During pre-production testing of a new sales-reporting ETL pipeline, the data team wants to verify that the system can still deliver accurate, complete hourly datasets if holiday-season traffic suddenly grows to five times the normal load. Which data-quality assurance practice should they perform to address this requirement?
Hold a user acceptance test where analysts review dashboard visuals for layout and formatting consistency.
Perform a load test using typical production volume to establish baseline throughput and response times.
Create a detailed data dictionary and lineage diagram to confirm business term definitions and transformations.
Run a stress test that feeds the pipeline with synthetic peak-season volume and monitors errors, latency, and recovery.
Stress testing intentionally drives a system beyond its normal operating limits to expose bottlenecks, failures, and recovery behavior under extreme conditions. Because the team must confirm that the pipeline keeps functioning when input volume spikes to five times the typical load, a stress test is the most appropriate method. A load test would measure performance at expected or slightly elevated volumes, not at overload conditions. User acceptance testing checks that outputs meet business needs but does not evaluate capacity. Creating documentation such as a data dictionary improves governance and explainability but does not test runtime performance.
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CompTIA Data+ DA0-002 (V2)
Data Governance
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