MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning engineer wants to deploy a pre-trained foundation model for text summarization using SageMaker JumpStart. Which of the following is a primary cost consideration when deploying such a model?
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
✓
The cost of GPU instances required for low-latency inference
Foundation models are large and require GPU instances, which are more expensive. Inference cost is driven by instance type (GPU vs CPU) and the number of instances. While throughput and latency are performance considerations, the primary cost factor is the compute instance type. Data transfer costs are secondary. Fine-tuning costs are separate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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The cost of fine-tuning the model on custom data
Why it's wrong here
The question is about deploying the pre-trained model, not fine-tuning.
- ✓
The cost of GPU instances required for low-latency inference
Why this is correct
GPU instances are expensive and the main cost driver for large model inference.
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The cost of data transfer for inference requests
Why it's wrong here
Data transfer costs are usually small relative to compute costs.
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The cost of storing the model artifacts in S3
Why it's wrong here
Storage cost is minimal compared to compute for inference.
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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.