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

1. You have a table 'EMPLOYEE DATA' containing Personally Identifiable Information (PII), including 'salary' and 'email'. You need to implement column-level security such that: 1) The 'salary' column is only visible to users in the 'FINANCE ROLE. 2) The 'email' column is masked with a SHA256 hash for all users except those in the 'HR ROLE. You create the following masking policies:

Which of the following SQL statements correctly applies these masking policies to the 'EMPLOYEE DATA table?

A) CREATE OR REPLACE TAG employee_data.salary VALUE 'mask_salary'; CREATE OR REPLACE TAG employee_data.email VALUE 'mask_email';
B) ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY mask email;
C) ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY = mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY = mask email;
D) ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask_salary ON COLUMN salary; ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask email ON COLUMN email;
E) ALTER TABLE EMPLOYEE_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;

2. You are ingesting data from an external stage (AWS S3) into a Snowflake table using Snowpipe. Data files are continuously being uploaded to the stage. After several hours, you notice that some data files are not being loaded. You check the Snowpipe error notifications and see 'net.snowflake.ingest.errors.FileSizeLimitExceededError'. You have already verified that the Snowpipe is correctly configured and the user has the necessary permissions. What are the MOST LIKELY reasons for this error and how can you resolve them?

A) The data files are being uploaded to the stage faster than Snowpipe can process them. Increase the value of the 'MAX CONCURRENCY parameter in the Snowpipe definition.
B) Snowflake has reached its maximum allowable data storage capacity. Increase your Snowflake storage capacity to resolve this issue.
C) The Snowpipe configuration is incorrect; specifically, the 'FILE FORMAT parameter is not correctly specified to handle the file type. Reconfigure the Snowpipe with the correct 'FILE FORMAT.
D) The Snowpipe is encountering a transient network error. Reset the pipe using ALTER PIPE REFRESH;
E) The size of the data files in the stage exceeds the maximum allowed size for Snowpipe. Split the large files into smaller files before uploading to the stage.

3. Which of the following statements are true regarding using Dynamic Data Masking and Column-Level Security in Snowflake? (Select all that apply)

A) Column-Level Security via views provides more fine-grained control over data access compared to Dynamic Data Masking.
B) Dynamic Data Masking policies can reference external tables directly without requiring special grants.
C) Using both Dynamic Data Masking and Column-Level Security (e.g. views) on the same column is redundant and will result in an error.
D) Dynamic Data Masking is applied at query runtime, while Column-Level Security through views or roles is applied when the object is created.
E) Dynamic Data Masking can be used to apply different masking rules based on the user's role, IP address, or other contextual factors.

4. You have a Snowflake table 'orders_raw' with a VARIANT column named 'order detailS that contains an array of order items represented as JSON objects. Each object has 'item id', 'quantity' , and 'price'. You need to calculate the total revenue for each order. Which SQL statement efficiently flattens the array and calculates the total revenue using LATERAL FLATTEN and appropriate casting?

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

5. You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, 'task B' , depends on the successful completion of 'task A'. 'task_B' occasionally fails due to transient network issues. To ensure the pipeline's robustness, you need to implement a retry mechanism for 'task_B' without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for 'task_B' is as follows:

A) Leverage Snowflake's event tables like QUERY_HISTORY and TASK _ HISTORY in the ACCOUNT_USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
B) Create a separate task, 'task_C', that is scheduled to run immediately after 'task will check the status of 'task_BS in the TASK HISTORY view. If 'task_B' failed, 'task_c' will re-enable 'task_B' and suspend itself. Use the parameter on 'task_B' to limit the number of retries.
C) Modify the task definition of 'task_B' to include a SQL statement that checks for the success of 'task_R in the TASK_HISTORY view before executing the main logic. If 'task_A' failed, use ' SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.
D) Utilize Snowflake's external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.
E) Embed the retry logic directly within the stored procedure called by 'task_B'. The stored procedure should catch exceptions related to network issues, introduce a delay using 'SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.

Solutions: