MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company plans to deploy a large foundation model using SageMaker JumpStart. They are concerned about costs because the model will be used intermittently. Which deployment option is MOST cost-effective for intermittent traffic?
⚠ Common exam trap
Watch out — candidates often confuse 'multi-model endpoints' with 'serverless' and assume they both scale to zero, but multi-model endpoints still run on provisioned instances that incur hourly costs regardless of traffic.
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
✓
Deploy as a serverless endpoint
Serverless endpoints in SageMaker automatically scale to zero when not in use, so you pay only for the compute time consumed during inference requests. This makes them the most cost-effective option for intermittent traffic, as you avoid paying for idle compute capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Purchase SageMaker Savings Plans for the endpoint
Why it's wrong here
Savings Plans discount steady, predictable compute usage over a one- or three-year commitment; they still charge for provisioned endpoint capacity during idle periods. Intermittent traffic needs scale-to-zero serverless inference, which bills only per invocation, so a commitment wastes money.
- ✓
Deploy as a serverless endpoint
Why this is correct
Serverless endpoints scale to zero when idle, so you pay only for inference requests rather than continuous instance hours. This directly satisfies the intermittent-traffic constraint, where a real-time endpoint would bill for provisioned capacity around the clock. Cold-start latency is the trade-off, but cost efficiency dominates for sporadic workloads.
- ✗
Use a batch transform job for each request
Why it's wrong here
Batch transform suits one-off bulk scoring of stored datasets, not interactive requests; it spins up instances per job and cannot serve low-latency inference. It is tempting because it avoids idle endpoint cost, but intermittent live traffic needs scale-to-zero serverless inference, which batch transform cannot provide.
- ✗
Deploy as a real-time endpoint with a multi-model endpoint
Why it's wrong here
Multi-model endpoints host many models behind one endpoint to cut hosting costs, but they still run continuously and bill per instance-hour regardless of traffic. Intermittent foundation-model inference needs scale-to-zero serverless inference, which multi-model endpoints cannot do.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 665 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.