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

1. You are working with a very large Snowflake table named 'CUSTOMER TRANSACTIONS which is clustered on 'CUSTOMER ID and 'TRANSACTION DATE. After noticing performance degradation on queries that filter by 'TRANSACTION AMOUNT and 'REGION' , you decide to explore alternative clustering strategies. Which of the following actions, when performed individually, will LEAST likely improve query performance specifically for queries filtering by 'TRANSACTION AMOUNT and 'REGION', assuming you can only have one clustering key?

A) Creating a search optimization on 'TRANSACTION_AMOUNT' and 'REGION' columns.
B) Adding ' TRANSACTION_AMOUNT and 'REGIO!V to the existing clustering key while retaining 'CUSTOMER_ID and 'TRANSACTION_DATE
C) Creating a new table clustered on 'TRANSACTION_AMOUNT and 'REGION', and migrating the data.
D) Dropping the existing clustering key and clustering on 'TRANSACTION_AMOUNT' and 'REGION'.
E) Creating a materialized view that pre-aggregates data by 'TRANSACTION_AMOUNT and 'REGION'.

2. A data engineering team is implementing column-level security on a Snowflake table named 'CUSTOMER DATA containing sensitive PII. They want to mask the 'EMAIL' column for users in the 'ANALYST role but allow users in the 'DATA SCIENTIST role to view the unmasked email addresses. The 'ANALYST role already has SELECT privileges on the table. Which of the following steps are necessary to achieve this using a masking policy?

A) Create a dedicated view on 'CUSTOMER DATA' for analysts with the 'EMAIL' column masked using a CASE statement within the view's SELECT statement. Grant SELECT privilege to the ANALYST role on the view only.
B) Create a masking policy with a CASE statement that checks the CURRENT ROLE() function to see if it's 'ANALYST'. If true, mask the email; otherwise, return the original email.
C) Create a masking policy that uses the CURRENT_USER() function to check if the current user belongs to the 'ANALYST' role.
D) Create a masking policy that uses the IS_ROLE_IN_SESSION('ANALYST') function to return a masked value if the analyst role is active in current session and the original value otherwise.
E) Create a masking policy that uses the CURRENT ROLE() function to return a masked value if the current role is 'ANALYST and the original value otherwise.

3. You are loading data from an S3 bucket into a Snowflake table using the COPY INTO command. The source data contains dates in various formats (e.g., 'YYYY-MM-DD', 'MM/DD/YYYY', 'DD-Mon-YYYY'). You want to ensure that all dates are loaded correctly and consistently into a DATE column in Snowflake. Which of the following COPY INTO options and commands is the MOST appropriate to handle this?

A) Use the 'VALIDATE(O)' command before the COPY INTO command to identify files with invalid date formats and then process them separately.
B) Utilize the 'DATE' function with explicit format strings inside a Snowpipe transformation pipeline. This involves pattern matching using 'CASE WHEN' statements to identify date formats before converting to the DATE data type.
C) Use the 'STRTOK TO DATE function within a SELECT statement in a Snowpipe transformation to dynamically parse the dates based on different patterns.
D) Use the 'ON_ERROR = 'SKIP FILE" option to skip files with invalid date formats.
E) Use the 'DATE FORMAT option in the COPY INTO command with a single format string that covers all possible date formats.

4. A Snowflake table 'CUSTOMER ORDERS is clustered by 'ORDER DATE. You have observed the clustering depth increasing over time, impacting query performance. To improve performance, you decide to recluster the table. However, you need to minimize the impact on concurrent DML operations and cost. Which of the following strategies would be MOST effective in managing this reclustering process?

A) Create a new table clustered by 'ORDER_DATE, copy data in parallel, and then swap tables.
B) Implement a continuous reclustering process using Snowpipe to automatically recluster new data as it arrives.
C) Use 'CREATE OR REPLACE TABLE with SELECT FROM CUSTOMER ORDERS to rebuild the table with optimized clustering.
D) Leverage Snowflake's automatic reclustering feature, monitor its performance, and adjust warehouse size as needed.
E) Recluster the entire table in a single transaction during off-peak hours.

5. You are developing a JavaScript stored procedure in Snowflake using Snowpark to perform a complex data transformation. This transformation involves multiple steps: filtering, joining with another table, and aggregating data'. You need to ensure that the stored procedure is resilient to failures and can be easily debugged. Which of the following practices would contribute to the robustness and debuggability of your stored procedure? (Select all that apply)

A) Passing the 'snowflake' binding as an argument to each modular function to facilitate logging and SQL execution within those functions.
B) Relying solely on try-catch blocks within the stored procedure to handle all potential exceptions.
C) Breaking down the complex transformation into smaller, modular functions within the stored procedure and testing each function independently.
D) Using Snowpark's logging capabilities to record intermediate results and error messages at various stages of the transformation.
E) Directly manipulating the Snowflake metadata (e.g., table schemas) within the stored procedure for dynamic schema evolution.

Solutions: