Quick Answer
The answer is to change the input mode from File to Pipe. This resolves the OutOfMemory error because File mode downloads the entire dataset from S3 to the training instance’s local storage before training starts, consuming all available memory for large datasets. Pipe mode, by contrast, streams data directly from S3 to the training algorithm in a continuous flow, drastically reducing the memory footprint and allowing you to keep your current instance type, which minimizes cost. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of SageMaker’s data ingestion modes and how they impact instance resource utilization. A common trap is assuming you need a larger, more expensive instance, but the correct, cost-effective fix is switching to Pipe mode. Memory tip: think “File fills, Pipe pours”—File loads everything into memory at once, while Pipe pours data through in a steady stream, preventing overflow.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 company is using Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is failing with an OutOfMemory error. The data scientist wants to minimize cost while resolving the issue. Which action should the data scientist take?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Increase the instance type to one with more memory.
Why wrong: Increases cost unnecessarily.
Use the 'auto' setting for the input mode.
Why wrong: 'auto' defaults to File mode if possible, not reducing memory.
Reduce the batch size hyperparameter.
Why wrong: May help but not as effective as Pipe mode and could impact model quality.
Change the input mode from 'File' to 'Pipe'.
Pipe mode streams data, reducing memory footprint.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Change the input mode from 'File' to 'Pipe'.
The OutOfMemory error occurs because the 'File' input mode downloads the entire training dataset to the instance's local storage before training begins, consuming significant memory. Switching to 'Pipe' mode streams data directly from S3 to the training algorithm, reducing memory footprint and avoiding the need for larger instances. This minimizes cost by using the existing instance type while resolving the memory issue.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase the instance type to one with more memory.
Why it's wrong here
Increases cost unnecessarily.
Use the 'auto' setting for the input mode.
Why it's wrong here
'auto' defaults to File mode if possible, not reducing memory.
Reduce the batch size hyperparameter.
Why it's wrong here
May help but not as effective as Pipe mode and could impact model quality.
Change the input mode from 'File' to 'Pipe'.
Why this is correct
Pipe mode streams data, reducing memory footprint.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may assume reducing the batch size (Option C) is the standard fix for memory issues, but they overlook that the 'File' input mode's full dataset download is the primary cause, and 'Pipe' mode directly addresses this without additional cost.
Detailed technical explanation
In SageMaker, 'File' mode downloads the entire dataset from S3 to the local Amazon Elastic Block Store (EBS) volume, which is then read into memory during training, causing high memory usage. 'Pipe' mode uses a streaming protocol where data is fetched on-the-fly via a FIFO pipe, allowing the algorithm to process data in chunks without storing the full dataset locally. This is particularly effective for large datasets that exceed the instance's memory capacity, as it leverages SageMaker's optimized data loading pipeline.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Change the input mode from 'File' to 'Pipe'. — The OutOfMemory error occurs because the 'File' input mode downloads the entire training dataset to the instance's local storage before training begins, consuming significant memory. Switching to 'Pipe' mode streams data directly from S3 to the training algorithm, reducing memory footprint and avoiding the need for larger instances. This minimizes cost by using the existing instance type while resolving the memory issue.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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