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Machine Learning Implementation and Operations →hardMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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:

``` tensorflow_estimator = TensorFlow( entry_point='train.py', role='arn:aws:iam::123456789012:role/SageMakerExecutionRole', instance_count=1, instance_type='ml.p3.2xlarge', output_path='s3://my-bucket/output', framework_version='2.3', py_version='py37', ) ```

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.

A
The training script must save the model to /opt/ml/model/saved_model instead of /opt/ml/model.
Why wrong: Wrong: Saving to /opt/ml/model is correct; SageMaker uploads the entire directory.
B
The SageMaker execution role does not have the s3:PutObject permission for the S3 bucket.
Correct: The role needs s3:PutObject to write to S3.
C
The output_path parameter is incorrectly formatted; it should include a trailing slash.
Why wrong: Wrong: The output_path format is correct; trailing slash is optional.
D
The TensorFlow estimator requires the model_dir parameter to be set to the S3 output path.
Why wrong: Wrong: model_dir is not a parameter of TensorFlow estimator; output_path is used.

Full breakdown with real-world context →

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 SageMaker execution role does not have the s3:PutObject permission for the S3 bucket.

The training script correctly saves the model to /opt/ml/model, which is the default directory that SageMaker automatically uploads to the S3 output path at the end of training. Since the job completes successfully, the script ran without errors. The most likely cause is that the SageMaker execution role lacks the s3:PutObject permission on the S3 bucket. Although the bucket policy allows write access from the role, the role itself must have the appropriate IAM permission. Option B is correct. Option A is incorrect because saving to /opt/ml/model is correct. Option C is incorrect because output_path does not require a trailing slash and is correctly formatted. Option D is incorrect because TensorFlow estimator does not have a model_dir parameter that overrides the default; the default is /opt/ml/model.

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 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
Read the scenario before looking for a memorised answer.
✗
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: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

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

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 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.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

What to study next

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..

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 training script correctly saves the model to /opt/ml/model, which is the default directory that SageMaker automatically uploads to the S3 output path at the end of training. Since the job completes successfully, the script ran without errors. The most likely cause is that the SageMaker execution role lacks the s3:PutObject permission on the S3 bucket. Although the bucket policy allows write access from the role, the role itself must have the appropriate IAM permission. Option B is correct. Option A is incorrect because saving to /opt/ml/model is correct. Option C is incorrect because output_path does not require a trailing slash and is correctly formatted. Option D is incorrect because TensorFlow 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?

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.

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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.