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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with creating a system to monitor the data quality of a 'SALES DATA" table. The table is updated daily with new sales records, and you need to ensure that the 'SALE AMOUNT column always contains positive values. You decide to use Snowflake tasks and streams for this purpose. Consider the following Snowflake script. What is the most appropriate way to modify the 'SALES DATA' table so the task has a 'WHEN' clause that runs only if the 'SALE AMOUNT has negative values? Assume the stream 'SALES DATA STREAM' is properly configured on 'SALES DATA'.

A)

B)

C)

D)

E)

2. A data engineer is tasked with optimizing a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a reporting table. The pipeline uses a series of Snowflake tasks orchestrated with a root task and child tasks. Performance monitoring shows inconsistent execution times for the transformation tasks. Which of the following strategies would provide the MOST granular insights into the performance bottlenecks within the pipeline and allow for targeted optimization?

A) Rely solely on the Snowflake web UI's Task History view to identify slow-running tasks.
B) Leverage Snowflake's event tables like QUERY HISTORY and TASK HISTORY in the ACCOUNT USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
C) Implement a custom logging mechanism within the transformation tasks to record execution times for each stage of the transformation process, and store these logs in a Snowflake table for analysis.
D) Enable query profiling for all queries executed within the transformation tasks using 'ALTER SESSION SET QUERY PROFILE = 'ON" , then analyze the query profiles for performance bottlenecks after each task run.
E) Use Snowflake's Resource Monitors to track overall warehouse consumption and assume that high consumption during transformation tasks indicates a bottleneck within those tasks.

3. You are tasked with creating a JavaScript stored procedure in Snowflake to perform a complex data masking operation on sensitive data within a table. The masking logic involves applying different masking rules based on the data type and the column name. Which approach would be the MOST secure and maintainable for storing and managing these masking rules? Assume performance is not your primary concern but code reuse and maintainability is the most important thing.

A) Storing masking logic in Javascript UDFs and calling these UDFs dynamically within the stored procedure based on column names and datatype
B) Defining the masking rules as JSON objects within the stored procedure code.
C) Hardcoding the masking rules directly within the JavaScript stored procedure.
D) Using external stages and pulling the masking rules from a configuration file during stored procedure execution.
E) Storing the masking rules in a separate Snowflake table and querying them within the stored procedure.

4. You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

A) Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.
B) Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.
C) Create a view that calls the secure UDF and share that view with the consumer account.
D) Share the underlying table and provide the consumer account with the anonymization algorithm separately.
E) Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.

5. You are designing a data sharing solution for a multi-tenant application where each tenant's data must be isolated. You have a 'sales' table with a 'tenant_id' column. You need to implement row-level security to ensure that each tenant can only access their own data when querying the shared table. Which of the following approaches, considering performance and security, is the MOST suitable for implementing this row-level filtering in Snowflake?

A) Implement a row access policy on the 'sales' table that filters data based on the 'tenant_id' column and the current role or user context.
B) Create a separate VIEW for each tenant, filtering by 'tenant_id'. Grant each tenant access only to their respective view.
C) Implement a user-defined function (UDF) that checks the current user's tenant ID and returns a boolean value indicating whether the row should be visible. Use this UDF in a WHERE clause in every query.
D) Create a scheduled task that duplicates the sales table into a new table for each tenant, filtering by the tenant_id.
E) Use Snowflake's data masking policies to mask all data for tenants other than the one currently querying the table.

Solutions: