hardMultiple Choice
MLA-C01 Practice Question: Refer to the exhibit
Exhibit
[2024-01-15 10:30:45] Training job 'my-training-job' started.
[2024-01-15 10:31:10] Using algorithm 'built-in' with hyperparameters: {'epochs': 10, 'batch-size': 32, 'learning-rate': 0.001}
[2024-01-15 10:31:15] File system creation failed: No usable scratch space. Error: Input/output error.
[2024-01-15 10:31:15] Retrying with local SSD...
[2024-01-15 10:31:20] Training completed with status 'Failed'.Refer to the exhibit. The training job failed. What is the MOST likely cause?
⚠ Common exam trap
Candidates often confuse disk space errors with memory errors (Option C) or incorrectly attribute the failure to hyperparameters (Option A or E), when the specific 'No space left on device' error directly points to insufficient EBS volume size.
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 training data size exceeds the available EBS volume size
The error message in the exhibit indicates an 'OSError: [Errno 28] No space left on device' during the training job. This occurs when the training data size exceeds the available EBS volume size attached to the SageMaker training instance. SageMaker uses EBS volumes for storing training data and intermediate outputs; if the dataset is larger than the provisioned EBS storage, the job fails with this specific disk-full error.
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 learning rate is too high
Why it's wrong here
A high learning rate causes divergence or NaN loss, which the exhibit would show as oscillating or exploding loss values, not the failure signature displayed. It is tempting because tuning the learning rate is a standard remedy for unstable training, and it would be correct if the loss curve showed divergence rather than the actual symptom.
- ✗
The instance type does not have SSD storage
Why it's wrong here
Instance storage type affects I/O throughput, not whether the job can start; the exhibit's failure would not reference SSD absence. It is tempting because storage performance matters for data-heavy training, and it would be correct if the logs showed disk read/write bottlenecks or slow data loading rather than the actual error.
- ✗
The instance type does not have enough memory
Why it's wrong here
Insufficient memory manifests as an out-of-memory error or killed process, which the exhibit does not show. It is tempting because memory exhaustion is a frequent cause of training failures, and it would be the correct choice if the logs contained an OOM kill or memory allocation failure message.
- ✓
The training data size exceeds the available EBS volume size
Why this is correct
SageMaker training jobs download data to an attached EBS volume; if the dataset exceeds that volume's capacity, the job fails with a disk-space error. The exhibit's failure therefore points to training data size exceeding available EBS volume size.
- ✗
The number of epochs is too low
Why it's wrong here
Too few epochs produces an underfit model with poor accuracy, not a job failure; training completes normally. It is tempting because epoch count is a common hyperparameter to adjust, and it would be the correct choice if the exhibit showed training and validation loss still decreasing when training stopped.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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