The answer is that the data scientist does not have permission to create an endpoint because the assigned IAM policy lacks the `sagemaker:CreateEndpoint` action. AWS IAM policies operate on an explicit allow model, meaning every required action must be listed; even if the policy includes `CreateModel` or `CreateEndpointConfig`, the absence of `CreateEndpoint` will cause the request to fail with an access denied error. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of granular SageMaker permissions and the common trap of assuming that related actions are bundled together. The exam often presents a policy with several SageMaker actions but deliberately omits one critical permission, forcing you to identify the missing IAM permission create SageMaker endpoint. A useful memory tip is to remember the three-step deployment chain: Model, EndpointConfig, and Endpoint—if any link is missing in the policy, the endpoint creation fails.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A data scientist is assigned an IAM policy as shown. The data scientist attempts to create a SageMaker endpoint to deploy a model, but the request fails. What is the most likely reason?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The data scientist does not have permission to create an endpoint.
The IAM policy shown does not include the `sagemaker:CreateEndpoint` action, which is required to create a SageMaker endpoint. Even if the data scientist has permissions for other SageMaker actions like `CreateModel` or `CreateEndpointConfig`, the explicit absence of `CreateEndpoint` in the policy will cause the request to fail with an access denied error. AWS IAM policies must explicitly grant each action needed for the operation.
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.
✗
The data scientist does not have permission to upload the model to S3.
Why it's wrong here
The policy allows s3:PutObject.
✗
The data scientist does not have permission to create a training job.
Why it's wrong here
The policy allows sagemaker:CreateTrainingJob.
✓
The data scientist does not have permission to create an endpoint.
Why this is correct
The policy has a Deny for sagemaker:CreateEndpoint.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The data scientist does not have permission to pass roles.
Why it's wrong here
No condition key for iam:PassRole is mentioned.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume that having permissions for model creation and configuration automatically implies permission for endpoint creation, but AWS requires each SageMaker API action to be explicitly listed in the IAM policy.
Detailed technical explanation
How to think about this question
Creating a SageMaker endpoint requires three distinct API calls in sequence: `CreateModel`, `CreateEndpointConfig`, and `CreateEndpoint`. Each of these actions must be explicitly allowed in the IAM policy. A common subtlety is that `CreateEndpointConfig` and `CreateEndpoint` are separate permissions; a policy might grant `CreateModel` and `CreateEndpointConfig` but omit `CreateEndpoint`, causing the final step to fail. In real-world scenarios, this often occurs when a policy is generated from a template that covers training but not real-time inference.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The data scientist does not have permission to create an endpoint. — The IAM policy shown does not include the `sagemaker:CreateEndpoint` action, which is required to create a SageMaker endpoint. Even if the data scientist has permissions for other SageMaker actions like `CreateModel` or `CreateEndpointConfig`, the explicit absence of `CreateEndpoint` in the policy will cause the request to fail with an access denied error. AWS IAM policies must explicitly grant each action needed for the operation.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Refer to the exhibit. A data scientist is assigned an IAM policy to deploy a SageMaker model. When the scientist tries to create an endpoint, the action fails with an authorization error. What is the missing permission?
medium
✓ A.iam:PassRole
B.sagemaker:ListEndpoints
C.sagemaker:InvokeEndpoint
D.sagemaker:UpdateEndpoint
Why A: The policy allows creating training jobs, models, endpoint configs, and endpoints, but does not allow invoking the endpoint for inference. The error is for creating the endpoint, which requires `sagemaker:CreateEndpoint` which is present. However, the error might be due to missing `sagemaker:InvokeEndpoint` which is needed for testing? Actually the question says creating the endpoint fails, so maybe the policy lacks `sagemaker:CreateEndpointConfig`? But that is present. Possibly the missing permission is `sagemaker:ListTags`? No. The most common missing permission is `sagemaker:DescribeEndpoint`? But the error is authorization error, likely missing `sagemaker:CreateEndpoint`? Wait, it is present. Perhaps the policy needs `iam:PassRole` to pass a role to SageMaker. Yes, SageMaker requires `iam:PassRole` to allow the service to assume a role. So the missing permission is `iam:PassRole`. Option D is correct. Option A: `sagemaker:InvokeEndpoint` is for inference, not creation. Option B: `sagemaker:UpdateEndpoint` is for updating. Option C: `sagemaker:ListEndpoints` is for listing.
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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.
Question Discussion
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