MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a linear regression model on a dataset with 10 million rows and 50 features. The training job takes 8 hours to complete. A data scientist wants to reduce the training time to under 2 hours without changing the dataset size or the model algorithm. The SageMaker instance type currently used is ml.m5.2xlarge. Which action should the data scientist take to achieve the desired training time?
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 SageMaker's distributed training with 4 ml.m5.2xlarge instances.
Using SageMaker's distributed training with multiple instances parallelizes the computation across 4 ml.m5.2xlarge instances, reducing wall-clock time by approximately a factor of 4, which can bring the 8-hour job down to around 2 hours. Option A (GPU instance) is not optimal for linear regression, which is CPU-bound. Option B (doubling vCPUs) provides only a 2x improvement, insufficient to reach under 2 hours. Option C (reducing features) changes the dataset and is not allowed per the requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the instance type to ml.p3.2xlarge (GPU instance).
Why it's wrong here
Linear regression does not benefit from GPU acceleration; GPUs are for deep learning.
- ✗
Change the instance type to ml.m5.4xlarge (double the vCPUs and memory).
Why it's wrong here
Doubling instance size yields at most 2x speedup, insufficient to go from 8 to 2 hours.
- ✗
Reduce the number of features from 50 to 25.
Why it's wrong here
Reducing features alters the dataset and is not allowed per the scenario.
- ✓
Use SageMaker's distributed training with 4 ml.m5.2xlarge instances.
Why this is correct
Distributed training parallelizes computation across instances, significantly reducing training time.
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