MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job is taking a long time, and the data scientist wants to improve performance by using a larger instance type with more vCPUs. The training job is currently using an ml.m5.large instance. The data scientist changes the instance type to ml.m5.4xlarge and resubmits the training job. However, the training time does not decrease significantly. What is the MOST likely reason?
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
✓
The built-in algorithm is not designed to scale with additional vCPUs.
The most likely reason the training time did not decrease significantly is that the built-in algorithm may not be designed to scale effectively with additional vCPUs. Option B correctly identifies this. Option A is less likely because, while single-threaded algorithms cannot use multiple vCPUs, many SageMaker built-in algorithms are parallelized but still have limited scalability due to overhead. Option C is possible but less common; the question's most likely reason is inherent scalability limitations. Option D is incorrect because if the dataset were too small, training time would already be low, and increasing vCPUs could still help, but the lack of improvement here points to scalability issues, not dataset size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The algorithm is single-threaded and cannot use multiple vCPUs.
Why it's wrong here
The algorithm is not inherently single-threaded; it depends on implementation. Many built-in algorithms are parallelized, so this is less likely.
- ✓
The built-in algorithm is not designed to scale with additional vCPUs.
Why this is correct
The built-in algorithm may not be able to utilize additional vCPUs effectively if it is not parallelized. This is the most likely reason.
- ✗
The training job is I/O bound, and increasing vCPUs does not help.
Why it's wrong here
Data size does not prevent parallelism. Even with large data, if the algorithm is parallelized, more vCPUs would help.
- ✗
The training dataset is too small to benefit from more vCPUs.
Why it's wrong here
Training on larger instances generally costs more but that does not directly affect time. Small datasets complete quickly, so this is unlikely.
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