Amazon-DEA-C01 Exam - Topic 2 Question 12 Discussion

Actual exam question for Amazon's Amazon-DEA-C01 exam

Question #: 12
Topic #: 2

A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.

Which AWS Glue feature should the data engineer use to meet this requirement?

AWorkflows

BTriggers

CJob bookmarks

DClassifiers

Show Suggested Answer

Suggested Answer: C

Problem Analysis:

The pipeline processes compressed files in S3 and must support incremental data processing.

AWS Glue features must facilitate tracking progress to avoid reprocessing the same data.

Key Considerations:

Incremental data processing requires tracking which files or partitions have already been processed.

The solution must be automated and efficient for large-scale ETL jobs.

Solution Analysis:

Option A: Workflows

Workflows organize and orchestrate multiple Glue jobs but do not track progress for incremental data processing.

Option B: Triggers

Triggers initiate Glue jobs based on a schedule or events but do not track which data has been processed.

Option C: Job Bookmarks

Job bookmarks track the state of the data that has been processed, enabling incremental processing.

Automatically skip files or partitions that were previously processed in Glue jobs.

Option D: Classifiers

Classifiers determine the schema of incoming data but do not handle incremental processing.

Final Recommendation:

Job bookmarks are specifically designed to enable incremental data processing in AWS Glue ETL pipelines.

AWS Glue Job Bookmarks Documentation

AWS Glue ETL Features

by Caprice at Mar 06, 2025, 03:33 PM

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Elin

4 months ago

Workflows are cool, but they don't handle incremental loads like job bookmarks do.

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Ma

4 months ago

Wait, can job bookmarks really handle that? Sounds too good to be true.

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Samira

4 months ago

Definitely going with job bookmarks! Makes sense for ETL.

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Lai

4 months ago

I think triggers could work too, but not as efficiently.

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Iraida

5 months ago

Job bookmarks are the way to go for incremental processing!

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Aleta

5 months ago

I practiced a similar question where job bookmarks were the answer for maintaining state in ETL processes. I think that’s what we need here too.

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Dudley

5 months ago

Classifiers seem to be related to schema detection, so I don’t think they would be the right choice for this question.

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Janna

5 months ago

I’m not entirely sure, but I feel like triggers might be more about scheduling jobs rather than handling incremental processing.

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Frederick

5 months ago

I remember studying about job bookmarks in AWS Glue, and I think they help keep track of processed data for incremental loads.

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Francoise

5 months ago

I'm leaning towards C - Job bookmarks. That seems like the most straightforward way to handle the incremental processing need. The other options like Workflows and Triggers are more about orchestration, which isn't the focus of the question. I feel pretty confident that Job bookmarks is the right answer here.

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Osvaldo

5 months ago

Workflows or Triggers could also be useful here, as they can help orchestrate the overall ETL pipeline. But I agree that Job bookmarks sounds like the most direct solution for the incremental processing requirement. I'll make sure to review that feature in detail.

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Precious

5 months ago

Hmm, I'm not sure about this one. The question mentions supporting incremental data processing, but I'm not familiar with all the Glue features. I'll have to think this through carefully and review the Glue documentation to make sure I understand the options.

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Yesenia

5 months ago

I think the answer is C - Job bookmarks. This feature allows Glue jobs to track the state of the data processing and resume from the last successful checkpoint, which seems like the right approach for incremental data processing.

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Gracia

12 months ago

I believe triggers could also be used for incremental data processing in the AWS Glue pipeline.

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Ernie

12 months ago

Haha, I bet the data engineer is wishing they had a 'Lazy' feature to just do all the work for them. But C. Job bookmarks is probably the way to go here.

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Malcom

11 months ago

C: Triggers might be helpful for scheduling the pipeline to run at specific times.

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Erinn

11 months ago

B: Workflows could also be useful for organizing the ETL process.

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Johnetta

11 months ago

A: Yeah, I agree. Job bookmarks would definitely help with incremental data processing.

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Cecilia

12 months ago

I'm going to go with C. Job bookmarks. Seems like the perfect tool for keeping track of where the pipeline left off and picking up from there on the next run.

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Martina

12 months ago

I agree with Julio, job bookmarks keep track of processed data and support incremental processing.

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Mauricio

12 months ago

Hmm, I'm torn between B. Triggers and C. Job bookmarks. Triggers could be used to kick off the pipeline based on new file arrivals, but bookmarks might be better for actually tracking the incremental progress.

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Reyes

11 months ago

You make a good point, maybe we can use both features together for a more robust solution.

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Johnetta

11 months ago

But wouldn't B. Triggers help kick off the pipeline when new files arrive?

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Lorrine

12 months ago

I think you're right, C. Job bookmarks would be better for tracking incremental progress.

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Julio

12 months ago

I think the data engineer should use job bookmarks for incremental data processing.

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Tayna

1 year ago

I think the answer is C. Job bookmarks. That seems like the most relevant feature for incremental data processing in an ETL pipeline.

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Benedict

11 months ago

Yes, job bookmarks help in maintaining the state of the ETL job and processing only the new data for incremental updates.

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Pura

11 months ago

I think job bookmarks are essential for keeping track of the last processed data and ensuring only new data is ingested.

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Simona

11 months ago

I agree, using job bookmarks would be the best option for supporting incremental data processing in the ETL pipeline.

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