MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 host a real-time inference endpoint for a natural language processing model. The endpoint is configured with an ml.m5.large instance. After deployment, the company observes that the inference latency is higher than expected, and the endpoint is experiencing CPU utilization near 100% during peak hours. The model is a PyTorch model that uses a transformer architecture. The company wants to reduce latency without increasing cost significantly. Which approach should the company take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the endpoint instance type to ml.g4dn.xlarge to use GPU acceleration.
The issue is high CPU utilization causing latency. Using a GPU instance (ml.g4dn.xlarge) can accelerate inference for transformer models due to parallel processing, reducing latency. Option C is correct. Option A (Elastic Inference) may help but is less effective than a full GPU for transformer models; also, it adds complexity. Option B (Auto Scaling) helps with traffic but does not reduce per-request latency. Option D (batch transform) is for offline inference, not real-time.
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.
✗
Configure the endpoint with Auto Scaling to add more instances during peak hours.
Why it's wrong here
Wrong: Auto Scaling handles traffic but does not reduce per-request CPU bottleneck.
✗
Switch to batch transform for inference.
Why it's wrong here
Wrong: Batch transform is not real-time; it does not meet low-latency requirements.
✗
Attach an Elastic Inference accelerator to the existing instance.
Why it's wrong here
Wrong: Elastic Inference may not provide enough acceleration for transformer models and adds latency overhead.
✓
Change the endpoint instance type to ml.g4dn.xlarge to use GPU acceleration.
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 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.
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 MLS-C01 NAT questions on configuration and troubleshooting.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Change the endpoint instance type to ml.g4dn.xlarge to use GPU acceleration. — The issue is high CPU utilization causing latency. Using a GPU instance (ml.g4dn.xlarge) can accelerate inference for transformer models due to parallel processing, reducing latency. Option C is correct. Option A (Elastic Inference) may help but is less effective than a full GPU for transformer models; also, it adds complexity. Option B (Auto Scaling) helps with traffic but does not reduce per-request latency. Option D (batch transform) is for offline inference, not real-time.
What should I do if I get this MLS-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 MLS-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.
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Question Discussion
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