- A
Increase the number of instances in the processing cluster
More instances allow parallel processing, reducing overall time.
- B
Switch to local mode to avoid network overhead
Why wrong: Local mode does not use distributed processing and is for small datasets.
- C
Change the processing script from Python to PySpark
Why wrong: Changing framework may not reduce time if the script is not optimized for Spark.
- D
Use a larger instance type, e.g., from r5.xlarge to r5.24xlarge
Why wrong: Larger instance improves performance but distributed processing scales better.
Question 1,034 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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.
A machine learning team is using SageMaker Processing jobs to run feature engineering on large datasets. The job takes a long time to complete. Which change would most likely reduce the processing time?
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 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 number of instances in the processing cluster
Increasing the number of instances in the processing cluster enables SageMaker Processing to distribute the workload across multiple nodes, leveraging parallel processing to reduce the overall execution time. SageMaker Processing uses a distributed computing model where each instance processes a subset of the data, so adding more instances directly increases parallelism and throughput for embarrassingly parallel tasks like feature engineering.
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 number of instances in the processing cluster
Why this is correct
More instances allow parallel processing, reducing overall time.
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.
- ✗
Switch to local mode to avoid network overhead
Why it's wrong here
Local mode does not use distributed processing and is for small datasets.
- ✗
Change the processing script from Python to PySpark
Why it's wrong here
Changing framework may not reduce time if the script is not optimized for Spark.
- ✗
Use a larger instance type, e.g., from r5.xlarge to r5.24xlarge
Why it's wrong here
Larger instance improves performance but distributed processing scales better.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling up (larger instance type) with scaling out (more instances), assuming that a bigger instance always yields faster processing, but for distributed data processing, horizontal scaling is usually more effective for large datasets.
Detailed technical explanation
How to think about this question
SageMaker Processing jobs use a managed cluster of EC2 instances, and the number of instances is specified via the `InstanceCount` parameter. For feature engineering tasks that are embarrassingly parallel (e.g., per-row transformations), scaling out with more instances is more effective than scaling up because it reduces the data partition size per node, minimizing memory pressure and I/O contention. In contrast, PySpark jobs require a SparkContext and are typically run on SageMaker Processing with the `SparkProcessor` class, not by simply changing the script language.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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 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: Increase the number of instances in the processing cluster — Increasing the number of instances in the processing cluster enables SageMaker Processing to distribute the workload across multiple nodes, leveraging parallel processing to reduce the overall execution time. SageMaker Processing uses a distributed computing model where each instance processes a subset of the data, so adding more instances directly increases parallelism and throughput for embarrassingly parallel tasks like feature engineering.
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.
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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Last reviewed: Jul 4, 2026
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