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MLA-C01 Practice Question: A company uses Amazon SageMaker to train a custom…
A company uses Amazon SageMaker to train a custom XGBoost model. The training job runs on a single ml.m5.large instance and takes 2 hours. To reduce training time without changing the algorithm, what should the data scientist do?
⚠ Common exam trap
The MLA-C01 exam often tests the misconception that changing the algorithm variant (e.g., from custom to built-in) or adding tuning will speed up a single training job, when in reality only scaling compute resources (larger instance or distributed training) directly reduces wall-clock 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 a larger instance type
Increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or ml.p3.2xlarge) provides more CPU/GPU cores, memory, and network bandwidth, directly reducing training time for the same XGBoost algorithm. Since the training job is compute-bound on a single instance, scaling vertically is the most straightforward way to accelerate training without modifying the algorithm or hyperparameters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of epochs
Why it's wrong here
Increasing epochs would increase training time, not reduce it.
- ✗
Use SageMaker's built-in XGBoost algorithm
Why it's wrong here
This changes the algorithm (from custom to built-in), which is not allowed by the condition 'without changing the algorithm'.
- ✗
Enable automatic model tuning
Why it's wrong here
Automatic model tuning runs multiple training jobs to find hyperparameters, increasing total time.
- ✓
Use a larger instance type
Why this is correct
A larger instance offers more compute resources, reducing training time for the same algorithm.
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