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Machine Learning Implementation and OperationsmediumMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

A data scientist is training a model using Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single ml.p3.2xlarge instance. The data is stored in S3 and is about 50 GB in size. Which action would MOST likely reduce training time?

⚠ Common exam trap

Many exam-takers assume upgrading to a larger instance (Option C) is the default solution for slow training, overlooking that the bottleneck is data ingestion (File mode download) rather than compute, and that Pipe mode is a cost-effective alternative that directly addresses the I/O bottleneck.

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

Change the input mode from File to Pipe.

Changing the input mode from File to Pipe reduces training time by streaming data directly from S3 to the training algorithm without first downloading it to the local disk. In File mode, SageMaker first downloads the entire 50 GB dataset to the instance's Amazon EBS volume, which adds significant I/O latency and storage overhead. Pipe mode uses a Linux FIFO (named pipe) to feed data on-the-fly, eliminating the download step and allowing the GPU to start processing sooner.

Answer analysis

Option-by-option breakdown

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

  • Enable automatic data sharding in the SageMaker training job.

    Why it's wrong here

    Automatic data sharding does not exist; SageMaker uses a single data channel by default.

  • Enable S3 server-side encryption on the training data.

    Why it's wrong here

    Encryption does not affect training speed.

  • Use a larger instance type, such as ml.p3.16xlarge.

    Why it's wrong here

    While larger instances have more compute, the bottleneck is likely data loading, not compute capacity.

  • Change the input mode from File to Pipe.

    Why this is correct

    Pipe mode streams data directly from S3, reducing I/O 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: Jul 4, 2026

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