DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is troubleshooting an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The job runs successfully but 5% of records are missing after the load. The engineer suspects data consistency issues. Which THREE actions could help diagnose and resolve the problem? (Choose THREE.)
⚠ Common exam trap
Many candidates assume performance tuning (increasing DPUs) or database-level transactions (staging tables) can fix data ingestion gaps, when the actual problem is incomplete or inconsistent file discovery from the source (S3).
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 the Redshift COPY command with a manifest file to load data.
Option A is correct because using the Redshift COPY command with a manifest file explicitly lists every S3 object to be loaded, preventing silent omissions of files that can occur with prefix-based loads and making missing records easier to detect. Option C is correct because Glue job bookmarks track which S3 files have already been processed, so enabling them prevents files from being skipped or reprocessed and helps identify gaps in the input data that cause missing records. Option E is correct because reviewing the job's CloudWatch Logs surfaces errors, warnings, and skipped-record messages from Glue and Redshift, which is the primary diagnostic step for understanding why 5% of records are missing. Option B does not belong because increasing DPUs only scales compute capacity and does not address data consistency or missing records. Option D does not belong because a staging table with a transaction ensures atomicity of the load but does not by itself diagnose or resolve records being dropped during extraction or copy.
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 the Redshift COPY command with a manifest file to load data.
Why this is correct
A manifest file explicitly lists every S3 object to load, so COPY fails loudly on missing or unreadable files rather than silently skipping them. This surfaces the 5% discrepancy and prevents partial loads, addressing the suspected consistency issue.
- ✗
Increase the number of DPUs for the Glue job.
Why it's wrong here
Adding DPUs increases parallel processing capacity and execution speed, but missing records stem from write or commit behaviour, not throughput. It is tempting because scaling DPUs is the standard remedy for slow or memory-constrained Glue jobs, and would be correct if the job were timing out or running long.
- ✓
Enable Glue job bookmarks to track processed files.
Why this is correct
Glue job bookmarks persist state per transformation context, so reruns skip already-processed S3 objects instead of reprocessing or skipping them inconsistently. This directly addresses the missing-records symptom by ruling out duplicate-suppression or stale bookmark state as the cause of the 5% loss.
- ✗
Use a staging table in Redshift with a transaction to commit.
Why it's wrong here
A staging table with a transactional commit is already the recommended pattern for atomic Redshift loads, so it addresses the symptom rather than diagnosing the root cause of the 5% loss. It is tempting because staging tables genuinely prevent partial loads during failures, making them the right fix when loads abort midway.
- ✓
Review the job's CloudWatch Logs for any error messages.
Why this is correct
CloudWatch Logs capture Glue job-level errors, including write failures to Redshift that the job may swallow without failing. Reviewing them surfaces rejected records, connection timeouts, or COPY errors explaining the 5% loss, directly addressing the missing-records symptom before deeper consistency checks.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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