MLA-C01 Question 294
Single answerYou are building a machine learning model training pipeline on AWS using Amazon SageMaker. Your team expects workloads to vary significantly between small experiments and large-scale training jobs, depending on project needs. You need to choose the most cost-effective resource allocation strategy. Which approach should you take?
- A
Use on-demand instances for all workloads to avoid over-provisioning and ensure flexibility.
- B
Use provisioned resources to handle all workloads, allowing you to reserve capacity upfront and reduce costs.
- C
Use on-demand instances for smaller workloads and Auto Scaling to handle large-scale training jobs.
- D
Use provisioned resources for smaller workloads and on-demand resources for large-scale training jobs.
Show answer and explanation
Correct answer: C
Explanation
The best approach for a machine learning pipeline with variable workloads is to use on-demand instances for smaller, low-resource tasks and Auto Scaling for large-scale training jobs. This strategy balances cost-efficiency by avoiding over-provisioning while ensuring that large workloads have the necessary resources when needed.
- A. Incorrect.
Using on-demand instances for all workloads provides flexibility but may lead to higher costs, especially for large-scale jobs, as you are not leveraging scaling or reserved capacity.
- B. Incorrect.
Using provisioned resources for all workloads can reduce costs for predictable workloads, but it is not ideal for workloads with unpredictable or varying demand, leading to potential underutilization.
- C. Correct.
Using on-demand instances for smaller workloads and Auto Scaling for large-scale workloads ensures cost-efficiency and flexibility. On-demand instances avoid over-commitment for smaller jobs, while Auto Scaling dynamically adjusts resources for larger jobs, optimizing costs.
- D. Incorrect.
Using provisioned resources for smaller workloads is inefficient, as smaller workloads do not require reserved capacity. Similarly, using on-demand resources for large-scale jobs can be more expensive than leveraging Auto Scaling.