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

1. You are using Snowpipe to load data from an AWS S3 bucket into Snowflake. The data files are compressed using GZIP and are being delivered frequently. You have observed that the pipe's backlog is increasing and data latency is becoming unacceptable. Which of the following actions could you take to improve Snowpipe's performance? (Select all that apply)

A) Reduce the number of columns in the target Snowflake table. Fewer columns reduce the overhead of data loading.
B) Check if the target table has any active clustering keys defined which could be causing slow down
C) Optimize the file size of the data files in S3. Smaller files are processed faster by Snowpipe.
D) Increase the virtual warehouse size associated with the pipe.
E) Ensure that the S3 event notifications are correctly configured and that there are no errors in the event delivery mechanism.

2. You have implemented a masking policy on the 'EMAIL' column of a 'USERS' table. The policy masks the email address for all users except those with the 'SUPPORT' role. You now need to grant the 'SELECT' privilege on this table to a new role, 'ANALYST. You want to ensure that the masking policy continues to work as expected for the 'ANALYST' role. Which of the following SQL statements should you execute?

A)

B)

C)

D)

E)

3. You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

A) The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.
B) The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.
C) The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.
D) DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.
E) The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS

4. You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.

A) Restore the entire Snowflake account to a point in time before the incorrect transformation.
B) Create a new table with the correct data and load from the original table filtered by a range of transaction IDs excluding the incorrect range.
C) Use the UNDROP TABLE command if the table was dropped accidentally, then manually re-apply the correct transformations.
D) Use Time Travel to query the historical version of the 'TRANSACTIONS' table using the 'AT' or 'BEFORE clause with either the timestamp or query ID. Then, use 'INSERT OVERWRITES or ' REPLACE TABLES statement to replace the current content of the original table with the historical data.
E) Create a clone of the ' TRANSACTIONS' table using Time Travel, specifying the 'AT' or 'BEFORE clause with either the timestamp or query ID of the desired state. Then, replace the original table with the cloned table.

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

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

Solutions: