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

1. A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

A) Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
B) Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.
C) Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.
D) Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
E) Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.

2. You are using the Snowflake Developer API to automate the creation and management of masking policies. You need to create a masking policy that masks an email address using SHA256 hashing. You also want to ensure that the policy can be applied to multiple tables and columns without modification. Assuming you have already established a connection to Snowflake using the Developer API, which of the following code snippets BEST demonstrates how to create and apply this masking policy using Python?

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

3. You need to create a UDF in Snowflake to perform complex data validation. This UDF must access an external API to retrieve validation rules based on the input data'. You want to ensure that sensitive API keys are not exposed within the UDF's code and that the external API call is made securely. Which of the following approaches is the MOST secure and appropriate for this scenario?

A) Store the API key as an environment variable within the UDF's JavaScript code. Snowflake automatically encrypts environment variables for security.
B) Pass the API key as an argument to the UDF when it is called. Rely on the caller to provide the correct key and keep it secure.
C) Hardcode the API key directly into the UDF's JavaScript code, obfuscating it with base64 encoding.
D) Use a Snowflake Secret to securely store the API key. Retrieve the secret within the UDF using the 'SYSTEM$GET_SECRET function, and use 'SECURITY INVOKER with caution or define the UDF as 'SECURITY DEFINER with appropriate role based access controls .
E) Store the API key in a Snowflake table with strict access controls, and retrieve it within the UDF using a SELECT statement. Use 'SECURITY INVOKER to ensure the UDF uses the caller's privileges when accessing the table.

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 a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
C) Create a temporary table with average salaries per department, then join it back to the original DataFrame.
D) 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
E) Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.

5. You are tasked with optimizing a Snowpipe Streaming pipeline that ingests data from Kafka into a Snowflake table named 'ORDERS' You notice that while the Kafka topic has high throughput, the data ingestion into Snowflake is lagging. The pipe definition is as follows: "sql CREATE OR REPLACE PIPE ORDERS_PIPEAS COPY INTO ORDERS FROM @KAFKA STAGE FILE_FORMAT = (TYPE = JSON); Which of the following actions, taken individually, would be MOST effective in improving the ingestion rate, assuming sufficient compute resources are available in your Snowflake virtual warehouse?

A) Increase the size of the virtual warehouse associated with the Snowflake account.
B) Increase the number of Kafka partitions and ensure Snowflake has enough compute to consume them in parallel using Snowpipe Streaming.
C) Tune the parameter in the file format definition to a smaller value.
D) Implement batching within the Kafka producer to send larger messages.
E) Enable auto-ingest for the Snowpipe.

Solutions: