- A
A TuningStep for hyperparameter tuning.
Why wrong: Tuning is not mandatory; a single training run may suffice.
- B
A ProcessingStep to run data preprocessing and feature engineering.
Processing is typically required to prepare data.
- C
A TransformStep for batch inference on the training data.
Why wrong: Batch transform is for inference, not part of the core training pipeline.
- D
A CreateModelStep (or RegisterModelStep) to register or deploy the trained model.
Model registration/deployment is the final essential step.
- E
A ConditionStep to decide whether to train a model based on data quality.
Why wrong: ConditionStep is optional for branching; not essential for every workflow.
Question 318 of 1,000
mediumMultiple SelectObjective-mapped
MLA-C01 Practice Question: Which TWO SageMaker Pipelines steps are essential…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which TWO SageMaker Pipelines steps are essential for automating a complete ML workflow from data processing to model deployment? (Choose 2.)
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
✓
A ProcessingStep to run data preprocessing and feature engineering.
A ProcessingStep is essential because it encapsulates the data preprocessing and feature engineering logic, typically using a SageMaker Processing job with a custom container or a built-in framework like scikit-learn. This step ensures that raw data is transformed into a format suitable for model training, making it a foundational component of any automated ML pipeline.
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.
- ✗
A TuningStep for hyperparameter tuning.
Why it's wrong here
Tuning is not mandatory; a single training run may suffice.
- ✓
A ProcessingStep to run data preprocessing and feature engineering.
Why this is correct
Processing is typically required to prepare data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A TransformStep for batch inference on the training data.
Why it's wrong here
Batch transform is for inference, not part of the core training pipeline.
- ✓
A CreateModelStep (or RegisterModelStep) to register or deploy the trained model.
Why this is correct
Model registration/deployment is the final essential step.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A ConditionStep to decide whether to train a model based on data quality.
Why it's wrong here
ConditionStep is optional for branching; not essential for every workflow.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that hyperparameter tuning or conditional logic are mandatory for a complete ML workflow, when in fact the minimal essential steps are data processing and model creation/deployment.
Detailed technical explanation
How to think about this question
Under the hood, a ProcessingStep creates a SageMaker Processing job that can run arbitrary scripts on managed infrastructure, outputting processed data to Amazon S3 for downstream steps. A CreateModelStep or RegisterModelStep is critical because it creates a SageMaker Model object (or registers it in the Model Registry) from the training job's output artifacts, which is a prerequisite for deploying the model to an endpoint. In a real-world scenario, the RegisterModelStep is often used with a Model Registry to enable model versioning and approval workflows, while a CreateModelStep is used for direct deployment.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: A ProcessingStep to run data preprocessing and feature engineering. — A ProcessingStep is essential because it encapsulates the data preprocessing and feature engineering logic, typically using a SageMaker Processing job with a custom container or a built-in framework like scikit-learn. This step ensures that raw data is transformed into a format suitable for model training, making it a foundational component of any automated ML pipeline.
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: Jul 4, 2026
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.
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