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

1. You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

A) Load the entire JSON into a 'VARIANT column and then use SQL with nested 'FLATTEN' functions to extract the desired values during query time.
B) Use a 'COPY' command with a 'TRANSFORM' clause that uses JavaScript UDFs to parse the JSON and extract the values during the load process. Load the extracted values directly into the target columns.
C) Create a view with nested 'FLATTEN' functions to extract the values from the 'VARIANT column. The view serves as the source for further transformations.
D) Use Snowpipe with auto-ingest, loading directly into the table with the 'VARIANT column. Define data quality checks with pre-load data transformation.
E) Use a stored procedure to parse the JSON data and insert values into the table row by row.

2. You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?

A) Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
B) Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.
C) Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
D) Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.
E) Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE

3. A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A) Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'
B) Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
C) Contact Snowflake Support and request restoration from Fail-safe.
D) Use the 'UNDROP DATABASE PRODUCTION command.
E) Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.

4. You have created a secure external function that uses a Snowflake secret to retrieve a cryptographic key and mask sensitive data'. However, users are still able to see the sensitive data'. Which of the following actions is MOST likely to resolve the issue?

A) Ensure that the user calling the function has the 'USAGE privilege on the security integration associated with the external function.
B) Ensure that the user has the 'USAGE privilege on the database and schema where the secret is stored.
C) Grant the 'EXECUTE FUNCTION' privilege on the function to the user.
D) Verify that the user has the 'OWNERSHIP privilege on the table containing the sensitive data.
E) Grant the ' SELECT privilege on the Snowflake secret object to the user.

5. You are building a data pipeline using Snowflake Tasks to orchestrate a series of transformations. One of the tasks, 'task _ transform data', depends on the successful completion of another task, 'task extract_data'. However, occasionally fails due to transient network issues. You want to implement a retry mechanism for 'task_extract data' without impacting the overall pipeline execution time significantly. Which of the following approaches is the most appropriate and efficient way to achieve this within the Snowflake Task framework?

A) Configure the task with an error notification integration that sends alerts upon failure. Manually monitor these alerts and manually resume the task if it fails. Use 'ALTER TASK task extract data RESUME;'
B) Create a new root-level task that checks the status of 'task_extract_data'. If it failed, the root-level task will execute a copy of the 'task_extract data' task. After this, it updates the 'task_transform_data"s 'AFTER' condition to depend on the new task that retries extraction.
C) Use the 'AFTER keyword in the 'CREATE TASK' statement for 'task_transform_data' to only execute if succeeds on its first attempt. If fails, the entire pipeline will stop, ensuring data consistency.
D) Implement a TRY...CATCH block within the task definition to catch any exceptions. Inside the CATCH block, use SYSTEM$WAIT to pause for a few seconds, then re- execute the core logic of the task. Repeat this process a limited number of times before failing the task permanently.
E) Modify the task definition to call a stored procedure. The stored procedure implements a loop with a retry counter. Inside the loop, execute the data extraction logic. If an error occurs, catch the exception, wait for a few seconds, and retry the extraction. After a specified number of retries, raise an exception to signal task failure.

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