Question 383 of 507
Deployment and Orchestration of ML WorkflowseasyMultiple ChoiceObjective-mapped

Quick Answer

The answer is `sagemaker:InvokeEndpoint`. This permission is required because invoking a SageMaker endpoint sends a real-time inference request to the deployed model, and IAM policies must explicitly grant this action for the SDK or CLI call to succeed. Without it, the user receives an access denied error, even if other SageMaker permissions like `sagemaker:InvokeEndpointAsync` or `sagemaker:DescribeEndpoint` are present. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of the specific action needed for synchronous inference versus asynchronous invocation, a common trap where candidates confuse `InvokeEndpoint` with broader SageMaker actions or forget that the endpoint itself does not grant invocation rights. A reliable memory tip is to think of the endpoint as a locked door: `sagemaker:InvokeEndpoint` is the key that opens it for real-time calls, while other permissions are just the address or blueprint.

MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

Exhibit

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:DescribeEndpoint",
        "sagemaker:ListEndpoints"
      ],
      "Resource": "*"
    }
  ]
}

Refer to the exhibit. A user is unable to invoke a SageMaker endpoint. The IAM policy shown is attached to the user. Which permission is missing to allow invocation?

Question 1easymultiple choice
Full question →

Exhibit

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:DescribeEndpoint",
        "sagemaker:ListEndpoints"
      ],
      "Resource": "*"
    }
  ]
}

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

sagemaker:InvokeEndpoint

To invoke a SageMaker endpoint, the user needs the `sagemaker:InvokeEndpoint` permission. The IAM policy shown lacks this action, which is required for making real-time inference requests to the endpoint. Without it, any attempt to call the endpoint via the SDK or CLI will fail with an access denied error.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

  • sagemaker:InvokeEndpoint

    Why this is correct

    InvokeEndpoint is required to send inference requests.

    Related concept

    Read the scenario before looking for a memorised answer.

  • sagemaker:DescribeEndpoint

    Why it's wrong here

    Already allowed but not sufficient for invocation.

  • sagemaker:CreateEndpoint

    Why it's wrong here

    Creating endpoints is not needed for invocation.

  • sagemaker:ListEndpoints

    Why it's wrong here

    Already allowed but not sufficient for invocation.

Common exam traps

Common exam trap: answer the scenario, not the keyword

AWS often tests the distinction between read-only permissions (like `DescribeEndpoint` or `ListEndpoints`) and the specific action required to perform an operation, leading candidates to confuse metadata access with actual invocation capability.

Detailed technical explanation

How to think about this question

Under the hood, invoking a SageMaker endpoint sends an HTTPS POST request to the endpoint's URL, which is backed by a load-balanced fleet of instances running a containerized model. The `sagemaker:InvokeEndpoint` action is checked by AWS IAM before the request reaches the endpoint, and the response includes model predictions in JSON or CSV format. In real-world scenarios, this permission is critical for applications like real-time fraud detection or chatbot inference pipelines.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this MLA-C01 question test?

Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: sagemaker:InvokeEndpoint — To invoke a SageMaker endpoint, the user needs the `sagemaker:InvokeEndpoint` permission. The IAM policy shown lacks this action, which is required for making real-time inference requests to the endpoint. Without it, any attempt to call the endpoint via the SDK or CLI will fail with an access denied error.

What should I do if I get this MLA-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 30, 2026

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This MLA-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 MLA-C01 exam.