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MLS-C01 Modeling Practice Question

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject",
        "s3:DeleteObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/training-data/*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:DescribeTrainingJob"
      ],
      "Resource": "*"
    }
  ]
}

Refer to the exhibit. A data scientist is trying to run a SageMaker training job using a script that reads data from the S3 bucket 'my-bucket' and writes the model artifact to the same bucket. The training job fails with an access denied error. What is the likely cause?

⚠ Common exam trap

The trap here is that candidates may focus on the read operation (data input) and overlook the write operation (model artifact output), or confuse S3 permissions with SageMaker-specific API actions like CreateModel.

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 IAM role does not have permission to write to the S3 bucket for the model artifact

The training job fails with an access denied error because the IAM role used by SageMaker lacks the s3:PutObject permission (or equivalent write access) for the S3 bucket 'my-bucket'. While the script reads data from the bucket, writing the model artifact requires explicit write permissions on the same bucket. Without this, SageMaker cannot upload the model artifact, causing the job to fail.

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 IAM role does not have permission to write to the S3 bucket for the model artifact

    Why this is correct

    The policy only allows PutObject on training-data/*, but the model artifact might be saved to a different prefix (e.g., output/).

  • The IAM role does not have sagemaker:CreateModel permission

    Why it's wrong here

    CreateModel is needed for deployment, not training.

  • The IAM role does not have s3:ListBucket permission

    Why it's wrong here

    ListBucket is not required for reading or writing objects if the exact key is known.

  • The IAM role does not have ec2:DescribeInstances permission

    Why it's wrong here

    EC2 permissions are not required for SageMaker training jobs.

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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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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