Match each SageMaker optimization technique to its description.
Drag a concept onto its matching description — or click a concept then click the description.
Train across multiple GPUs or instances
Hyperparameter optimization with Bayesian search
Use spot instances for cost savings
Stream data directly from S3 for faster training
Monitor training and detect issues
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Managed Spot Training: Uses EC2 Spot instances to reduce training cost
Managed Spot Training reduces cost via spot instances; Data Parallelism speeds training by distributing data; Automatic Model Tuning finds optimal hyperparameters; Compilation (Neo) optimizes for inference hardware. Common confusions include swapping definitions between these techniques.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Managed Spot Training: Uses EC2 Spot instances to reduce training cost
Why this is correct
Managed Spot Training leverages spare EC2 capacity to lower costs, with checkpointing for resilience.
SageMaker Data Parallelism: Distributes data across multiple GPUs/instances to speed up training
Why this is correct
Data parallelism splits the dataset across devices, enabling faster training of large models.
SageMaker Automatic Model Tuning: Automatically searches hyperparameter space to optimize model
Why this is correct
Automatic model tuning (hyperparameter optimization) uses algorithms like Bayesian search to find the best hyperparameters.
SageMaker Compilation: Optimizes trained model for target hardware using SageMaker Neo
Why this is correct
SageMaker Neo compiles models to run efficiently on specific hardware, improving inference performance.
Managed Spot Training: Optimizes model for inference on edge devices
Why it's wrong here
Incorrect — this describes SageMaker Compilation (Neo), not Managed Spot Training.
SageMaker Data Parallelism: Automatically searches hyperparameter space to optimize model
Why it's wrong here
Incorrect — this describes SageMaker Automatic Model Tuning, not data parallelism.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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