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

1. You are building a data pipeline in Snowflake using Snowpark Python. As part of the pipeline, you need to create a dynamic SQL query to filter records from a table named 'PRODUCT REVIEWS based on a list of product categories. The list of categories is passed to a stored procedure as a string argument, where categories are comma separated. The filtered data needs to be further processed within the stored procedure. Which of the following approaches are MOST efficient and secure ways to construct and execute this dynamic SQL query using Snowpark?

A) Using the Snowpark "functions.lit()' function to create literal values from the list of product categories and incorporating them into the SQL query, then use 'session.sql()' to run it.
B) Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.
C) Using Snowpark's on the list of product categories after converting them into a Snowflake array, and then using 'session.sql()' to execute the query.
D) Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.
E) Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.

2. A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

A)

B)

C)

D)

E)

3. A data engineering team is building a real-time data pipeline in Snowflake. Data arrives continuously and needs to be processed with minimal latency. The team is using Snowflake Streams and Tasks for incremental data processing. However, they are encountering issues where the tasks are sometimes skipped or delayed, leading to data inconsistencies. Which combination of actions would BEST address these issues and ensure reliable near real-time data processing?

A) Monitor the 'TASK HISTORY view regularly to identify skipped or delayed tasks and manually re-run them as needed. This is a reactive approach and does not prevent future occurrences.
B) Adjust the ' ERROR_INTEGRATION' parameter on the task definition to send notifications when tasks fail. This allows for manual intervention but does not prevent skipping.
C) Disable task scheduling and rely solely on Snowflake's Auto-Resume feature for warehouses. This simplifies the pipeline and reduces the chance of errors.
D) Increase the warehouse size to ensure sufficient compute resources. This will prevent tasks from being skipped due to resource contention.
E) Configure the tasks to run using a serverless compute model (Snowflake-managed compute). Ensure the parameter is set to a higher value and implement error handling within the task using TRY/CATCH blocks.

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

5. You have a Snowflake table called 'RAW ORDERS that contains semi-structured JSON data in a column named 'ORDER DETAILS. You need to extract specific fields from the JSON data, perform some data type conversions, and then load the transformed data into a relational table named 'CLEAN ORDERS'. Your requirements are as follows: 1. Extract the (STRING) from the JSON and store it as 'ORDER ID (NUMBER). 2. Extract the (STRING) from the JSON and store it as 'CUSTOMER ID (NUMBER). 3. Extract the 'order_date' (STRING) from the JSON and store it as 'ORDER DATE' (DATE). 4. Extract (STRING) from the JSON and store it as 'TOTAL AMOUNT' (FLOAT). Which of the following Snowpark Python code snippets correctly transforms the data and loads it into the 'CLEAN ORDERS table using a combination of Snowpark DataFrame operations and SQL? Assume that session 'sp' is already initialized.

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

Solutions: