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

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

1. A Snowflake data warehouse contains a table 'WEB EVENTS' with columns like 'EVENT ID', 'EVENT TIMESTAMP, 'USER , 'PAGE URL', and 'SESSION ID'. The data engineering team has enabled search optimization on 'PAGE URL' because analysts frequently filter on specific URLs. However, they notice that queries filtering on multiple 'PAGE URL' values (e.g., using 'WHERE PAGE URL IN ('urll', 'ur12', are not performing as well as expected. What are the potential reasons for this behavior, and what strategies can be used to improve performance in this scenario? Select all that apply:

A) The number of distinct values in the 'PAGE URL' column is very high, leading to a large search access path, making IN list lookups inefficient. Consider clustering by PAGE_URL
B) Statistics on the 'PAGE URL' column are outdated. Run 'ANALYZE TABLE WEB EVENTS to refresh the statistics.
C) Search optimization is automatically disabled when using IN clause, therefore it is important to rewrite the query without using IN operator.
D) Search optimization is not designed to efficiently handle IN list lookups with a large number of values. Consider using a temporary table or common table expression (CTE) to pre-filter the data.
E) The warehouse size is too small to handle the complexity of the IN list lookup. Increase the warehouse size.

2. A financial services company is using Snowflake Streams on a table 'TRANSACTIONS' to capture changes for auditing purposes. The 'TRANSACTIONS' table contains sensitive data, and the auditing team requires the stream to only capture changes to specific columns: 'ACCOUNT ID', 'TRANSACTION DATE', and 'TRANSACTION AMOUNT'. Which of the following approaches is the MOST efficient and secure way to achieve this requirement, ensuring minimal performance impact and data exposure?

A) Create a Stream on the 'TRANSACTIONS' table and use a masking policy on the stream's output to redact the unnecessary columns.
B) Create a Stream on the 'TRANSACTIONS' table. Periodically truncate stream and reload all data from TRANSACTION table by applying filter while loading.
C) Create a View that selects only the 'ACCOUNT ID, 'TRANSACTION DATE, and 'TRANSACTION AMOUNT columns and create a Stream on the View.
D) Create a task that clones the TRANSACTIONS table and a stream on that cloned table, limiting what changes are captured using a WHERE clause on the cloning command.
E) Create a standard Stream on the 'TRANSACTIONS table and then filter the results in downstream processing to only include the required columns.

3. You are building a data pipeline that utilizes a Snowflake stage to store intermediate results. You need to ensure data security and compliance. Which of the following methods offer the BEST approach for securing data stored in a Snowflake stage?

A) Encrypt the data at rest on the storage layer using Snowflake's built-in encryption features. Snowflake automatically encrypts all data at rest.
B) Configure the stage to use temporary storage, which automatically deletes the data after a specified retention period.
C) Utilize network policies to restrict access to the stage based on IP address or network identifier. Only authorized IP addresses should be able to interact with the stage.
D) Encrypt the data client-side before uploading it to the stage and decrypt it after loading it into Snowflake. This provides an additional layer of security.
E) Apply masking policies to the columns in the tables that are loaded from the stage. This ensures sensitive data is masked before it reaches the target tables.

4. You are designing a complex data pipeline in Snowflake that involves multiple interdependent Tasks. Several of these Tasks need to access sensitive customer data, and you want to ensure that the least privilege principle is followed. How should you configure the Tasks and their associated roles to minimize the risk of unauthorized data access while maintaining the functionality of the pipeline? (Select TWO)

A) Create separate, specific roles for each Task or group of related Tasks. Grant each role only the minimum necessary privileges to access the specific tables and functions required by that Task. Assign each Task to the appropriate role using the 'EXECUTE AS OWNER clause.o
B) Use stored procedures executed with 'EXECUTE AS CALLER to encapsulate the sensitive data access logic. The stored procedure owner (who should have appropriate privileges) grants execute privilege to the Task's role, but the Task itself does not directly interact with the sensitive data.
C) Grant the role to the user that owns all the Tasks. This ensures that all Tasks have the necessary privileges to access any data within the Snowflake account.
D) Grant 'SELECT privilege on all tables containing sensitive data to the 'PUBLIC' role. Tasks will inherit these privileges and can access the data without explicit role assignments.
E) Create a single role with broad data access privileges and grant this role to all Tasks. This simplifies role management and ensures that no Task encounters permission errors during execution.

5. A daily process loads data into a Snowflake table named 'TRANSACTIONS using a COPY INTO statement. The table is clustered on 'TRANSACTION DATE'. Over time, you observe a significant degradation in query performance when querying data within specific date ranges. Analyzing the 'SYSTEM$CLUSTERING INFORMATION' function output for the 'TRANSACTIONS' table reveals a low 'effective clustering_ratio' and a high 'average_overlaps'. Which combination of actions below would BEST address the performance degradation and improve query efficiency?

A) Implement a data maintenance schedule that regularly reclusters the table using 'ALTER TABLE TRANSACTIONS RECLUSTER;' during off-peak hours and monitor the 'SYSTEM$CLUSTERING INFORMATION' function periodically.
B) Drop the existing clustering key on 'TRANSACTION_DATE, then recreate it with a different clustering key such as 'HASH(TRANSACTION_ID)'.
C) Recluster the table using 'ALTER TABLE TRANSACTIONS RECLUSTER$ and adjust the virtual warehouse size to maximize resource allocation during the recluster operation.
D) Create a new table with the desired clustering and load data using 'CREATE TABLE AS SELECT statement.
E) Drop the current clustered table and create a new table with partition by clauses

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