DEA-C02 Exam Dumps | A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even

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Question 96/149

A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A. Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.

B. Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.

C. Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.

D. Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.

E. Create separate tables for each "PRODUCT_ID' to improve query performance.

Correct Answer: B

Analyzing the cardinality and data distribution (Option B) is crucial to understanding the effectiveness of search optimization. If 'PRODUCT_ID has skewed data distribution, search optimization might not be as effective. helps estimate the number of unique values, and histograms reveal the distribution. While estimating the cost of search optimization (Option A) is good practice, it doesn't directly address the potential skewness issue. Clustering (Option C) is a different optimization technique, and dropping/recreating the table (Option D) is a drastic measure without evidence of corruption. Creating separate tables for each 'PRODUCT_ID is not scalable and will drastically increase maintenance overhead.

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