SNOWPRO-CORE Practice Questions: Data Transformations Domain

Published: August 21, 2025 | 10 min read

Test your SNOWPRO-CORE knowledge with 5 practice questions from the Data Transformations domain. Includes detailed explanations and answers.

SNOWPRO-CORE Practice Questions

Master the Data Transformations Domain

Test your knowledge in the Data Transformations domain with these 5 practice questions. Each question is designed to help you prepare for the SNOWPRO-CORE certification exam with detailed explanations to reinforce your learning.

Question 1

How can you optimize the performance of a transformation query in Snowflake that involves multiple joins and aggregations?

A) Increase the warehouse size temporarily during query execution.

B) Switch to a higher Snowflake edition for better performance.

C) Use materialized views for frequently accessed aggregations.

D) Disable auto-suspend on the warehouse to keep it running.

Show Answer & Explanation

Correct Answer: C

Explanation: Option C is correct as materialized views can significantly improve query performance by storing precomputed results. Option A might help with performance but is more costly. Option B is incorrect as switching editions doesn't directly improve query performance. Option D is not related to query optimization and can lead to unnecessary costs.

Question 2

In Snowflake, you have a VARIANT column named 'json_data' in a table 'orders'. You need to extract the 'customer_id' field from this JSON data for all records. Which of the following SQL queries correctly performs this transformation?

A) SELECT json_data:customer_id FROM orders;

B) SELECT json_data['customer_id'] FROM orders;

C) SELECT json_data::customer_id FROM orders;

D) SELECT json_data->'customer_id' FROM orders;

Show Answer & Explanation

Correct Answer: A

Explanation: Option A is correct because Snowflake uses the colon (:) operator to extract fields from a VARIANT column when the field is specified as a string key. Option B is incorrect because the bracket notation is not used in Snowflake for JSON extraction. Option C is incorrect because the double colon (::) is used for casting, not for JSON field extraction. Option D is incorrect because the arrow (->) operator is not valid in Snowflake for JSON field extraction. For more details, refer to the Snowflake documentation on JSON functions and operators: https://docs.snowflake.com/en/sql-reference/functions-json.html

Question 3

In Snowflake, which SQL command would you use to create a new table that stores the result of a transformation query without using temporary storage?

A) CREATE TABLE AS SELECT

B) CREATE TRANSIENT TABLE AS SELECT

C) COPY INTO

D) INSERT INTO SELECT

Show Answer & Explanation

Correct Answer: A

Explanation: Option A is correct because 'CREATE TABLE AS SELECT' (CTAS) creates a new table and directly stores the result of a query. Option B creates a transient table, which is similar but intended for temporary data that doesn't need to be retained after a session. Option C is used for loading data into an existing table, and Option D inserts data into an existing table.

Question 4

Which of the following statements about Snowflake's capability to handle data transformations using tasks is true?

A) Tasks in Snowflake can only be scheduled to run once a day.

B) Tasks in Snowflake can be used to automate data transformation workflows using SQL statements.

C) Tasks in Snowflake automatically handle schema changes in transformation processes.

D) Tasks in Snowflake require manual intervention to start each time they run.

Show Answer & Explanation

Correct Answer: B

Explanation: Tasks in Snowflake can be used to automate data transformation workflows by running SQL statements on a schedule or based on certain conditions. They are designed to automate the execution of SQL scripts, making them ideal for data transformations. Option A is incorrect because tasks can be scheduled with more flexibility than once a day. Option C is incorrect because tasks do not automatically handle schema changes. Option D is incorrect because tasks can run automatically based on schedules or conditions without manual intervention.

Question 5

Which of the following is a best practice for optimizing query performance when transforming large datasets in Snowflake?

A) Use clustering keys on frequently filtered columns.

B) Store all data as VARIANT to handle semi-structured data efficiently.

C) Disable auto-suspend for virtual warehouses to ensure they are always ready.

D) Use a single large virtual warehouse for all workloads to maximize resource utilization.

Show Answer & Explanation

Correct Answer: A

Explanation: Option A is correct because clustering keys help optimize query performance by reducing the amount of data scanned. Option B is incorrect because while VARIANT is useful for semi-structured data, structured data should use appropriate column types. Option C is incorrect because auto-suspend helps manage costs by pausing unused resources. Option D is incorrect because it's often more efficient to use appropriately sized warehouses for different workloads.

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About SNOWPRO-CORE Certification

The SNOWPRO-CORE certification validates your expertise in data transformations and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.