DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue to read data from an Amazon Kinesis Data Stream. The Glue job is configured to process the stream in micro-batches. The engineer notices that the job is not processing all records and sometimes skips data. The Kinesis stream has multiple shards, and the Glue job is using the 'kinesis' connection type. What is the most likely cause of the missing records?
⚠ Common exam trap
The trap here is focusing on starting position or scaling issues, when the core problem is often checkpointing, which ensures continuity across job 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
✓
The Glue job is not checkpointing its progress correctly, so it reprocesses or skips records when the job restarts or fails.
Checkpointing is critical for AWS Glue jobs reading from Kinesis Data Streams. Without proper checkpointing, the job cannot track which records have been processed, leading to data loss or duplication upon restart. The most likely cause of missing records is incorrect or disabled checkpointing, which causes the job to resume from an outdated position.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Glue job is reading from the stream using the 'GetRecords' API without specifying a shard iterator, causing it to miss records.
Why it's wrong here
The Glue Kinesis connector handles shard iterators internally. It is not typical for a user to manually specify shard iterators in a Glue job. Missing a shard iterator would cause an error, not silent skipping. This option is technically inaccurate regarding how Glue interacts with Kinesis.
- ✓
The Glue job is not checkpointing its progress correctly, so it reprocesses or skips records when the job restarts or fails.
Why this is correct
AWS Glue uses checkpoints to track the last processed record in a Kinesis stream. If checkpointing is not configured or fails, the job may start from an incorrect position after a restart, leading to skipped or reprocessed records. This is a common cause of missing data in stream processing jobs. Ensuring proper checkpointing is essential for exactly-once or at-least-once processing.
- ✗
The Glue job is not configured with the correct starting position, so it starts reading from the latest record instead of the earliest.
Why it's wrong here
The starting position determines where the job begins reading when it first starts, but it does not cause skipping of records during processing. If the job starts from the latest, it would miss older records, but the issue described is about skipping records during ongoing processing. This is not the most likely cause of intermittent data loss.
- ✗
The Glue job is using the 'kinesis' connection but has not enabled the '--enable-auto-scaling' option, causing it to under-provision shards.
Why it's wrong here
Auto-scaling is not a valid Glue job option for Kinesis. Glue jobs can scale by adding more workers, but the number of shards in a Kinesis stream is fixed and determines the maximum throughput. Under-provisioning of Glue workers could cause backpressure, but not direct record skipping. This option is based on a misunderstanding of Glue and Kinesis scaling.
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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
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