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

1. You are tasked with creating a development environment from a production database in Snowflake. The production database is named 'PROD DB' and contains several schemas, including 'CUSTOMER DATA' and 'PRODUCT DATA'. You want to create a clone of the 'PROD DB' database named 'DEV DB', but you only need the 'CUSTOMER DATA' schema for development purposes and all the data should be masked with a custom UDF 'MASK EMAIL' for 'email' column in 'CUSTOMER' table. The 'email' column is VARCHAR. Which of the following sequences of SOL statements would achieve this in Snowflake? Note: UDF MASK EMAIL already exists in the account.

A)

B)

C)

D)

E)

2. A data engineering team is responsible for processing a high volume of semi-structured JSON data ingested daily into Snowflake. The ingestion process currently uses a single 'X-Large' virtual warehouse. During peak hours, the data loading latency increases significantly, impacting downstream reporting. The team is considering either scaling up to a '3X-Large' warehouse or scaling out by creating a multi- cluster warehouse with a minimum of 2 and a maximum of 4 'X-Large' clusters. Which of the following factors should be prioritized when making this decision to optimize performance, considering cost and concurrency requirements?

A) The anticipated number of concurrent data loading jobs and the ability of Snowflake to automatically distribute these jobs across multiple clusters.
B) The complexity of the JSON data and the potential for improved parsing performance by a larger, single warehouse due to increased memory.
C) The budget constraints and the higher per-second cost of a larger warehouse compared to the potential cost savings from reduced overall execution time.
D) The type of JSON processing used. If using SQL functions like PARSE JSON, scaling out is more beneficial than scaling up.
E) The impact on other workloads sharing the same virtual warehouse, favoring scaling up to isolate the data loading process from other query activities.

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

4. A Snowflake data warehouse contains a table 'WEB EVENTS' with columns like 'EVENT ID', 'EVENT TIMESTAMP, 'USER , 'PAGE URL', and 'SESSION ID'. The data engineering team has enabled search optimization on 'PAGE URL' because analysts frequently filter on specific URLs. However, they notice that queries filtering on multiple 'PAGE URL' values (e.g., using 'WHERE PAGE URL IN ('urll', 'ur12', are not performing as well as expected. What are the potential reasons for this behavior, and what strategies can be used to improve performance in this scenario? Select all that apply:

A) The warehouse size is too small to handle the complexity of the IN list lookup. Increase the warehouse size.
B) Search optimization is not designed to efficiently handle IN list lookups with a large number of values. Consider using a temporary table or common table expression (CTE) to pre-filter the data.
C) Statistics on the 'PAGE URL' column are outdated. Run 'ANALYZE TABLE WEB EVENTS to refresh the statistics.
D) Search optimization is automatically disabled when using IN clause, therefore it is important to rewrite the query without using IN operator.
E) The number of distinct values in the 'PAGE URL' column is very high, leading to a large search access path, making IN list lookups inefficient. Consider clustering by PAGE_URL

5. You have a Snowflake task that executes a complex stored procedure. This stored procedure performs several UPDATE statements on a large table. After enabling the 'QUERY TAG' parameter, you notice that the task history in Snowflake shows frequent suspensions due to exceeding warehouse resource limits. The warehouse is already scaled to the largest size. Which combination of the following strategies would BEST address this issue and minimize task suspensions, assuming you CANNOT further scale the warehouse?

A) Implement a retry mechanism within the task's SQL code to automatically retry failed UPDATE statements after a short delay.
B) Set a lower 'SUSPEND TASK AFTER N FAILURES' value to proactively suspend the task before it consumes excessive resources.
C) Increase the 'ERROR ON N parameter for the task to allow for more consecutive timeouts before the task is suspended.
D) Break down the stored procedure into smaller, more manageable transactions and commit changes more frequently. Consider utilizing batch processing techniques.
E) Optimize the UPDATE statements in the stored procedure to reduce resource consumption by using techniques such as clustering keys, partitioning and avoiding full table scans.

Solutions: