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

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

2. You accidentally truncated a large table named 'SALES DATA' in your 'REPORTING DB" database. You realize this happened 2 days ago, and your account has the default Time Travel retention of 1 day. You need to recover this table with minimal downtime. Analyze the situation and determine the best course of action, considering cost and recovery time.

A) Immediately contact Snowflake Support to initiate a restore from Fail-safe, understanding that this process may take several hours or even days.
B) Raise a support ticket requesting data recovery from failsafe. Since data retention period has expired.
C) Because the data retention period has expired, the table is unrecoverable using Snowflake's built-in features; you must restore from an external backup solution if available.
D) Increase the account-level to 2 days and then use the UNDROP TABLE SALES_DATA' command.
E) Create a clone of the table using the 'AT clause and a timestamp from 1 day ago. This would prevent any additional cost.

3. You have a table 'ORDERS in your Snowflake database. You are implementing a new data transformation pipeline. Before deploying the pipeline to production, you want to validate the changes in a development environment. You decide to use Time Travel to create a snapshot of the 'ORDERS' table before the transformation and compare it with the transformed data'. Which sequence of SQL commands would best facilitate this validation, assuming your development database and schema structure mirrors production?

A)

B)

C)

D)

E)

4. You're working on a data transformation pipeline in Snowflake. You need to create a SQL UDF called that accepts the following parameters: 'price' (NUMBER) (NUMBER) (BOOLEAN) The function should calculate the final price after applying the discount. If the customer , an additional 5% discount should be applied on top of the initial discount. Choose all the valid SQL UDF definitions that accurately implement the requirements. Only one Discount Percentage needs to apply to the base price.

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

5. Consider the following Snowflake Javascript UDF designed to convert temperatures between Celsius and Fahrenheit:

Which statement regarding the UDF's security and behavior is MOST accurate?

A) The UDF is vulnerable to SQL injection because the 'unit' parameter is not validated against a predefined list of acceptable values.
B) The UDF will always return a value, even if the input 'unit is invalid, due to the implicit type conversion in JavaScript.
C) While the ' SECURE keyword hides the UDF's source code from users without sufficient privileges, users with the ACCOUNTADMIN role can still view the source code.
D) The 'SECURE' keyword ensures that the UDF's code is completely hidden from all users, including those with the ACCOUNTADMIN role.
E) The UDF is not vulnerable to SQL injection because Javascript UDFs in Snowflake are sandboxed and prevent direct SQL execution.

Solutions: