- A
Configure SageMaker batch transform for the real-time endpoint to process requests asynchronously.
Why wrong: Batch transform is for offline inference, not real-time; it would not meet the sub-500 ms latency requirement.
- B
Increase the auto-scaling maximum instance count to 10 and set target CPU utilization to 50%.
Why wrong: This increases cost and may not reduce latency if the bottleneck is compute time per request, not throughput.
- C
Switch the endpoint instance type to a GPU instance such as ml.g4dn.xlarge to accelerate inference.
GPU instances can accelerate inference for gradient boosting models by parallelizing computations, reducing per-request latency significantly.
- D
Enable data compression on the endpoint to reduce payload size and network latency.
Why wrong: Data compression reduces network latency but does not address the compute time for inference, which is the primary cause of latency spikes.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 data science team at a financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting classifier trained on historical transaction data. The model is deployed to a SageMaker endpoint with an ML.M5.LARGE instance for real-time inference. After deployment, the team observes that the endpoint's latency spikes to over 2 seconds during peak hours (10:00-12:00 and 14:00-16:00), causing timeouts for client applications. The average latency during off-peak hours is 200 ms. The team has enabled auto-scaling with a target average CPU utilization of 70%, but the endpoint still experiences high latency during peak hours. The instance count never scales beyond 2 instances during peaks. The model size is 500 MB, and each request includes 200 features. The team needs to reduce latency to under 500 ms at the 99th percentile during peak hours without increasing costs beyond the current budget. Which course of action should the team take?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"never"Why it matters: Absolute qualifier. True only if the statement has zero exceptions — be cautious of options that seem obvious but break down in edge cases.
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
Switch the endpoint instance type to a GPU instance such as ml.g4dn.xlarge to accelerate inference.
Option C is correct because GPU instances like ml.g4dn.xlarge are optimized for compute-intensive workloads such as gradient boosting inference, which involves numerous matrix operations. By offloading the computation to the GPU, the model can process each request faster, reducing latency from over 2 seconds to under 500 ms at the 99th percentile without increasing the instance count or budget. This directly addresses the root cause—CPU-bound inference during peak hours—while keeping costs stable.
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.
- ✗
Configure SageMaker batch transform for the real-time endpoint to process requests asynchronously.
Why it's wrong here
Batch transform is for offline inference, not real-time; it would not meet the sub-500 ms latency requirement.
- ✗
Increase the auto-scaling maximum instance count to 10 and set target CPU utilization to 50%.
Why it's wrong here
This increases cost and may not reduce latency if the bottleneck is compute time per request, not throughput.
- ✓
Switch the endpoint instance type to a GPU instance such as ml.g4dn.xlarge to accelerate inference.
Why this is correct
GPU instances can accelerate inference for gradient boosting models by parallelizing computations, reducing per-request latency significantly.
Clue confirmation
The clue word "never" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable data compression on the endpoint to reduce payload size and network latency.
Why it's wrong here
Data compression reduces network latency but does not address the compute time for inference, which is the primary cause of latency spikes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume auto-scaling or instance count adjustments will solve latency issues, but the real bottleneck is per-instance compute capacity, which GPU acceleration directly addresses without increasing costs.
Detailed technical explanation
How to think about this question
Gradient boosting classifiers, especially those implemented in frameworks like XGBoost or LightGBM, rely heavily on sequential tree traversal and floating-point operations, which are CPU-intensive. GPU instances leverage thousands of CUDA cores to parallelize these operations, significantly reducing inference latency for models of this size. In practice, switching to a GPU instance can yield 5-10x speedup for tree-based models, making it a cost-effective solution when CPU utilization is already saturated.
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 MLA-C01 question test?
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Switch the endpoint instance type to a GPU instance such as ml.g4dn.xlarge to accelerate inference. — Option C is correct because GPU instances like ml.g4dn.xlarge are optimized for compute-intensive workloads such as gradient boosting inference, which involves numerous matrix operations. By offloading the computation to the GPU, the model can process each request faster, reducing latency from over 2 seconds to under 500 ms at the 99th percentile without increasing the instance count or budget. This directly addresses the root cause—CPU-bound inference during peak hours—while keeping costs stable.
What should I do if I get this MLA-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: "never". Absolute qualifier. True only if the statement has zero exceptions — be cautious of options that seem obvious but break down in edge cases.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 24, 2026
This MLA-C01 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 MLA-C01 exam.
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