The answer is enabling checkpointing in the AWS Glue streaming job and specifying an S3 location for checkpoint data. This configuration is essential because checkpointing tracks the exact offset of data consumed from Kinesis Data Streams, periodically saving the processing state to S3. If the job fails, Glue resumes from the last committed offset, preventing duplicate records or data loss and thereby achieving exactly once processing in AWS Glue streaming. On the AWS Certified Data Engineer Associate DEA-C01 exam, this concept tests your understanding of fault-tolerant streaming mechanics; a common trap is assuming that simply partitioning output by date and hour guarantees exactly-once semantics, but without checkpointing, restarts can reprocess old data. Remember the memory tip: “Checkpoint to S3, no duplicate spree”—the S3 checkpoint location is the linchpin for idempotent recovery in Glue streaming jobs.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The data is JSON formatted and includes a timestamp field. The company wants to partition the output in Amazon S3 by date and hour, and ensure exactly-once processing semantics. Which combination of configurations 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
✓
Enable checkpointing in the AWS Glue streaming job and specify an S3 location for checkpoint data.
Option B is correct because AWS Glue streaming jobs require checkpointing to track the progress of data consumption from Kinesis Data Streams and to ensure exactly-once processing semantics. By enabling checkpointing and specifying an S3 location, Glue periodically saves the state of processed records, allowing it to resume from the last committed offset in case of failures, thus preventing duplicates or data loss.
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.
✗
Disable checkpointing and use the 'exactly_once' delivery option in Kinesis Data Streams.
Why it's wrong here
Kinesis Data Streams does not have an 'exactly_once' delivery option; checkpointing is required.
✓
Enable checkpointing in the AWS Glue streaming job and specify an S3 location for checkpoint data.
Why this is correct
Glue streaming jobs support checkpointing to S3 for exactly-once processing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon DynamoDB as a checkpoint store by configuring the Glue job with a DynamoDB connection.
Why it's wrong here
AWS Glue streaming jobs checkpoint to S3, not DynamoDB.
✗
Use Kinesis Client Library (KCL) checkpointing with a DynamoDB table.
Why it's wrong here
KCL checkpointing is for custom applications, not AWS Glue streaming ETL jobs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the checkpointing mechanism of AWS Glue (which uses S3) with the Kinesis Client Library (KCL) pattern (which uses DynamoDB), leading them to select option D or C, even though Glue streaming jobs do not support DynamoDB for checkpointing.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue streaming jobs use Apache Spark Structured Streaming, which relies on checkpointing to store the offset range of data consumed from Kinesis. The checkpoint data is written as a series of files in the specified S3 location, and upon restart, Glue reads the latest committed offset to resume processing, ensuring exactly-once semantics. A subtle behavior is that checkpointing must be enabled with a unique S3 path per job; reusing the same path across different job runs can cause offset conflicts and data duplication.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable checkpointing in the AWS Glue streaming job and specify an S3 location for checkpoint data. — Option B is correct because AWS Glue streaming jobs require checkpointing to track the progress of data consumption from Kinesis Data Streams and to ensure exactly-once processing semantics. By enabling checkpointing and specifying an S3 location, Glue periodically saves the state of processed records, allowing it to resume from the last committed offset in case of failures, thus preventing duplicates or data loss.
What should I do if I get this DEA-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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