MLA-C01 ML Model Development Practice Question
A data scientist is training an XGBoost model on a large dataset using a SageMaker Training Job. They want to minimize costs without sacrificing model performance. Which instance type and training strategy should they choose?
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 a single ml.g4dn.xlarge Spot instance with no distributed training
Using Spot instances with Managed Spot Training can reduce costs by up to 90% compared to On-Demand, and SageMaker automatically handles interruptions. For single-instance training, a single ml.g4dn.xlarge provides sufficient compute for moderate-sized datasets.
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 single ml.g4dn.xlarge Spot instance with no distributed training
Why this is correct
Spot instances drastically reduce cost; single instance avoids parallelism overhead for XGBoost.
- ✗
Use a single ml.m5.large On-Demand instance with model parallelism
Why it's wrong here
ml.m5.large is CPU-only and underpowered for XGBoost; model parallelism is for large models, not needed here.
- ✗
Use multiple ml.trn1.2xlarge On-Demand instances with data parallelism
Why it's wrong here
Trainium instances are designed for deep learning, not tree-based models; On-Demand is expensive.
- ✗
Use a single ml.p3.2xlarge On-Demand instance with data parallelism
Why it's wrong here
On-Demand is more expensive and ml.p3.2xlarge may be overkill for XGBoost; data parallelism requires multiple instances.
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