The answer is s3:ListBucket. This permission is required because SageMaker training must verify that the output S3 bucket exists before it can write the model artifact; the s3:PutObject action alone is insufficient, as the service first performs a list operation to confirm the bucket’s existence and accessibility. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how SageMaker interacts with S3 during training job setup—a common trap is assuming only write permissions are needed, but the service’s internal validation step demands read-level access to the bucket. Remember that SageMaker treats the output path as a destination it must first discover, not just write to. A useful memory tip: “List before you Put” — SageMaker lists the bucket to confirm it exists before putting any object, so always include s3:ListBucket on the bucket resource when granting write access for training jobs.
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 setting up a SageMaker training job and has attached this IAM policy to the execution role. The training job fails with an access denied error when trying to write to the output path 's3://my-bucket/output/model.tar.gz'. What additional permission is needed?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
s3:ListBucket
The training job fails because SageMaker needs to verify that the output S3 bucket exists before writing to it. The s3:ListBucket permission is required to list the contents of the bucket (or confirm its existence) as part of the write operation. Without this permission, the service cannot validate the bucket, resulting in an access denied error even if s3:PutObject is allowed.
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.
✓
s3:ListBucket
Why this is correct
SageMaker requires ListBucket permission to access the bucket.
Related concept
Read the scenario before looking for a memorised answer.
✗
s3:GetObject for the output path
Why it's wrong here
GetObject is already allowed.
✗
s3:DeleteObject
Why it's wrong here
Not needed for writing.
✗
iam:PassRole on the role itself
Why it's wrong here
Already allowed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume only s3:PutObject is needed for writing to S3, but AWS services like SageMaker often require s3:ListBucket to verify the bucket exists before performing write operations.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker's training container uses the AWS SDK to perform a HeadBucket or ListObjectsV2 call against the output bucket before writing the model artifact. This is a pre-flight check to ensure the bucket exists and is accessible. Without s3:ListBucket, the SDK cannot confirm bucket existence, causing a 403 AccessDenied even if the PutObject permission is present. In real-world scenarios, this often catches users who grant only s3:PutObject and s3:GetObject, forgetting the implicit list permission needed for bucket validation.
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: s3:ListBucket — The training job fails because SageMaker needs to verify that the output S3 bucket exists before writing to it. The s3:ListBucket permission is required to list the contents of the bucket (or confirm its existence) as part of the write operation. Without this permission, the service cannot validate the bucket, resulting in an access denied error even if s3:PutObject is allowed.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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. A data scientist has this IAM policy attached to an IAM role used by SageMaker. When trying to create a training job, the scientist gets an access denied error. The training data is in 's3://my-bucket/training-data/'. What is the most likely cause?
easy
A.The bucket name is misspelled
B.The S3 resource ARN is incorrect
✓ C.Missing s3:ListBucket permission
D.The sagemaker:CreateTrainingJob action is not allowed
Why C: Option C is correct. The policy allows 's3:GetObject' but not 's3:ListBucket', which is required for SageMaker to access objects in the bucket. Option A is wrong because the actions are allowed. Option B is wrong because the resource is specified correctly. Option D is wrong because the bucket and prefix are correct.
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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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