- A
EC2 GPU instances (P and G family)
Why wrong: GPU instances use NVIDIA GPUs — AWS's custom ML chips (Inferentia and Trainium) are purpose-built for ML and offer better performance/cost for those workloads.
- B
EC2 Inf and Trn instances (AWS Inferentia and Trainium)
AWS Inferentia (Inf instances) and Trainium (Trn instances) are custom AWS-designed ML chips that provide high-throughput, cost-effective ML inference and training.
- C
EC2 Compute-optimized instances (C family)
Why wrong: C-family instances use high-performance Intel/AMD CPUs — they're designed for general compute-intensive workloads, not ML acceleration.
- D
AWS Lambda with extended memory
Why wrong: Lambda is for serverless functions — it doesn't provide access to GPU or custom ML accelerator hardware.
Quick Answer
The answer is EC2 Inf and Trn instances powered by AWS Inferentia and Trainium, which are purpose-built ML accelerator chips designed to optimize machine learning workloads. Inferentia is custom-built for high-performance, low-latency inference, while Trainium is engineered specifically for efficient training of deep learning models, offering superior performance per watt and lower cost compared to general-purpose GPUs. On the AWS Certified Cloud Practitioner CLF-C02 exam, this question tests your understanding of specialized compute options versus generic GPU instances—a common trap is confusing GPU-based EC2 instances like P3 or P4 with these custom ML accelerators. Remember that AWS designed these chips from the ground up for ML, not for graphics or general parallel processing. A quick memory tip: think “Inferentia for inference, Trainium for training”—the names themselves tell you which workload each chip accelerates.
CLF-C02 Cloud Technology and Services Practice Question
This CLF-C02 practice question tests your understanding of cloud technology and services. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 wants to accelerate their machine learning workloads using purpose-built ML chips instead of general-purpose GPUs. Which AWS compute option provides custom ML accelerator chips?
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
EC2 Inf and Trn instances (AWS Inferentia and Trainium)
Option B is correct because AWS Inferentia and Trainium are purpose-built ML accelerator chips designed specifically to optimize machine learning inference and training workloads, respectively. Unlike general-purpose GPUs, these custom chips provide higher performance per watt and lower cost for ML tasks, making them the ideal choice for accelerating ML workloads with dedicated hardware.
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.
- ✗
EC2 GPU instances (P and G family)
Why it's wrong here
GPU instances use NVIDIA GPUs — AWS's custom ML chips (Inferentia and Trainium) are purpose-built for ML and offer better performance/cost for those workloads.
- ✓
EC2 Inf and Trn instances (AWS Inferentia and Trainium)
Why this is correct
AWS Inferentia (Inf instances) and Trainium (Trn instances) are custom AWS-designed ML chips that provide high-throughput, cost-effective ML inference and training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
EC2 Compute-optimized instances (C family)
Why it's wrong here
C-family instances use high-performance Intel/AMD CPUs — they're designed for general compute-intensive workloads, not ML acceleration.
- ✗
AWS Lambda with extended memory
Why it's wrong here
Lambda is for serverless functions — it doesn't provide access to GPU or custom ML accelerator hardware.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume GPU instances (like P3 or G4) are the best choice for all ML workloads, overlooking that AWS offers purpose-built ML chips (Inferentia and Trainium) specifically designed to outperform GPUs in cost and efficiency for dedicated ML tasks.
Detailed technical explanation
How to think about this question
AWS Inferentia chips use a custom neural network inference accelerator architecture with high-bandwidth memory and optimized matrix multiplication engines, achieving up to 2.3x higher throughput and 70% lower cost per inference compared to GPU-based instances for models like BERT. Trainium chips are designed for distributed training with support for bfloat16 and FP32 precision, and they integrate with AWS Neuron SDK to automatically optimize model graphs for the hardware. In practice, a company running real-time recommendation systems can use Inf1 instances to reduce inference latency from milliseconds to microseconds while cutting costs by up to 45% versus GPU instances.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 CLF-C02 question test?
Cloud Technology and Services — This question tests Cloud Technology and Services — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: EC2 Inf and Trn instances (AWS Inferentia and Trainium) — Option B is correct because AWS Inferentia and Trainium are purpose-built ML accelerator chips designed specifically to optimize machine learning inference and training workloads, respectively. Unlike general-purpose GPUs, these custom chips provide higher performance per watt and lower cost for ML tasks, making them the ideal choice for accelerating ML workloads with dedicated hardware.
What should I do if I get this CLF-C02 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: Jun 11, 2026
This CLF-C02 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 CLF-C02 exam.
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