MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company wants to deploy a foundation model from SageMaker JumpStart with the lowest possible inference cost, given that latency requirements are flexible. They have a mix of traffic volumes. Which approach should they take?
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
✓
Select the smallest instance type that meets throughput requirements and enable automatic scaling
SageMaker JumpStart provides pre-built models; for cost optimization, choosing the smallest suitable instance type and enabling auto-scaling based on demand reduces cost while handling varying traffic.
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 Savings Plans to get a discount on on-demand instances
Why it's wrong here
Savings Plans discount compute duration commitments, not the idle capacity that serverless or scale-to-zero endpoints eliminate; with variable traffic and flexible latency, paying for provisioned instance-hours regardless of utilisation leaves the dominant cost driver untouched.
- ✗
Deploy the model on the largest GPU instance to handle peak load
Why it's wrong here
A single largest GPU instance provisions for peak load continuously, so idle periods still incur full instance-hour charges; with flexible latency, autoscaling across smaller instances or scale-to-zero endpoints matches capacity to actual traffic at lower cost.
- ✗
Deploy the model on a serverless inference endpoint
Why it's wrong here
Serverless inference suits sporadic, unpredictable traffic with cold-start tolerance, but it bills per invocation at a premium rate and caps instance sizes, so sustained or bursty volumes cost more than a scale-to-zero endpoint on accelerated compute.
- ✓
Select the smallest instance type that meets throughput requirements and enable automatic scaling
Why this is correct
Flexible latency permits the smallest viable instance, and automatic scaling matches capacity to fluctuating traffic volumes. Together these minimise inference cost while meeting throughput, since you pay only for the instances actually needed at each moment.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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