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
Analysts' queries filter a 10 TB fact table by transaction date but still scan the entire table. What is the most effective optimization?
Answer: Partition the table by transaction date to enable partition pruning
Date partitioning lets the engine skip partitions outside the filter range, drastically cutting data scanned.
Why does columnar storage typically improve performance for analytical workloads?
Answer: It reads only the columns referenced by a query and compresses similar values well
Columnar formats minimize I/O by reading only needed columns and achieve high compression on homogeneous data.
What is the primary trade-off when introducing materialized views to speed up dashboards?
Answer: Additional storage and refresh overhead, with possible data staleness between refreshes
Materialized views precompute results, costing storage and refresh compute and potentially serving stale data.
A distributed join runs slowly because a few tasks process far more rows than others due to one dominant key value. Which technique best addresses this?
Answer: Salting the skewed key to spread rows across tasks
Salting adds a random component to hot keys so their rows distribute evenly across workers.
Cloud data warehouse costs tripled after auto-scaling was enabled. Which FinOps practice should the CDO implement first?
Answer: Set resource monitors and budgets with showback or chargeback by team
Budgets, monitors, and cost attribution create visibility and accountability without halting the business.
A data lake's query performance degrades because streaming ingestion writes millions of tiny files. What is the standard remedy?
Answer: Periodic compaction of small files into larger ones
Compaction reduces file-open and metadata overhead caused by the small-files problem.
Why might a repeated warehouse query suddenly stop benefiting from the result cache?
Answer: The underlying table data changed, invalidating the cached result
Result caches are invalidated when source data changes so users never receive outdated results.
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