- A
Pre-load multiple model containers on the same endpoint
Why wrong: Pre-loading is not configurable; containers are loaded on demand.
- B
Reduce the batch size for inference requests
Why wrong: Reducing batch size increases frequency but not per-request latency.
- C
Use a larger instance type with more memory and compute
Larger instances can process large payloads faster.
- D
Enable payload compression using SageMaker built-in compression
Why wrong: SageMaker does not support payload compression.
Question 302 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. 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 deploys a machine learning model on Amazon SageMaker for real-time inference. The model receives requests with large payloads (up to 5 MB) and the inference latency is high. Which configuration change would MOST likely reduce latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 a larger instance type with more memory and compute
Option C is correct because increasing the instance type to one with more memory and compute directly addresses the bottleneck caused by large payloads (up to 5 MB) and high inference latency. SageMaker real-time endpoints process requests synchronously, so a larger instance provides more CPU/GPU and memory bandwidth to serialize/deserialize and process the payload faster, reducing overall latency.
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.
- ✗
Pre-load multiple model containers on the same endpoint
Why it's wrong here
Pre-loading is not configurable; containers are loaded on demand.
- ✗
Reduce the batch size for inference requests
Why it's wrong here
Reducing batch size increases frequency but not per-request latency.
- ✓
Use a larger instance type with more memory and compute
Why this is correct
Larger instances can process large payloads faster.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable payload compression using SageMaker built-in compression
Why it's wrong here
SageMaker does not support payload compression.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse batch size (relevant for batch transform jobs) with real-time inference request size, or assume that multi-model endpoints improve single-request latency, when in fact they add overhead.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker real-time endpoints use a synchronous HTTP/HTTPS invocation model where the entire payload must be received, deserialized, and processed before a response is returned. For large payloads, network I/O and memory allocation become significant factors; a larger instance type (e.g., moving from ml.m5.large to ml.m5.4xlarge) increases available memory bandwidth and CPU cores, reducing time spent on data copying and serialization. In practice, if the model itself is compute-bound, GPU instances (e.g., ml.g4dn or ml.p3) can further reduce latency by parallelizing tensor operations.
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 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: Use a larger instance type with more memory and compute — Option C is correct because increasing the instance type to one with more memory and compute directly addresses the bottleneck caused by large payloads (up to 5 MB) and high inference latency. SageMaker real-time endpoints process requests synchronously, so a larger instance provides more CPU/GPU and memory bandwidth to serialize/deserialize and process the payload faster, reducing overall latency.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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