter-GPU connectivity, which are essential for large-scale distributed training. These instances reduce communication bottlenecks and allow larger batch sizes, directly improving throughput and model convergence speed.
What should I do if I get this MLA-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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Last reviewed: Jul 4, 2026
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.
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