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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. You have a 'WEB EVENTS' table that stores user activity on a website. It includes columns like 'USER ID, 'EVENT TYPE , EVENT TIMESTAMP, and 'PAGE URL'. You need to create a materialized view that calculates the number of distinct users visiting each page daily. You are also tasked with minimizing the impact on the underlying 'WEB EVENTS' table during materialized view refreshes, as other critical processes rely on it. Which of the following strategies would provide the MOST efficient solution, considering both performance and concurrency?

A) Create a task that truncates and reloads the materialized view daily. This ensures data consistency and prevents incremental refresh issues.
B) Create a materialized view with a 'REFRESH COMPLETE strategy to ensure full data consistency after each refresh, even though it may lock the underlying table.
C) Create a materialized view and configure it to incrementally refresh, leveraging Snowflake's automatic refresh capabilities without any explicit scheduling.
D) Create a materialized view and schedule regular, small batch refreshes to minimize lock contention and resource consumption on the 'WEB_EVENTS' table.
E) Create a standard materialized view that calculates the distinct user count per page daily directly from the 'WEB EVENTS table without any special configuration.

2. You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?

A) Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
B) Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
C) Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.
D) Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
E) Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.

3. You have created an external table in Snowflake that points to a large dataset stored in Azure Blob Storage. The data consists of JSON files, and you've noticed that query performance is slow. Analyzing the query profile, you see that Snowflake is scanning a large number of unnecessary files. Which of the following strategies could you implement to significantly improve query performance against this external table?

A) Partition the data in Azure Blob Storage based on a relevant column (e.g., date) and define partitioning metadata in the external table definition using PARTITION BY.
B) Create a materialized view on top of the external table to pre-aggregate the data.
C) Create an internal stage, copy all JSON Files, create and load the target table, and drop external table
D) Increase the size of the Snowflake virtual warehouse to provide more processing power.
E) Convert the JSON files to Parquet format and recreate the external table to point to the Parquet files.

4. You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)

A) Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.
B) All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.
C) External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.
D) Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.
E) Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.

5. A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?

A) Implement data clustering on a column that is frequently used in time travel query filters.
B) Decrease the parameter for the 'SALES_DATA' table.
C) Create a materialized view that pre-computes the results of common time travel queries.
D) Periodically clone the ' SALES DATA' table to a separate historical table.
E) Increase the parameter for the 'SALES_DATX table.

Solutions: