Optimize Query Performance COF-C03 Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A company has a large fact table (10 TB) that is frequently queried with filters on `order_date` and `customer_id`. The table is currently unclustered, and queries that filter on `order_date` alone scan many micro-partitions. The data is loaded daily with new orders. What should the data team do to improve query performance for these filters?

   • ACreate a composite clustering key on (`order_date`, `customer_id`).
   • BUse a larger warehouse for all queries.
   • CEnable search optimization on the table.
   • DCreate a clustering key on `order_date` only.
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2. 12foundation · easy

   Which practice is most likely to improve query performance by reducing unnecessary transformations?

   • AUsing VARCHAR for all columns to simplify the schema
   • BStoring dates as strings to preserve formatting
   • CUsing SELECT * to retrieve all columns
   • DAvoiding the use of functions on columns in WHERE clauses
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4. 13foundation · easy

   In which scenario would a materialized view be most beneficial?

   • AA one-time ad-hoc query is run to explore a new dataset
   • BA query filters on a column that is not indexed
   • CA dashboard runs the same aggregation query on a large table every few minutes
   • DA table is updated frequently with many small inserts and the query is rarely run
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5. 14application · medium

   A company runs a mix of short, frequent queries and occasional long-running reports on the same warehouse. The short queries are often queued behind the long reports, causing delays. The company wants to reduce queueing without increasing overall cost significantly. What should they do?

   • AEnable multi-clustering on the current warehouse.
   • BCreate a separate warehouse for the long-running reports.
   • CSet auto-suspend to a shorter timeout.
   • DIncrease the size of the current warehouse.
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6. 15foundation · easy

   Why is it recommended to use the smallest appropriate data type for a column in Snowflake?

   • AIt makes the column nullable
   • BIt automatically creates a clustering key on the column
   • CIt enables the search optimization service
   • DIt reduces storage and improves query performance by decreasing the amount of data read
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