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MLA-C01 Practice Question: A team has a large number of models that need to…
A team has a large number of models that need to be deployed for batch inference weekly. They want to minimize cost and management overhead. Which approach is MOST efficient?
⚠ Common exam trap
AWS often tests the distinction between batch and real-time inference, where candidates mistakenly choose persistent endpoints (Option C or D) for batch workloads, overlooking that Batch Transform is purpose-built for cost-efficient, ephemeral batch processing.
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 SageMaker Batch Transform with separate jobs for each model.
SageMaker Batch Transform is the most efficient approach for weekly batch inference because it automatically provisions and terminates compute resources for each job, minimizing cost and management overhead. Running separate jobs for each model allows independent scaling and avoids the complexity of managing persistent endpoints or multi-model hosting for batch workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Pipelines to run inference as part of the pipeline.
Why it's wrong here
SageMaker Pipelines orchestrates training workflows and model lineage; running inference inside it still requires provisioning compute per model and does not provide the managed batch-transform scheduling the scenario needs. Pipelines would be correct for automating repeatable training, tuning and deployment steps rather than weekly batch inference across many models.
- ✓
Use SageMaker Batch Transform with separate jobs for each model.
Why this is correct
SageMaker Batch Transform provisions compute only for the duration of each job, then tears it down, so weekly batch inference incurs no idle endpoint cost. Running separate jobs per model isolates each workload, satisfying the stem's cost-minimisation and low-management-overhead constraints without persistent infrastructure.
- ✗
Create a single SageMaker endpoint for all models and update the model periodically.
Why it's wrong here
One shared endpoint cannot host many distinct models simultaneously; swapping the model weekly serialises inference and forces redeployment, adding management overhead and risking downtime. A single endpoint suits hosting one model (or a few via multi-model endpoints) for real-time traffic, not weekly batch scoring of many models.
- ✗
Deploy each model to a separate SageMaker endpoint and delete after use.
Why it's wrong here
Per-model endpoints incur provisioning and teardown overhead for every model each week, and endpoint instances bill while active, contradicting the cost and management-overhead requirement. Endpoints suit real-time, low-latency inference against a small number of models, not large-scale recurring weekly batch scoring.
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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.