MLS-C01 Data Engineering Practice Question
A data scientist is using Amazon SageMaker to train a model. The training data is stored in Amazon S3 and is approximately 500 GB. The data scientist notices that the training job is taking a long time to start because the data is being copied to the training instance's storage. The data scientist wants to reduce the startup time for subsequent training jobs. Which action should the data scientist take?
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
✓
Use Pipe input mode instead of File input mode for the training job
Using Pipe input mode streams data directly from S3 to the training algorithm without downloading, reducing startup time. Option B is wrong because FSx for Lustre is not needed for simple streaming. Option C is wrong because increasing instance storage does not address the data transfer issue. Option D is wrong because using EBS optimized instances does not change the data loading mechanism.
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 Pipe input mode instead of File input mode for the training job
Why this is correct
Pipe mode streams data from S3 directly, reducing startup time.
- ✗
Use an EBS-optimized instance type
Why it's wrong here
EBS optimization improves I/O performance but does not change the data loading method.
- ✗
Use Amazon FSx for Lustre as a high-performance file system mounted to the training instance
Why it's wrong here
Using Amazon FSx for Lustre introduces an additional high-performance file system layer, requiring data to be loaded into it from S3 before training can commence, which doesn't directly address the problem of data being copied *to the training instance's local storage* from S3 at startup. This option is tempting because FSx for Lustre excels at providing a POSIX-compliant, high-performance shared file system for compute-intensive workloads, making it ideal for scenarios where multiple instances need shared, low-latency access to a large dataset, or for burstable workloads that need to quickly process data from S3.
- ✗
Increase the size of the training instance's Amazon EBS storage volume
Why it's wrong here
Larger EBS volume does not speed up data copy from S3.
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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Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data science team uses Amazon SageMaker to train models on a large dataset stored in S3. The dataset is 500 GB in CSV format and is updated daily. The team wants to optimize data loading for training jobs to reduce I/O wait time. Which data ingestion strategy is MOST effective?
medium- A.Use SageMaker File input mode and increase the EBS volume size to 1 TB.
- ✓ B.Use SageMaker Pipe input mode to stream data directly from S3.
- C.Convert the CSV files to Parquet format and use File input mode.
- D.Load the data into an Amazon EFS file system and mount it to the training instance.
Why B: SageMaker Pipe input mode streams data directly from S3 to the training algorithm without writing to the instance's EBS volume, eliminating disk I/O bottlenecks. This is especially effective for large datasets (500 GB) that are updated daily, as it reduces startup time and avoids the need to download the entire dataset before training begins.
Variation 2. A machine learning engineer is using Amazon SageMaker to train a model. The training dataset is 2 TB and is stored in Amazon S3. The engineer wants to reduce the training time by improving data loading performance. Which data ingestion mode should be used?
easy- ✓ A.Pipe mode
- B.Incremental mode
- C.File mode
- D.Fast file mode
Why A: Pipe mode is the correct choice because it streams data directly from Amazon S3 to the training container via a Unix named pipe, bypassing disk writes and reducing I/O latency. For a 2 TB dataset, this eliminates the bottleneck of downloading data to the training instance's Amazon Elastic Block Store (EBS) volume, significantly improving data loading performance and reducing overall training time.
JA
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.