The answer is SageMaker Serverless Inference. This is the most cost-effective deployment option for a model with low baseline traffic that must handle sudden spikes because it automatically scales to zero during idle periods and scales up instantly to accommodate bursts, charging only for the compute time consumed per inference request. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how serverless architecture eliminates the cost of idle provisioned infrastructure, making it ideal for unpredictable or intermittent traffic patterns—a common trap is choosing a real-time endpoint with auto-scaling, which still incurs costs for a minimum number of instances even when traffic is low. Remember the memory tip: “Spikes to zero, pay per go” to recall that Serverless Inference both scales down to zero and bills only for actual usage.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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 has a model that receives low traffic but needs to handle sudden spikes. Which deployment option is most cost-effective?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Serverless Inference
SageMaker Serverless Inference is the most cost-effective option for low-traffic models with sudden spikes because it automatically scales to zero when not in use and scales up instantly to handle bursts, charging only for the compute time consumed per inference request. This eliminates the cost of idle provisioned infrastructure, making it ideal for unpredictable or intermittent traffic patterns.
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.
✓
SageMaker Serverless Inference
Why this is correct
Serverless scales to zero during idle periods and handles spikes, minimizing cost.
Related concept
Read the scenario before looking for a memorised answer.
✗
SageMaker Real-Time Endpoint with Auto Scaling
Why it's wrong here
Auto scaling still requires at least one instance running, incurring baseline cost.
✗
SageMaker Multi-Model Endpoint
Why it's wrong here
Multi-model endpoints still run on persistent instances; not serverless.
✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for batch processing, not real-time inference.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that auto-scaling (Option B) is the most cost-effective for spikes, but the trap is that auto-scaling still requires a baseline of provisioned instances that incur cost even when idle, whereas serverless inference scales to zero and charges only for active compute time.
Detailed technical explanation
How to think about this question
SageMaker Serverless Inference uses AWS Lambda-like infrastructure under the hood, where the model is loaded into a container on demand and the endpoint scales from zero concurrency based on incoming request volume, with a maximum concurrency limit per endpoint (default 50). A subtle behavior is that cold starts can occur after a period of inactivity, adding latency to the first request, so it is best suited for workloads where occasional latency spikes are acceptable. In a real-world scenario, a chatbot that receives sporadic user queries would benefit from serverless inference, as it avoids the cost of a dedicated endpoint while still handling bursty traffic.
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.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: SageMaker Serverless Inference — SageMaker Serverless Inference is the most cost-effective option for low-traffic models with sudden spikes because it automatically scales to zero when not in use and scales up instantly to handle bursts, charging only for the compute time consumed per inference request. This eliminates the cost of idle provisioned infrastructure, making it ideal for unpredictable or intermittent traffic patterns.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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