MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data science team is using Amazon SageMaker to train a model. The training job is failing with an 'OutOfMemory' error. The team is using a p3.2xlarge instance with 61 GB of memory. They need to resolve this issue as quickly as possible. Which action should they take?
⚠ Common exam trap
The trap here is that candidates may overthink optimization strategies (like reducing batch size or enabling distributed training) when the simplest and fastest solution is to increase instance memory, as the question explicitly asks for the quickest resolution.
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 larger instance type, such as p3.8xlarge
The 'OutOfMemory' error indicates the training job requires more memory than the 61 GB available on the p3.2xlarge instance. The fastest resolution is to scale vertically by using a larger instance type, such as the p3.8xlarge, which provides 244 GB of memory. This directly addresses the memory shortage without requiring code changes or architectural modifications, minimizing downtime.
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 larger instance type, such as p3.8xlarge
Why this is correct
Larger instance types have more memory and can handle the workload.
- ✗
Reduce the batch size in the training script
Why it's wrong here
Reducing batch size may help but requires code changes and might not be enough.
- ✗
Use a spot instance to save costs
Why it's wrong here
Spot instances do not provide more memory.
- ✗
Enable distributed training across multiple instances
Why it's wrong here
Distributed training splits the model but does not increase memory per instance.
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