hardMultiple Choice
AIF-C01 Practice Question: A team is using Amazon SageMaker to train a deep…
A team is using Amazon SageMaker to train a deep learning model. The training job is taking too long. Which action is MOST likely to reduce training time while maintaining model quality?
⚠ Common exam trap
A common misconception is that using Spot Instances reduces training time, but it actually only reduces cost and can increase time due to interruptions. Distributed training with SageMaker's data parallelism is the correct approach for reducing wall-clock 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
✓
Increase the number of training instances for distributed training
Increasing the number of training instances for distributed training (Option D) directly reduces training time by parallelizing the workload across multiple machines, which is the most effective approach for deep learning models that are computationally intensive. SageMaker's distributed training libraries (e.g., SageMaker Distributed Data Parallel) split the data and model across instances, enabling linear or near-linear speedups while preserving model quality through synchronized gradient updates.
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 incremental training
Why it's wrong here
Incremental training reuses an existing model artefact to train on new data, cutting the data volume per run; it does not accelerate a single long training job on a fixed dataset. Distributed training or larger GPU instances reduce that job's duration. Incremental training suits periodic model refresh scenarios.
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Enable managed spot training
Why it's wrong here
Managed spot training lowers cost by using spare capacity, but jobs can be interrupted and resumed, and it does not by itself shorten training. Distributed training or GPU-accelerated instances reduce wall-clock time. Spot training is correct when the priority is minimising compute spend on interruption-tolerant jobs.
- ✗
Switch to a smaller instance type
Why it's wrong here
A smaller instance type reduces available vCPU, memory and GPU capacity, so each epoch runs slower and training time increases rather than falls. Larger accelerated instances or distributed training cut wall-clock time. Smaller instances suit cost-sensitive inference or light experimentation, not long deep-learning training jobs.
- ✓
Increase the number of training instances for distributed training
Why this is correct
Distributed training spreads gradient computation across multiple instances, so each epoch processes more data in parallel and wall-clock training time falls. Adding instances scales throughput while preserving model quality, since the same algorithm and hyperparameters are used across the cluster.
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