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ML Model Development →easyMultiple ChoiceObjective-mapped

MLA-C01 ML Model Development Practice Question

This MLA-C01 practice question tests your understanding of ml model development. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model is a fine-tuned BERT variant. The endpoint has been running for two weeks with acceptable latency (average 200 ms). However, over the past 24 hours, the latency has increased to an average of 800 ms, and the number of simultaneous requests has doubled. The team expects traffic to continue to grow. The current endpoint configuration uses a single ml.m5.large instance. The model is loaded into memory once, and the inference framework is PyTorch. The team needs to maintain latency under 500 ms. Which course of action should the team take to address the latency increase while minimizing cost?

Question 1easymultiple choice

Read the full NAT/PAT explanation →

A
Switch to ml.c5.large instances because CPU-optimized instances provide better inference performance for NLP models.
Why wrong: Incorrect: BERT models are often memory-intensive; c5 instances have less memory per vCPU and may not improve latency; also, this doesn't add scaling.
B
Increase the instance size to ml.m5.xlarge and keep a single instance.
Why wrong: Incorrect: A larger single instance may handle current load but does not scale for future growth and is a single point of failure.
C
Enable automatic scaling for the endpoint with a target average latency of 500 ms and use multiple ml.m5.large instances.
Correct: Auto scaling adds instances based on latency, distributing load and maintaining under 500 ms, and minimizes cost by scaling only when needed.
D
Implement a multi-model endpoint with multiple ml.m5.large instances and use Amazon Elastic Inference (EI) accelerators.
Why wrong: Incorrect: Multi-model endpoints are for serving multiple models, not for scaling a single model. EI can accelerate but not address scaling directly.

Full breakdown with real-world context →

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

✓

Enable automatic scaling for the endpoint with a target average latency of 500 ms and use multiple ml.m5.large instances.

With increased traffic, a single instance is overloaded. Auto scaling with a latency target dynamically adds instances to handle load, maintaining latency. Option A scales up but doesn't add redundancy; B switches instance family but doesn't address scaling; C suggests multi-model endpoint which is for hosting multiple models, not scaling a single model, and EI may not be cost-effective. Therefore D is correct.

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.

✗
Switch to ml.c5.large instances because CPU-optimized instances provide better inference performance for NLP models.
Why it's wrong here
Incorrect: BERT models are often memory-intensive; c5 instances have less memory per vCPU and may not improve latency; also, this doesn't add scaling.
✗
Increase the instance size to ml.m5.xlarge and keep a single instance.
Why it's wrong here
Incorrect: A larger single instance may handle current load but does not scale for future growth and is a single point of failure.
✓
Enable automatic scaling for the endpoint with a target average latency of 500 ms and use multiple ml.m5.large instances.
Why this is correct
Correct: Auto scaling adds instances based on latency, distributing load and maintaining under 500 ms, and minimizes cost by scaling only when needed.
Related concept
Static NAT maps one inside address to one outside address.
✗
Implement a multi-model endpoint with multiple ml.m5.large instances and use Amazon Elastic Inference (EI) accelerators.
Why it's wrong here
Incorrect: Multi-model endpoints are for serving multiple models, not for scaling a single model. EI can accelerate but not address scaling directly.

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 MLA-C01 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this MLA-C01 question test?

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: Enable automatic scaling for the endpoint with a target average latency of 500 ms and use multiple ml.m5.large instances. — With increased traffic, a single instance is overloaded. Auto scaling with a latency target dynamically adds instances to handle load, maintaining latency. Option A scales up but doesn't add redundancy; B switches instance family but doesn't address scaling; C suggests multi-model endpoint which is for hosting multiple models, not scaling a single model, and EI may not be cost-effective. Therefore D is correct.

What should I do if I get this MLA-C01 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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