hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data scientist is training a large transformer…
A data scientist is training a large transformer model using SageMaker's model parallelism library. The training job is failing with an out-of-memory (OOM) error. Which two actions can help resolve the OOM error? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse pipeline parallelism with tensor parallelism, assuming decreasing pipeline degree reduces memory, when in fact it increases per-GPU memory load due to fewer stages.
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
✓
Reduce the sequence length
Reducing the sequence length decreases the memory footprint of the attention mechanism, which scales quadratically with sequence length in transformer models. This directly reduces the peak memory usage per GPU, helping to avoid out-of-memory errors during training with SageMaker's model parallelism.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce the sequence length
Why this is correct
Shorter sequences directly reduce memory usage for attention and hidden states.
- ✓
Enable activation checkpointing
Why this is correct
Activation checkpointing reduces memory at the cost of recomputation.
- ✗
Increase the batch size per GPU
Why it's wrong here
Larger batch sizes increase memory consumption.
- ✗
Switch to a smaller instance type
Why it's wrong here
Smaller instances have less memory, making OOM more likely.
- ✗
Decrease the pipeline parallelism degree
Why it's wrong here
Decreasing the pipeline degree puts more layers per stage, increasing memory usage.
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