MLS-C01 Practice Question: Machine Learning Implementation and Operations

Exhibit

Refer to the exhibit.

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

An IAM policy attached to a SageMaker notebook role is shown in the exhibit. A data scientist is trying to run a training job from the notebook, but the job fails with an access denied error. The training job needs to read data from 'my-bucket' and write output to 'my-bucket'. What is the most likely cause of the failure?

⚠ Common exam trap

Candidates often assume the notebook role's permissions automatically apply to the training job, but SageMaker requires a separate execution role for the training job, and the failure is due to that role lacking S3 permissions.

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 training job execution role does not have the same permissions

The IAM policy shown is attached to the SageMaker notebook role, which is used by the data scientist to interact with the notebook. However, when a training job is launched, it runs under a separate execution role (the SageMaker execution role for training jobs), not the notebook role. The training job fails because that execution role lacks the necessary S3 permissions (e.g., s3:GetObject, s3:PutObject) to read from and write to 'my-bucket'. The notebook role's permissions are irrelevant to the training job's runtime actions.

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 policy does not allow s3:ListBucket
Why it's wrong here
ListBucket may be needed for some operations but not for read/write.
✓
The training job execution role does not have the same permissions
Why this is correct
The notebook role is used for the notebook; the training job uses an execution role that may lack permissions.
✗
The policy does not allow sagemaker:CreateTrainingJob
Why it's wrong here
The policy allows CreateTrainingJob.
✗
The S3 bucket is not specified in the Resource
Why it's wrong here
The bucket is specified.
✗
The policy does not allow s3:GetObject
Why it's wrong here
The policy allows s3:GetObject.

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

MLS-C01 Practice Question: Machine Learning Implementation and Operations

Exhibit

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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