DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building a pipeline that ingests records from an Amazon Kinesis data stream and writes them to Amazon S3 in Parquet format. The engineer wants to use AWS Glue to perform the transformation and needs the pipeline to handle records that arrive out of order and to deduplicate based on a record ID. Which combination of features should the engineer use?
⚠ Common exam trap
The trap here is assuming that Kinesis Data Firehose or Glue's ResolveChoice transform can deduplicate records by ID, when deduplication requires explicit windowed logic on a key and timestamp.
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
✓
Use AWS Glue streaming ETL with a Kinesis source, apply a windowed deduplication using the record ID and event timestamp, and write the results to S3 in Parquet.
Glue streaming ETL reading from Kinesis, combined with windowed deduplication on record ID and event timestamp, handles both out-of-order arrival and duplicates. Writing Parquet to S3 meets the format goal. Batch ETL from S3 adds latency and misses cross-batch duplicates, Firehose lacks built-in deduplication and does not use Glue for transformation, and ResolveChoice is not a deduplication tool.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon Kinesis Data Firehose with a Lambda function for record transformation, and rely on Firehose's built-in deduplication to handle duplicates.
Why it's wrong here
Kinesis Data Firehose does not provide built-in deduplication of records based on a record ID; it delivers records to destinations but does not deduplicate them. A Lambda function can transform records but would need custom logic to deduplicate, and Firehose's delivery model does not guarantee ordering or deduplication. This option also does not use AWS Glue for the transformation, which the scenario requires.
- ✗
Use AWS Glue batch ETL with an S3 source, schedule the job every minute, and use the DropDuplicates transform on the record ID.
Why it's wrong here
A batch ETL job that reads from S3 and runs every minute cannot ingest directly from Kinesis and would require a separate process to land stream data in S3 first. The one-minute schedule also introduces latency and does not address out-of-order records within the stream. DropDuplicates on record ID removes duplicates only within the batch, so duplicates across batches would persist, making this approach unsuitable for near-real-time deduplication.
- ✗
Use AWS Glue streaming ETL with a Kinesis source, set the job to process records in order, and use the ResolveChoice transform to merge duplicate records.
Why it's wrong here
Glue streaming ETL does not provide a simple 'process records in order' setting that reorders out-of-order records from Kinesis. ResolveChoice is designed to resolve ambiguous data types in a DynamicFrame, not to deduplicate records by ID. This option misuses ResolveChoice and assumes an ordering guarantee that Kinesis does not provide, so it does not meet the deduplication and ordering requirements.
- ✓
Use AWS Glue streaming ETL with a Kinesis source, apply a windowed deduplication using the record ID and event timestamp, and write the results to S3 in Parquet.
Why this is correct
Glue streaming ETL can read from Kinesis and process micro-batches. Applying a windowed deduplication on the record ID and event timestamp handles out-of-order arrival and removes duplicates within the window. Writing the output as Parquet to S3 satisfies the format requirement. This combination directly addresses both the ordering and deduplication needs while leveraging Glue's streaming capabilities.
Visual reference
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 |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This DEA-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 DEA-C01 exam.