The correct answer is that the number of training steps or epochs specified exceeds the dataset size. This error occurs because TensorFlow’s dataset iterator raises an OutOfRangeError when it reaches the end of a finite sequence—in this case, the TFRecord files in S3—and the training loop tries to pull more batches than the dataset contains. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how SageMaker integrates with TensorFlow’s data pipeline, particularly the need to align epoch and step counts with the actual record count. A common trap is assuming the error is due to corrupted data or S3 access issues, but the root cause is almost always a mismatch between the configured training iterations and the dataset size. Remember the mnemonic: “Steps must fit the set”—if your steps or epochs outrun your records, you’ll hit the end of the sequence.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, TensorFlow's tf.data API uses iterators that raise 'OutOfRangeError' when the dataset is fully consumed. In SageMaker training, the number of steps per epoch is often calculated as total_samples / batch_size; if this calculation is off or epochs are set too high, the iterator exhausts the data before the training loop completes. A real-world scenario is when a user specifies a fixed number of steps in the TensorFlow Estimator's 'train' method without accounting for the actual dataset size, leading to this error.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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