You must check row counts between source and target after each load. Which goal is this?
Why D is the answer
Data quality validation
Reconciliation catches missing or duplicated data.
In detail
- Comparing row counts is data quality validation.
- Compare totals as well.
Use case
An illustrative scenario, not a real incident.
After each load, a job compares row counts, and a mismatch shows missing data.
See it in different ways
flowchart LR
A["S3 event or schedule"] --> B["Step Functions or MWAA"]
B --> C["Glue job"]
C --> D{"Quality passed?"}
D -->|Yes| E["Load Redshift"]
D -->|No| F["Notify the team"]stateDiagram-v2 [*] --> Load Load --> Validate: rules and row counts Validate --> Publish: passes Validate --> Quarantine: fails Quarantine --> Load: fix, then rerun safely Publish --> [*]
flowchart TD
Q{"Do the counts match?"}
Q -->|"Yes"| A0["Continue"]
Q -->|"No"| A1["Investigate"]Key point: Reconciliation validates loads.
Why the other options are wrong
- A. Cost allocation
- That is about cost.
- B. Network routing
- That is about networks.
- C. Identity federation
- That is about identity.