- A
Change the instance type to a GPU instance such as ml.g4dn.xlarge.
GPU instances accelerate model inference, reducing per-request latency.
- B
Use a multi-model endpoint to serve multiple models on the same instance.
Why wrong: Multi-model endpoints improve resource utilization but not per-model latency.
- C
Enable automatic scaling based on inference latency.
Why wrong: Scaling adjusts capacity but does not reduce per-request latency.
- D
Increase the number of instances and use a target tracking scaling policy.
Why wrong: Adding instances spreads load but does not reduce per-request latency for CPU-bound models.
Question 537 of 1,755
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 company deploys a real-time inference endpoint using Amazon SageMaker with an ML model that has strict latency requirements. The endpoint currently uses a single ml.c5.xlarge instance. During a load test, the p99 latency exceeds the 100ms threshold. The team adds more instances but latency does not improve because the model is heavily CPU-bound. What is the MOST cost-effective change to meet the latency requirement?
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
✓
Change the instance type to a GPU instance such as ml.g4dn.xlarge.
The model is CPU-bound, meaning the bottleneck is compute capacity, not throughput. GPU instances like ml.g4dn.xlarge offload parallel computation from the CPU, significantly reducing per-inference latency. This directly addresses the root cause without over-provisioning, making it the most cost-effective solution.
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.
- ✓
Change the instance type to a GPU instance such as ml.g4dn.xlarge.
Why this is correct
GPU instances accelerate model inference, reducing per-request latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a multi-model endpoint to serve multiple models on the same instance.
Why it's wrong here
Multi-model endpoints improve resource utilization but not per-model latency.
- ✗
Enable automatic scaling based on inference latency.
Why it's wrong here
Scaling adjusts capacity but does not reduce per-request latency.
- ✗
Increase the number of instances and use a target tracking scaling policy.
Why it's wrong here
Adding instances spreads load but does not reduce per-request latency for CPU-bound models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume adding more instances (horizontal scaling) always reduces latency, but for CPU-bound models, the bottleneck is per-instance compute, not request queuing, so vertical scaling with GPU instances is required.
Detailed technical explanation
How to think about this question
GPU instances leverage CUDA cores for massive parallelism, ideal for matrix operations in deep learning models. Even for non-deep learning models, if the inference involves heavy numerical computation (e.g., large feature engineering), GPU acceleration can reduce latency. Under the hood, SageMaker uses NVIDIA TensorRT or custom containers to offload compute, and the ml.g4dn.xlarge offers 16 GB GPU memory and up to 8 vCPUs, balancing cost and performance.
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.
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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: Change the instance type to a GPU instance such as ml.g4dn.xlarge. — The model is CPU-bound, meaning the bottleneck is compute capacity, not throughput. GPU instances like ml.g4dn.xlarge offload parallel computation from the CPU, significantly reducing per-inference latency. This directly addresses the root cause without over-provisioning, making it the most cost-effective solution.
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.
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Last reviewed: Jul 4, 2026
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