DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer is using AWS Glue to transform data from Amazon S3 and load it into Amazon Redshift. The job must run incrementally and process only new data since the last run. The source data is partitioned by date in S3, and new partitions are added daily. Which AWS Glue feature should the engineer enable to track previously processed data?

⚠ Common exam trap

A common mix-up: candidates confuse Data Catalog partition indexes, which optimize metadata queries, with job bookmarks, which track processed data for incremental runs.

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

✓

Job bookmarks

Job bookmarks are the AWS Glue feature designed to track previously processed data. When enabled, Glue maintains state for each job, so subsequent runs process only new data, such as new S3 partitions or files. This is the standard way to implement incremental ETL in Glue without reprocessing historical data.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Data Catalog partition index
Why it's wrong here
A Data Catalog partition index improves query performance by allowing faster partition filtering, but it does not track which data has been processed by a Glue job. It is a metadata optimization for the Data Catalog, not a job state mechanism. Enabling a partition index would not prevent the job from reprocessing old data; it only speeds up partition lookups during job execution.
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Glue DataBrew profile
Why it's wrong here
Glue DataBrew is a visual data preparation tool, and profiles are used to analyze data quality and statistics. A DataBrew profile does not track processed data for ETL jobs. This option is unrelated to incremental processing in Glue ETL. Using DataBrew would not help the engineer avoid reprocessing old partitions; it serves a different purpose entirely.
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Glue FindMatches
Why it's wrong here
FindMatches is a machine learning transform in AWS Glue used to identify duplicate or matching records across datasets. It does not track processing state or enable incremental loads. This option is a distractor because FindMatches is a Glue feature, but it is used for deduplication, not for incremental processing. It would not help the engineer process only new data since the last run.
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Job bookmarks
Why this is correct
Job bookmarks in AWS Glue track the state of data that has already been processed. When enabled, Glue stores bookmark state for each job run, allowing the job to process only new data since the last successful run. For S3 sources, bookmarks track objects and partitions based on their last modified time and other metadata. This directly addresses the requirement to process only new data incrementally without reprocessing old partitions.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

DEA-C01 Data Ingestion and Transformation Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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