Performance Monitoring & Optimization Flashcards
7 cards from real CDO practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Performance Monitoring & Optimization flashcards as text
A customer address passes all format validation rules but the customer moved two years ago. Which data quality dimension is failing?
Answer: Accuracy
Accuracy measures whether data correctly reflects the real-world entity, beyond merely conforming to format rules.
On a statistical process control chart tracking daily record counts, a point falls beyond three standard deviations from the mean. What does this usually signal?
Answer: A special-cause variation that warrants investigation
Points outside the 3-sigma control limits indicate special-cause variation that is unlikely to be random.
According to the 1-10-100 rule of data quality, what does the "100" represent?
Answer: The cost of a failure when bad data is used and acted on
The rule says prevention costs $1, correction $10, and failure from using bad data $100.
Alerts on a retail sales metric fire every holiday season, causing alert fatigue. What is the best monitoring improvement?
Answer: Use dynamic, seasonality-aware baselines for anomaly thresholds
Seasonality-aware baselines adjust expected ranges so normal peaks are not flagged as anomalies.
An executive dashboard shows incorrect revenue figures. Which capability most accelerates root cause analysis?
Answer: End-to-end data lineage tracing the figure back to upstream sources
Lineage reveals which upstream tables, transformations, and jobs feed the metric so the failure point can be found quickly.
When building an enterprise data quality scorecard, why should critical data elements (CDEs) be weighted more heavily?
Answer: Their defects have the greatest business, regulatory, or financial impact
Weighting by business impact ensures the score reflects the data that matters most to decisions and compliance.
Which practice best prevents upstream schema changes from silently breaking downstream analytics pipelines?
Answer: Data contracts between producers and consumers enforced in CI/CD
Enforced data contracts catch breaking schema or quality changes before they are deployed to production.
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