The answer is that the SageMaker execution role lacks the `s3:PutObject` permission for the S3 bucket. Even though the bucket policy allows write access from the role, the IAM role itself must explicitly grant the `s3:PutObject` action on the bucket—SageMaker evaluates both the identity-based policy (the role) and the resource-based policy (the bucket), and a missing permission on the role side will block the upload. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how SageMaker training jobs interact with S3: the framework automatically copies everything from `/opt/ml/model` to the `output_path` after training, so the script saving to the correct directory is not the issue. A common trap is assuming a permissive bucket policy is sufficient, but the execution role’s IAM policy is the primary gatekeeper. Memory tip: think “Role first, bucket second”—the role must have the write key before the bucket can unlock the door.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 company uses Amazon SageMaker to train machine learning models. The data science team has developed a training script that uses TensorFlow. They want to run the training job on a GPU instance (ml.p3.2xlarge) and store the model artifact in Amazon S3. The training job completes successfully, but the model artifact is not saved to S3. The team has confirmed that the S3 bucket policy allows write access from the SageMaker execution role. The training script uses the TensorFlow estimator with the following configuration:
The train.py script saves the model using `model.save('/opt/ml/model')`. What is the MOST likely reason the model artifact is not being saved to S3?
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 SageMaker execution role does not have the s3:PutObject permission for the S3 bucket.
The TensorFlow estimator's output_path specifies where the model artifact should be uploaded after training. However, SageMaker automatically uploads the contents of /opt/ml/model to S3 at the end of training. The script is saving to the correct directory. The issue is likely that the training script is not saving the model correctly or the training fails before saving. But given the job completes successfully, the most common cause is that the SageMaker execution role does not have permission to write to the S3 bucket. The bucket policy allows write access, but the IAM role may lack the necessary S3 permissions. Option C is correct because the role needs s3:PutObject permission on the bucket. Option A is incorrect because the output_path is correctly specified. Option B is incorrect because the script saves to the right directory. Option D is incorrect because the estimator does not have a 'model_dir' parameter that overrides the default; the default is /opt/ml/model.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 training script must save the model to /opt/ml/model/saved_model instead of /opt/ml/model.
Why it's wrong here
Wrong: Saving to /opt/ml/model is correct; SageMaker uploads the entire directory.
✓
The SageMaker execution role does not have the s3:PutObject permission for the S3 bucket.
Why this is correct
Correct: The role needs s3:PutObject to write to S3.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
The output_path parameter is incorrectly formatted; it should include a trailing slash.
Why it's wrong here
Wrong: The output_path format is correct; trailing slash is optional.
✗
The TensorFlow estimator requires the model_dir parameter to be set to the S3 output path.
Why it's wrong here
Wrong: model_dir is not a parameter of TensorFlow estimator; output_path is used.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Command / output trap
Wrong: The output_path format is correct; trailing slash is optional.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: The SageMaker execution role does not have the s3:PutObject permission for the S3 bucket. — The TensorFlow estimator's output_path specifies where the model artifact should be uploaded after training. However, SageMaker automatically uploads the contents of /opt/ml/model to S3 at the end of training. The script is saving to the correct directory. The issue is likely that the training script is not saving the model correctly or the training fails before saving. But given the job completes successfully, the most common cause is that the SageMaker execution role does not have permission to write to the S3 bucket. The bucket policy allows write access, but the IAM role may lack the necessary S3 permissions. Option C is correct because the role needs s3:PutObject permission on the bucket. Option A is incorrect because the output_path is correctly specified. Option B is incorrect because the script saves to the right directory. Option D is incorrect because the estimator does not have a 'model_dir' parameter that overrides the default; the default is /opt/ml/model.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
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?
Static NAT maps one inside address to one outside address.
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 company is using Amazon SageMaker to train a machine learning model. The training job is configured to use the File mode to download data from S3 to the training instances. The training data is stored in a single S3 bucket with multiple prefixes. Which TWO actions are required to ensure the training job can access the data? (Choose TWO.)
hard
✓ A.Grant the SageMaker execution role s3:GetObject permission for the data bucket.
B.Configure the training job to use Pipe mode.
✓ C.Specify the S3 data channel with the correct prefix.
D.Concatenate all data files into a single file.
E.Convert the data to RecordIO-protobuf format.
Why A: Options A and D are correct. Option A: The IAM role must have s3:GetObject permission for the bucket. Option D: The input data channel must specify the S3 URI with the correct prefix. Option B is wrong because File mode does not require RecordIO. Option C is wrong because Pipe mode is not used. Option E is wrong because File mode does not require data in a single file.
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