MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model for object detection using Amazon SageMaker. The training job is using a single GPU instance and is taking too long. Which THREE actions can reduce training time? (Choose THREE.)
⚠ Common exam trap
Test-takers frequently confuse reducing batch size with speeding up training, but in practice, smaller batches increase the number of gradient updates and can lead to longer wall-clock time, especially on GPU instances where larger batches better utilize parallel hardware.
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
✓
Enable mixed precision training with FP16
Enabling mixed precision training with FP16 reduces memory usage and accelerates computation by using half-precision floating-point numbers where possible, which is particularly effective on NVIDIA GPUs with Tensor Cores (e.g., V100, A100). This can nearly double throughput for deep learning models without sacrificing model accuracy, as critical operations still use FP32 precision.
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 a CPU instance instead of GPU
Why it's wrong here
CPU instances are slower for training deep learning models compared to GPUs.
- ✓
Enable mixed precision training with FP16
Why this is correct
Mixed precision uses half-precision floats, speeding up computation and reducing memory usage.
- ✓
Use a GPU instance with more GPUs, such as p3.16xlarge
Why this is correct
More GPUs allow more parallelism, reducing training time.
- ✗
Reduce the batch size
Why it's wrong here
Smaller batch size reduces memory per step but increases the number of steps, often leading to longer training time.
- ✓
Use distributed training across multiple instances
Why this is correct
Distributed training parallelizes the workload, reducing overall training time.
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