- A
Increase the instance count for the Processing step
More instances allow distributed processing, reducing wall clock time.
- B
Enable pipeline caching for the Processing step
Why wrong: Caching skips the step if inputs unchanged, but does not speed up execution when inputs change.
- C
Use a larger instance type with more vCPUs
Why wrong: While a larger instance helps, parallelization with multiple instances is more effective for scalable speedup.
- D
Use a Tuning step instead
Why wrong: Tuning is for hyperparameter optimization, not feature engineering.
Question 802 of 1,000
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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.
An ML pipeline uses SageMaker Processing to run a feature engineering script. The script takes a long time and the team wants to speed up pipeline execution. What is the MOST effective approach?
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
✓
Increase the instance count for the Processing step
Increasing the instance count for the SageMaker Processing step enables distributed execution of the feature engineering script across multiple nodes. SageMaker Processing supports distributed processing by default when you set the instance_count > 1, which can dramatically reduce wall-clock time for embarrassingly parallel workloads like feature engineering. This is the most effective approach because it directly parallelizes the computation without requiring code changes if the script is designed to work with distributed frameworks like PySpark or if the data is sharded appropriately.
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.
- ✓
Increase the instance count for the Processing step
Why this is correct
More instances allow distributed processing, reducing wall clock time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable pipeline caching for the Processing step
Why it's wrong here
Caching skips the step if inputs unchanged, but does not speed up execution when inputs change.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
While a larger instance helps, parallelization with multiple instances is more effective for scalable speedup.
- ✗
Use a Tuning step instead
Why it's wrong here
Tuning is for hyperparameter optimization, not feature engineering.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between vertical scaling (larger instance) and horizontal scaling (more instances), where candidates mistakenly choose a larger instance type thinking it's always faster, but for distributed workloads like feature engineering, horizontal scaling is more effective and cost-efficient.
Detailed technical explanation
How to think about this question
SageMaker Processing uses a managed Spark or custom container environment where setting instance_count > 1 automatically distributes data across nodes using a distributed file system (e.g., Amazon S3 with sharded input). For custom scripts, you must ensure the code is stateless and can process a subset of data (e.g., using S3 prefix-based sharding or a distributed framework like Dask). The actual speedup is near-linear up to a point, limited by data shuffle overhead and the serial portions of the algorithm (Amdahl's law).
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the instance count for the Processing step — Increasing the instance count for the SageMaker Processing step enables distributed execution of the feature engineering script across multiple nodes. SageMaker Processing supports distributed processing by default when you set the instance_count > 1, which can dramatically reduce wall-clock time for embarrassingly parallel workloads like feature engineering. This is the most effective approach because it directly parallelizes the computation without requiring code changes if the script is designed to work with distributed frameworks like PySpark or if the data is sharded appropriately.
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.
About these practice questions
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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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