MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker built-in Image Classification algorithm on a dataset with 1000 classes. The training is very slow. They want to speed it up without sacrificing accuracy. Which instance type and training configuration is MOST appropriate?
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
✓
Use ml.p3.16xlarge instances with data parallelism
For image classification, GPU instances like ml.p3 or ml.g4dn are suitable. ml.p3.16xlarge provides 8 V100 GPUs. ml.m5 is CPU only. ml.c5 is CPU. ml.trn1 is for training, but for this built-in algorithm, GPU instances are standard.
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 ml.trn1.32xlarge instances with data parallelism
Why it's wrong here
ml.trn1 is for training using SageMaker distributed, but built-in Image Classification may not support it natively without custom container.
- ✗
Use ml.m5.24xlarge instances with data parallelism
Why it's wrong here
ml.m5 are CPU instances, slower for deep learning.
- ✗
Use ml.c5.18xlarge instances with model parallelism
Why it's wrong here
ml.c5 are CPU instances, not ideal.
- ✓
Use ml.p3.16xlarge instances with data parallelism
Why this is correct
ml.p3.16xlarge has powerful GPUs and data parallelism can speed up training.
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