MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a custom TensorFlow model. The training job is failing with the error: 'OutOfRangeError: End of sequence'. The input data is stored in TFRecord format in S3. What is the most likely cause?
⚠ Common exam trap
Candidates often confuse 'OutOfRangeError' with data corruption or memory issues, but the error specifically indicates the dataset has been fully iterated, not that the data is damaged or resources are insufficient.
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 number of training steps or epochs specified exceeds the dataset size.
The 'OutOfRangeError: End of sequence' error in TensorFlow occurs when the training loop attempts to read more data than is available in the dataset. This typically happens when the number of training steps or epochs specified exceeds the total number of records in the TFRecord files, causing the iterator to reach the end of the dataset prematurely.
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 TFRecord files are corrupted.
Why it's wrong here
Corruption would cause parse errors, not 'End of sequence'.
- ✓
The number of training steps or epochs specified exceeds the dataset size.
Why this is correct
The training loop continues beyond available data, causing the error.
- ✗
The instance type does not have enough memory.
Why it's wrong here
Memory would cause OOM, not this error.
- ✗
The shuffle buffer size is too large.
Why it's wrong here
Large buffer may cause memory issues but not this error.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.