The correct choice is to trigger an AWS Glue ETL job via an S3 event notification using Lambda, as this creates a cost-effective, event-driven pipeline that runs only when new data arrives. This architecture eliminates the need for scheduled or continuous job runs, directly addressing the requirement to transform raw JSON into Parquet format without incurring idle compute costs. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of serverless orchestration and cost optimization for data transformation workflows—a common trap is selecting a scheduled Glue job or a constantly running Spark cluster, which wastes resources. The key insight is that S3 events can invoke Lambda, which then starts the Glue job, ensuring zero cost when no data is uploaded. Memory tip: think “Event triggers, cost figures”—if the job only fires on an event, your bill stays low.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 data scientist needs to transform raw JSON data from an S3 bucket into Parquet format using AWS Glue. The job must be cost-effective and run only when new data arrives. Which solution should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a Glue ETL job triggered by an S3 event notification via Lambda.
Option D is correct because it uses an S3 event notification to invoke a Lambda function, which then triggers an AWS Glue ETL job only when new data arrives. This event-driven architecture ensures cost-effectiveness by avoiding continuous or scheduled runs, and it directly transforms raw JSON into Parquet format as required.
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.
✗
Create a Glue crawler that runs continuously.
Why it's wrong here
Crawlers discover schema, not transform data.
✗
Schedule a Glue ETL job to run every hour.
Why it's wrong here
Scheduled runs may waste resources if no new data.
✗
Use Glue DataBrew to transform data and schedule it daily.
Why it's wrong here
DataBrew is interactive, not automated event-driven.
✓
Create a Glue ETL job triggered by an S3 event notification via Lambda.
Why this is correct
Event-driven trigger ensures cost-effectiveness.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Glue crawlers (which only catalog metadata) with Glue ETL jobs (which transform data), or assume scheduled jobs are always cost-effective without considering event-driven triggers.
Detailed technical explanation
How to think about this question
Under the hood, S3 event notifications send a JSON message to Lambda via the S3 API (e.g., s3:ObjectCreated:*), and the Lambda function uses the AWS SDK to start a Glue ETL job with the specific S3 object path as a parameter. This pattern leverages the serverless nature of Lambda and Glue, ensuring zero idle cost and near-real-time processing. A real-world scenario is processing clickstream data where files arrive irregularly; event-driven triggers avoid the cost of polling or scheduled jobs.
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 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a Glue ETL job triggered by an S3 event notification via Lambda. — Option D is correct because it uses an S3 event notification to invoke a Lambda function, which then triggers an AWS Glue ETL job only when new data arrives. This event-driven architecture ensures cost-effectiveness by avoiding continuous or scheduled runs, and it directly transforms raw JSON into Parquet format as required.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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