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Machine Learning Implementation and OperationsmediumMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

Exhibit

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateModel",
        "sagemaker:CreateEndpointConfig",
        "sagemaker:CreateEndpoint"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "ecr:GetDownloadUrlForLayer",
        "ecr:BatchGetImage"
      ],
      "Resource": "arn:aws:ecr:us-east-1:123456789012:repository/sagemaker-inference"
    },
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/model/*"
    }
  ]
}

Refer to the exhibit. A developer has this IAM policy attached to an IAM role used by SageMaker. When attempting to create an endpoint, the operation fails with an access denied error. What is the MOST likely cause?

⚠ Common exam trap

Many exam-takers assume only s3:GetObject is needed for reading model artifacts, overlooking that SageMaker's internal validation process also requires s3:ListBucket to verify the artifact's location and existence.

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 policy is missing s3:ListBucket on the model bucket.

The error occurs because SageMaker needs to list objects in the S3 bucket where the model artifacts are stored before it can download them to create the endpoint. The attached policy grants s3:GetObject but not s3:ListBucket, which is required for the initial validation and listing of model artifacts in the bucket. Without s3:ListBucket, the CreateEndpoint API call fails with an access denied 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 policy is missing ecr:DescribeRepositories.

    Why it's wrong here

    DescribeRepositories is not required for pulling images.

  • The policy is missing s3:ListBucket on the model bucket.

    Why this is correct

    SageMaker needs to list the bucket to access model artifacts.

  • The policy is missing sagemaker:DescribeEndpoint.

    Why it's wrong here

    DescribeEndpoint is not needed for creation.

  • The policy is missing sagemaker:InvokeEndpoint.

    Why it's wrong here

    InvokeEndpoint is for invoking, not creating.

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.