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

1. You have implemented a Snowpipe using auto-ingest to load data from an AWS S3 bucket. The pipe is configured to load data into a table with a 'DATE column ('TRANSACTION DATE'). The data files in S3 contain a date field in the format 'YYYYMMDD'. Occasionally, you observe data loading failures in Snowpipe with the error message indicating an issue converting the string to a date. The 'FILE FORMAT' definition includes 'DATE FORMAT = 'YYYYMMDD''. Furthermore, you are also noticing that after a while, some files are not being ingested even though they are present in the S3 bucket. How to effectively diagnose and resolve these issues?

A) The issue may arise if the time zone of the Snowflake account does not match the time zone of your data in AWS S3. Try setting the 'TIMEZONE parameter in the FILE FORMAT definition. For files that are not being ingested, manually refresh the Snowpipe with 'ALTER PIPE ... REFRESH'.
B) The 'DATE FORMAT parameter is case-sensitive. Ensure it matches the case of the incoming data. Also, check the 'VALIDATION MODE and ERROR parameters to ensure error handling is appropriately configured for files with date format errors. For the files that are not ingested use 'SYSTEM$PIPE to find the cause of the issue.
C) Snowflake's auto-ingest feature has limitations and may not be suitable for inconsistent data formats. Consider using the Snowpipe REST API to implement custom error handling and data validation logic. Monitor the Snowflake event queue to ensure events are being received.
D) Verify that the 'DATE FORMAT is correct and that all files consistently adhere to this format. Check for corrupted files in S3 that may be preventing Snowpipe from processing subsequent files. Additionally, review the Snowpipe error notifications in Snowflake to identify the root cause of ingestion failures. Use 'SYSTEM$PIPE to troubleshoot the files not ingested
E) The error could be due to invalid characters in the source data files. Implement data cleansing steps to remove invalid characters from the date fields before uploading to S3. For files not being ingested, check S3 event notifications for missing or failed events.

2.

A) The Lambda function is returning a string instead of a number. Modify the Lambda function to return the discount as a number (e.g., 'discount = 0.15' instead of 'discount = '0.15")
B) The Lambda function returns the discount within a nested JSON structure Tdata': [[discount]]}'. The Snowflake function is not designed to handle this. The lambda function should return '{'data':
C) The 'RETURNS NULL ON NULL INPUT clause in the external function definition is causing the function to return NULL even when valid inputs are provided. Remove this clause.
D) The Snowflake external function is not correctly parsing the JSON response from the Lambda function. Implement a wrapper function in Snowflake to parse the JSON and extract the discount value before returning it.
E) The data types in the Lambda function and Snowflake function definition do not match. Specifically, the Lambda function expects strings while Snowflake is sending numbers and vice versa. Modify the Lambda function to handle numeric inputs and ensure the Snowflake function definition aligns with the expected output data type (FLOAT).

3. You are working with a Snowpark DataFrame named 'customer data' that contains sensitive Personally Identifiable Information (PII). The DataFrame has columns such as 'customer id', 'name', 'email' , and 'phone number'. Your task is to create a new DataFrame that only contains 'customer id' and a hash of the 'email' address for anonymization purposes, while also filtering out any customers whose 'customer id' starts with 'TEMP'. Which of the following approaches adheres to best practices for data security and efficiency in Snowpark, using secure hashing algorithms provided by Snowflake?

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

4. You've created a JavaScript UDF in Snowflake to perform complex string manipulation. You need to ensure this UDF can handle a large volume of data efficiently. The UDF is defined as follows:

When testing with a large dataset, you observe poor performance. Which of the following strategies, when applied independently or in combination, would MOST likely improve the performance of this UDF?

A) Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.
B) Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.
C) Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.
D) Replace the JavaScript UDF with a SQL UDF that uses built-in Snowflake string functions like 'REGEXP REPLACE and 'REPLACE. SQL UDFs are generally more optimized within Snowflake's execution engine.
E) Pre-compile the regular expressions used within the JavaScript UDF outside of the function and pass them as constants into the function. JavaScript regex compilation is expensive, and pre-compilation can reduce overhead.

5. You are designing a data protection strategy for a Snowflake database. You need to implement dynamic data masking on the 'CREDIT CARD' column in the 'TRANSACTIONS' table. The requirement is that users with the 'FINANCE ADMIN' role should see the full credit card number, while all other users should see only the last four digits. You have the following masking policy:

What is the next step to apply this masking policy to the 'CREDIT CARD' column?

A)

B)

C)

D)

E)

Solutions: