Question 1

When working with CSV, JSON, and Parquet data types in Snowflake, how does selecting the appropriate data format impact query performance and storage efficiency?

Options :

A : Parquet format optimizes query performance and reduces storage requirements

B : CSV format enhances query performance and minimizes storage needs

C : JSON format accelerates query execution but increases storage requirements

D : All formats have similar impacts on query performance and storage

Answer: A

Question 2

How do aggregate functions differ from window functions in Snowflake?

Options :

A : Window functions modify table structures.

B : Aggregate functions return single values.

C : Window functions work on entire tables.

D : Aggregate functions operate on groups of rows.

Answer: B,D

Question 3

Which aspects are important when making predictions based on data for forecasting purposes? (Select all that apply)

Options :

A : Incorporating statistical methods for accurate predictions

B : Using only basic arithmetic functions for forecasting

C : Considering trends and anomalies in historical data

D : Relying solely on historical data without considering external factors

Answer: A,C

Question 4

Why are materialized views valuable in data analysis compared to regular views?

Options :

A : Regular views simplify complex data structures for ease of analysis.

B : They offer real-time updates reflecting instantaneous database changes.

C : Materialized views provide precomputed snapshots, improving query performance.

D : Materialized views restrict data access, enhancing security.

Answer: C

Question 5

How does leveraging partition pruning optimize query performance in Snowflake?

Options :

A : Filters unnecessary partitions during query execution

B : Reduces data accessibility across multiple warehouses

C : Increases storage requirements for optimized query access

D : Limits query complexity and optimization possibilities

Answer: A

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