AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 9

Case Study -
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?

Answer options

• A. Use Managed Spot Training.
• B. Use SageMaker managed warm pools.
• C. Use SageMaker Training Compiler.
• D. Use the SageMaker distributed data parallelism (SMDDP) library.

Correct answer: B

Explanation

Using SageMaker managed warm pools allows for pre-initialized compute instances, significantly cutting down startup times for training jobs. Other options, like Managed Spot Training, focus on cost savings rather than minimizing startup times, while the SageMaker Training Compiler and SMDDP library are geared towards optimizing training performance rather than infrastructure initialization.