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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

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

2. You're managing a Snowflake data warehouse and need to create a development environment for testing a complex stored procedure that updates a critical table, 'SALES DATA'. The procedure is located in the 'PRODUCTION' database and you want to ensure minimal impact to the production environment during development. You decide to use cloning and time travel. Which of the following strategies is the MOST efficient and safest approach to achieve this, minimizing downtime and resource consumption in production?

A) Create a snapshot of the 'SALES DATA' table using Time Travel at a specific timestamp (e.g., 1 hour ago), then clone only the stored procedure, updating it to point to the Time Travel version of 'SALES DATA' in the development environment. This provides a consistent dataset for testing while minimizing the impact on production and cloned data volumes.
B) Clone only the 'SALES DATA' table into a development database. This minimizes storage consumption but requires developers to manually recreate or mock any dependencies the stored procedure has on other tables in the 'PRODUCTION' database.
C) Clone the entire 'PRODUCTION' database into a new development database. This ensures developers have access to all necessary data and dependencies but consumes significant storage and may impact production performance during the cloning process.
D) Clone the schema in which 'SALES_DATX is stored along with the stored procedure. Use time travel on the cloned schema to revert all objects in the schema to a point in time before the stored procedure was last run, then update the stored procedure to point to the cloned schema. This gives a consistent starting point for testing in development.
E) Clone the "PRODUCTION' database. Immediately after cloning, use Time Travel to revert the 'SALES_DATR table in the development database to a state before the stored procedure was last run in production. Then clone the stored procedure itself. This gives a starting point without the procedure's impact.

3. You're configuring a Kafka connector to load data into Snowflake. You need to ensure that only records with a specific 'event_type' field equal to 'payment_processed' are loaded into the 'PAYMENT TABLE in Snowflake. Given the following connector configuration snippet, which additional property must be added to the 'transforms' section to achieve this filtering?

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

4. You are designing a data protection strategy for a Snowflake environment that processes sensitive payment card industry (PCI) data'. You decide to use a combination of column-level security and external tokenization. Which of the following statements are TRUE regarding the advantages of using both techniques together? (Select TWO)

A) The use of both techniques increases query performance drastically.
B) Masking policies and external tokenization provide independent layers of security. If one is compromised, the other still provides protection.
C) Combining masking policies and external tokenization allows for complete elimination of PCl data from the Snowflake environment, even during processing.
D) Column-level security can be used to restrict access to the tokenization UDF itself, ensuring that only authorized users can perform tokenization or detokenization operations.
E) Tokenization ensures compliance with PCl DSS standards, while masking policies are primarily useful for internal access control and obfuscation for development environments. Using both doesn't increase security.

5. A large e-commerce company uses Snowflake to store website clickstream data in a table named 'WEB EVENTS'. This table is partitioned using the 'EVENT DATE column. The company needs to analyze user behavior across different devices. A common query involves joining 'WEB EVENTS' with a smaller 'USER DEVICES' table (containing user-to-device mappings) to determine the device type for each event. However, the performance of this join operation is poor, especially when filtering 'WEB EVENTS' by a specific date range. The 'USER DEVICES table is small enough to fit in memory. What is the most effective approach to optimize this query for performance?

A) Broadcast the 'USER DEVICES table to all compute nodes before performing the join. (Hint: Consider using 'BROADCAST hint)
B) Create a materialized view that pre-joins 'WEB_EVENTS' and 'USER_DEVICES' tables without filtering
C) Use a standard 'JOIN' operation between 'WEB_EVENTS' and USER_DEVICES' without any modifications.
D) Use a 'LATERAL FLATTEN' function to process the data in parallel.
E) Convert the 'WEB EVENTS' table to use a VARIANT data type and query with JSON path expressions.

Solutions: