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

1. A financial institution needs to tokenize sensitive customer data (credit card numbers) stored in a Snowflake table named 'CUSTOMER_DATA before it's consumed by a downstream reporting application. The institution uses an external tokenization service accessible via a REST API. Which of the following approaches is the MOST secure and scalable way to implement tokenization during data loading, minimizing exposure of the raw credit card data within Snowflake?

A) Use Snowflake's Data Sharing feature to securely share the raw data with the downstream application, instructing them to perform the tokenization within their own environment.
B) Load the raw data into a staging table, then create a Snowflake Task that executes a stored procedure. The stored procedure calls the external tokenization API using 'SYSTEM$EXTERNAL_FUNCTION_REQUEST' for each row and updates the original table with the tokenized values.
C) Utilize Snowflake's Snowpipe to ingest the data directly. Inside a COPY INTO statement, use an external function to call the tokenization service during the ingestion process to tokenize the data before it's loaded into the target table.
D) Use a Snowflake UDF (User-Defined Function) written in Java that calls the external tokenization API directly. Create a masking policy that utilizes the UDF and applies it to the credit card number column.
E) Load the raw data directly into the 'CUSTOMER DATA' table. Create a masking policy that utilizes a UDF that calls the external tokenization API directly to tokenize the credit card number values on read.

2. 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 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
B) The warehouse size is too small to handle the complexity of the IN list lookup. Increase the warehouse size.
C) Statistics on the 'PAGE URL' column are outdated. Run 'ANALYZE TABLE WEB EVENTS to refresh the statistics.
D) 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.
E) Search optimization is automatically disabled when using IN clause, therefore it is important to rewrite the query without using IN operator.

3. A data engineer needs to optimize the performance of a series of complex transformations performed using Snowflake stored procedures. These procedures involve multiple table joins, aggregations, and data filtering operations. The current execution time is unacceptably long. Which of the following optimization strategies are most likely to provide the greatest performance improvements, considering both code-level optimizations and Snowflake's architecture? Select all that apply.

A) Rewrite the stored procedures to use procedural logic (e.g., loops and cursors) more extensively, as this allows for fine-grained control over the execution flow and can improve performance for certain operations.
B) Implement data caching within the stored procedures using temporary tables to store intermediate results, reducing the need to repeatedly query the same data.
C) Use external functions written in languages like Python or Java for computationally intensive tasks that are not efficiently handled by SQL, taking advantage of Snowflake's support for external functions.
D) Refactor the stored procedures to leverage set-based operations (e.g., using SQL statements with joins and aggregations) whenever possible, allowing Snowflake to parallelize the execution across multiple nodes.
E) Ensure the underlying tables are appropriately clustered and consider using materialized views for frequently accessed aggregations or pre-computed results to improve query performance.

4. You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?

A) Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.
B) SQL UDF with nested CASE statements for address standardization.
C) Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.
D) JavaScript UDF utilizing regular expressions for simple string replacements.
E) Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.

5. You are tasked with migrating data from a legacy SQL Server database to Snowflake. One of the tables, 'ORDERS' , contains a column 'ORDER DETAILS that holds concatenated string data representing multiple order items. The data is formatted as 'iteml :qtyl ;item2:qty2;...'. You need to transform this string data into a JSON array of objects, where each object represents an item with 'name' and 'quantity' fields. Which of the following steps and functions would you use in Snowflake to achieve this transformation, in addition to loading the data?

A) Use to split the string into rows, then use 'SPLIT to separate item name and quantity, and finally use 'OBJECT_CONSTRUCT and to create the JSON array.
B) Utilize a Java UDF to parse the string and directly generate the JSON array.
C) Use ' to extract item names and quantities, then use 'ARRAY_CONSTRUCT and 'OBJECT_CONSTRUCT to create the JSON array.
D) Use 'SPLIT with ';' as delimiter, then apply 'SPLIT again with ':' as delimiter. Finally, construct the JSON array using 'ARRAY_AGG' and 'OBJECT CONSTRUCT
E) Use ' STRTOK TO ARRAY' to split the string into an array, then iterate through the array using a JavaScript UDF to create the JSON objects.

Solutions: