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

1. You are developing a JavaScript stored procedure in Snowflake using Snowpark to transform data'. The procedure needs to efficiently calculate the sum of 'amount' for each 'customer_id' in a large table named 'orders'. You want to avoid transferring large amounts of data to the client and utilize Snowpark's pushdown capabilities. Which of the following JavaScript code snippets is the MOST efficient and correct way to achieve this? Assume 'snowflake' binding is available.

A)

B)

C)

D)

E)

2. You have a Snowflake table 'orders_raw' with a VARIANT column named 'order detailS that contains an array of order items represented as JSON objects. Each object has 'item id', 'quantity' , and 'price'. You need to calculate the total revenue for each order. Which SQL statement efficiently flattens the array and calculates the total revenue using LATERAL FLATTEN and appropriate casting?

A) Option B
B) Option E
C) Option D
D) Option C
E) Option A

3. You are designing a data sharing solution where the consumer account needs real-time access to a secure view that aggregates data from several tables in your provider account. The consumer should not be able to see the underlying tables. Which of the following approaches offers the MOST secure and efficient way to implement this data sharing while minimizing the risk of data leakage and performance impact on your provider account?

A) Create a standard view that joins the tables and share the view using a data share. Implement row-level security policies on the underlying tables.
B) Create a UDF that encapsulates the data aggregation logic and share the UDF's result using a data share, calling the UDF on demand.
C) Create a secure view that joins the tables and share only the secure view using a data share.
D) Create a materialized view on top of the tables, refresh it periodically, and share the materialized view.
E) Create a shared database and grant SELECT privilege on the underlying tables directly to the consumer's role.

4. You have implemented a row access policy on a 'products' table to restrict access based on the user's group. The policy uses a mapping table 'user_groups' to determine which products a user is allowed to see. After implementing the policy, users are reporting significant performance degradation when querying the 'products' table. What are the MOST likely causes of this performance issue, and what steps can you take to mitigate them? Select all that apply.

A) The row access policy is causing full table scans on the 'products' table. Review the query patterns and consider adding clustering keys to the 'products' table to improve data access patterns.
B) The row access policy is overly complex and contains computationally expensive functions. Simplify the policy logic and avoid using UDFs or complex subqueries within the policy definition.
C) The users do not have sufficient privileges to access the 'user_groups' table. Grant the necessary SELECT privileges to the users on the 'user_groupS table.
D) The 'user_groups' table is not properly indexed, causing slow lookups during policy evaluation. Create an index on the 'username' and 'group' columns of the 'user_groups' table.
E) The row access policy is interfering with Snowflake's data pruning capabilities. Ensure that the policy expression can be evaluated efficiently by Snowflake's query optimizer by using the 'USING' clause of the ROW ACCESS POLICY.

5. You need to define a UDF in Snowflake that takes a date as input and returns the next business day (Monday-Friday). If the input date is a Friday, the UDF should return the following Monday. If the input date is a Saturday or Sunday, the function should return the following Monday as well. Which of the following UDF definitions correctly implements this logic?

A) Option B
B) Option E
C) Option D
D) Option C
E) Option A

Solutions: