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

2. You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?

A) Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
B) Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.
C) Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
D) Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.
E) Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.

3. You have a table 'EMPLOYEE with a column 'SALARY'. You need to mask the 'SALARY column for all roles EXCEPT for the PAYROLL ADMIN' role. You create the following masking policy:

You then apply this masking policy to the 'SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask; However, users who are not granted the 'PAYROLL _ADMIN' role are still seeing the actual salary values. What is the MOST likely reason for this and how can you resolve it?

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

4. You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

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

5. You are tasked with setting up a Kafka Connector to ingest data into Snowflake. You need to ensure fault tolerance. Which of the following Kafka Connect configurations are essential for enabling fault tolerance and ensuring minimal data loss during connector failures? Select all that apply.

A) Utilize Snowflake's auto-ingest feature alongside the Kafka Connector.
B) Configure 'errors.deadletterqueue.topic.name' to specify a Dead Letter Queue (DLQ) topic.
C) Configure 'errors.tolerance' to 'all'.
D) Set 'tasks.max' to a value greater than 1.
E) Enable Kafka Connect's internal offset storage by configuring 'offset.storage.topic' and 'config.storage.topic'.

Solutions: