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MLS-C01 Modeling Practice Question

A data scientist is training a model using SageMaker's built-in XGBoost algorithm with a large dataset stored in CSV format. The training job is using File mode. The data scientist wants to reduce the time it takes to start training. Which approach would be most effective?

⚠ Common exam trap

Many candidates assume converting to a more efficient format like Parquet will speed up training startup, but in File mode the bottleneck is the download step, not the read efficiency, so Pipe mode directly addresses the root cause.

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 mode for the input data channel.

Pipe mode streams data directly from Amazon S3 into the training container, eliminating the need to first download the entire dataset to the EBS volume. This reduces the startup time significantly because training can begin as soon as the first records arrive, rather than waiting for the full download to complete.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Increase the size of the EBS volume.

    Why it's wrong here

    EBS volume size does not affect startup time.

  • Convert the data to Parquet format.

    Why it's wrong here

    XGBoost built-in algorithm does not support Parquet.

  • Use Pipe mode for the input data channel.

    Why this is correct

    Pipe mode starts training immediately by streaming data.

  • Increase the number of training instances.

    Why it's wrong here

    More instances do not reduce startup time.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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Last reviewed: Jun 24, 2026

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