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MLS-C01 Modeling Practice Question

Network Topology
$ aws sagemaker describe-training-jobtraining-job-name my-jobRefer to the exhibit."TrainingJobName": "my-job","TrainingJobStatus": "Failed","HyperParameters": {"sagemaker_program": "train.py","sagemaker_submit_directory": "s3://my-bucket/code/"},"InputDataConfig": ["ChannelName": "training","DataSource": {"S3DataSource": {"S3DataType": "S3Prefix","S3Uri": "s3://my-bucket/data/train/"

A data scientist ran a SageMaker training job that failed with the error shown. The training script expects the data in '/opt/ml/input/data/training/train.csv'. What is the most likely issue?

⚠ Common exam trap

Candidates often confuse the channel name (which is arbitrary) with the S3 data path format, assuming the error is about the channel name mismatch rather than the distinction between pointing to a file versus a folder in S3.

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

The S3 data path should point to the exact file, not the folder

The SageMaker training job expects the S3 data path to point directly to the CSV file (e.g., s3://bucket/train.csv), not to a folder containing the file. When the path points to a folder, SageMaker downloads the folder contents but the training script's hardcoded path '/opt/ml/input/data/training/train.csv' fails because the file is not placed at that exact location—SageMaker copies the file into the channel directory with its original name, but the folder path causes the file to be nested or missing, leading to a file-not-found error.

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 hyperparameter 'sagemaker_program' is misspelled

    Why it's wrong here

    The hyperparameter is correct.

  • The training script has a bug in reading the file

    Why it's wrong here

    The error indicates file not found, not a read bug.

  • The channel name should be 'train' instead of 'training'

    Why it's wrong here

    The channel name matches the script path '/opt/ml/input/data/training/'.

  • The S3 data path should point to the exact file, not the folder

    Why this is correct

    SageMaker copies the prefix content into the channel directory; if train.csv is not at the root of that prefix, the path is wrong.

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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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.