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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 1

You have a Snowflake Stream named 'ORDERS STREAM' on an 'ORDERS' table, which is used to incrementally load data into a historical orders table named 'HISTORICAL ORDERS'. The data pipeline involves a series of tasks: 1) Consume changes from the 'ORDERS STREAM', 2) Apply transformations and data quality checks, and 3) Merge the changes into 'HISTORICAL ORDERS' using a MERGE statement. After a recent data load, you notice that the 'HISTORICAL ORDERS' table contains duplicate records for certain 'ORDER values. The MERGE statement uses 'ORDER ID' as the matching key. You have confirmed that the transformation logic is correct and idempotent. Examine the MERGE statement below. What could be causing the duplicates, given the context of Streams and incremental loading?

A. The stream is not configured to capture DELETE operations from the ORDERS table, causing records that should have been removed in HISTORICAL ORDERS to remain.
B. The MERGE statement is not correctly handling updates and deletes from the stream. The 'WHEN NOT MATCHED' and 'WHEN MATCHED' clauses are not mutually exclusive, leading to potential insertions of duplicate rows.
C. The 'ORDERS STREAM' is retaining historical data beyond the data retention period, causing older records to be re-processed.
D. The stream's or 'BEFORE clause is being used incorrectly, potentially rewinding the stream to an earlier point in time.
E. Multiple tasks are concurrently consuming from the same 'ORDERS STREAM' without proper coordination, causing records to be processed multiple times.

Question 2

You're designing a data masking solution for a 'CUSTOMER' table with columns like 'CUSTOMER ID', 'NAME', 'EMAIL', and 'PHONE NUMBER. You want to implement the following requirements: 1. The 'SUPPORT' role should be able to see the last four digits of the 'PHONE NUMBER and a hashed version of the 'EMAIL'. 2. The 'MARKETING' role should be able to see the full 'NAME' and a domain-only version of the 'EMAIL' (everything after the '@' symbol). 3. All other roles should see masked values for 'EMAIL' and 'PHONE NUMBER. Which of the following masking policy definitions BEST achieves these requirements using Snowflake's built-in functions and RBAC?

A.

B.

C.

D.

E.

Question 3

You have a Snowflake Stream named 'PRODUCT CHANGES' created on a table 'PRODUCTS'. A downstream task attempts to consume records from the stream, but occasionally fails with a 'Table PRODUCTS has been altered' error. The 'PRODUCTS' table undergoes DDL changes (e.g., adding/dropping columns) infrequently, but these changes are necessary for evolving business requirements. How can you design a more resilient data pipeline that minimizes disruptions caused by DDL changes to the 'PRODUCTS' table while still leveraging the 'PRODUCT CHANGES' stream?

A. Implement error handling in the downstream task to automatically retry consuming records from the 'PRODUCT CHANGES' stream after a delay, assuming the DDL changes will be completed quickly.
B. Before executing any DDL changes on the 'PRODUCTS' table, drop and recreate the 'PRODUCT CHANGES' stream. This ensures the stream definition is always in sync with the table structure.
C. Create a task that monitors the 'PRODUCTS' table for DDL changes using 'INFORMATION SCHEMA. TABLES'. When a change is detected, pause the downstream task, execute the DDL change, and then resume the downstream task after a short delay.
D. Create a new Stream on the 'PRODUCTS' table after each DDL change. The downstream task should dynamically switch to consuming from the new stream when the old stream encounters an error.
E. Use a materialized view instead of a standard view as the source for the stream. Materialized views are less susceptible to issues when the underlying base table changes

Question 4

Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?

A. Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.
B. Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
C. Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.
D. Create a temporary table with average salaries per department, then join it back to the original DataFrame.
E. Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.

Question 5

A data engineer is using Snowpark Scala to create a UDF that calculates the distance between two geographical coordinates (latitude and longitude) using the Haversine formula'. The function should accept four 'Double' values (latl, lonl , lat2, lon2) and return the distance in kilometers as a 'Double'. The UDF must be named 'haversine distance'. What is the correct Scala code to define and register this UDF with Snowflake, including the import statements required for using Snowpark functions?

A. Option E
B. Option C
C. Option B
D. Option A
E. Option D

Solutions: