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?
⚠ Common exam trap
MLA-C01 often tests the misconception that data transfer or storage costs dominate ML deployment, when in fact compute instances, especially GPUs, are the primary cost driver for inference.
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
When deploying a pre-trained foundation model via SageMaker JumpStart, the model is already trained, so fine-tuning cost is optional and not primary. The main ongoing cost is the compute instance used for inference, especially GPU instances needed for low-latency, high-throughput text summarization. Data transfer for inference requests is typically negligible compared to compute, and S3 storage for model artifacts is a minor one-time cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The cost of fine-tuning the model on custom data
Why it's wrong here
Fine-tuning is optional and separate from deployment; the question asks about deploying the pre-trained model, where endpoint instance hours dominate. It would be correct if the engineer chose to adapt the model on custom data, but that is a distinct training workload, not the deployment cost.
- ✓
The cost of GPU instances required for low-latency inference
Why this is correct
JumpStart foundation models are large and require accelerated compute, so GPU instance hours dominate deployment cost. Low-latency inference demands continuously running GPU capacity rather than serverless or CPU options, making the instance type and count the primary cost consideration for this deployment.
- ✗
The cost of data transfer for inference requests
Why it's wrong here
Inference request data transfer is normally negligible beside compute charges; the dominant cost is the continuously running endpoint instance hosting the model. It would be correct for high-volume cross-region or egress-heavy architectures, but not as the primary consideration for a standard JumpStart deployment.
- ✗
The cost of storing the model artifacts in S3
Why it's wrong here
S3 storage for model artifacts is a minor, one-off cost compared with the hourly compute of a persistent inference endpoint. It would be correct when retaining many large model versions long-term, but the primary driver for a deployed JumpStart model is endpoint instance hours.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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