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最新的 SnowPro Advanced DEA-C02 免費考試真題:

1. You need to implement a data masking solution in Snowflake for a table 'CUSTOMER DATA' containing PII. The requirement is to mask the email address based on the user's role: if the user is in 'ANALYST ROLE , the email address should be partially masked (e.g., 'a @example.com'), otherwise, it should be fully masked (e.g., @ .com'). Which of the following masking policy definitions and subsequent actions will correctly implement this?

A) Create two separate masking policies, one for 'ANALYST_ROLE' and one for all other roles. Apply both policies to the 'EMAIL' column of 'CUSTOMER DATA'. Grant the 'APPLY MASKING POLICY privilege on the 'CUSTOMER DATA' table to the 'ANALYST_ROLE.
B) Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, fully mask using 'REGEXP REPLACE. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.
C) Create a masking policy 'email_mask' that always fully masks the email address. Grant the 'UNMASK' privilege on the 'EMAIL' column to the 'ANALYST ROLE
D) Create a masking policy 'email_mask' using 'REGEXP_REPLACE to replace the first part of the email with asterisks if the current role is not 'ANALYST_ROLE' , otherwise use 'LEFT and ' REGEXP_REPLACE to mask only part of the username. Apply this policy to the 'EMAIL ' column of 'CUSTOMER DATA'.
E) Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, return original value. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.

2. You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

A) Share the underlying table and provide the consumer account with the anonymization algorithm separately.
B) Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.
C) Create a view that calls the secure UDF and share that view with the consumer account.
D) Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.
E) Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.

3. A large e-commerce company uses Snowflake to store website clickstream data in a table named 'WEB EVENTS'. This table is partitioned using the 'EVENT DATE column. The company needs to analyze user behavior across different devices. A common query involves joining 'WEB EVENTS' with a smaller 'USER DEVICES' table (containing user-to-device mappings) to determine the device type for each event. However, the performance of this join operation is poor, especially when filtering 'WEB EVENTS' by a specific date range. The 'USER DEVICES table is small enough to fit in memory. What is the most effective approach to optimize this query for performance?

A) Convert the 'WEB EVENTS' table to use a VARIANT data type and query with JSON path expressions.
B) Use a standard 'JOIN' operation between 'WEB_EVENTS' and USER_DEVICES' without any modifications.
C) Broadcast the 'USER DEVICES table to all compute nodes before performing the join. (Hint: Consider using 'BROADCAST hint)
D) Use a 'LATERAL FLATTEN' function to process the data in parallel.
E) Create a materialized view that pre-joins 'WEB_EVENTS' and 'USER_DEVICES' tables without filtering

4. A data engineering team is using a Snowflake stream to capture changes made to a source table named 'orders'. They want to only capture 'INSERT and 'UPDATE operations but exclude 'DELETE operations from being captured in the stream. Which of the following configurations will achieve this requirement? Assume the stream has already been created and is named 'orders_stream'.

A) Use task and stream combination. In the task, create view using 'select from orders where metadata$isDelete = false' and create stream on that view.
B) Create a view on top of the base table that filters out deleted rows, and then create a stream on the view.
C) Create a Snowflake task that periodically truncates the stream's metadata table, removing DELETE records.
D) It's impossible to configure a stream to exclude specific DML operations. All changes are always tracked.
E) Alter the stream using the 'HIDE_DELETES parameter: 'ALTER STREAM orders_stream SET HIDE_DELETES = TRUE;'

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) 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.
B) 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;'
C) 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.
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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