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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You've created a JavaScript stored procedure using Snowpark to transform data'. The stored procedure is failing, and you suspect an issue with how Snowpark is handling null values during a join operation. Given two Snowpark DataFrames, and 'df2 , what is the expected behavior when performing an inner join on a column containing null values in both DataFrames, and how can you mitigate potential issues?

A) The inner join will treat null values as equal, resulting in rows where the join column is null in both DataFrames being included in the result. To avoid this, you should filter out null values before the join.
B) The behavior of the inner join with null values is undefined and may vary depending on the data types and the specific version of Snowpark. Explicit null handling is always required.
C) Inner Join will not throw an error, and will exclude the rows where join column is null. If you need to join records with null values, pre-processing dataframes using to replace null with a valid sentinel value before performing the join is one way to handle this.
D) The inner join will automatically exclude rows where the join column is null in either DataFrame. There is no need for explicit null handling.
E) The inner join will exclude rows where the join column is null in either DataFrame. To include these rows, you must use a full outer join instead.

2. You have implemented a row access policy on a 'products' table to restrict access based on the user's group. The policy uses a mapping table 'user_groups' to determine which products a user is allowed to see. After implementing the policy, users are reporting significant performance degradation when querying the 'products' table. What are the MOST likely causes of this performance issue, and what steps can you take to mitigate them? Select all that apply.

A) The row access policy is interfering with Snowflake's data pruning capabilities. Ensure that the policy expression can be evaluated efficiently by Snowflake's query optimizer by using the 'USING' clause of the ROW ACCESS POLICY.
B) The 'user_groups' table is not properly indexed, causing slow lookups during policy evaluation. Create an index on the 'username' and 'group' columns of the 'user_groups' table.
C) The users do not have sufficient privileges to access the 'user_groups' table. Grant the necessary SELECT privileges to the users on the 'user_groupS table.
D) The row access policy is causing full table scans on the 'products' table. Review the query patterns and consider adding clustering keys to the 'products' table to improve data access patterns.
E) The row access policy is overly complex and contains computationally expensive functions. Simplify the policy logic and avoid using UDFs or complex subqueries within the policy definition.

3. You're using Snowpark Python to transform data in a Snowflake table called 'employee_data' which includes columns , 'department, 'salary' , and 'performance_rating'. You need to identify the top 3 highest-paid employees within each department based on their salary, but only for departments where the average performance rating is above 4.0. Which of the following approaches using Snowpark efficiently combines window functions, filtering, and aggregations to achieve this?

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

4. 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)

5. 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$PIPE to find the cause of the issue.
C) Verify that the 'DATE FORMAT is correct and that all files consistently adhere to this format. Check for corrupted files in S3 that may be preventing Snowpipe from processing subsequent files. Additionally, review the Snowpipe error notifications in Snowflake to identify the root cause of ingestion failures. Use 'SYSTEM$PIPE to troubleshoot the files not ingested
D) 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.
E) 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.

Solutions: