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

1. You are tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.

A) Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.
B) Regularly creating full clones of the 'SALES_DATR table to a separate database.
C) Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
D) Relying solely on Snowflake's Time Travel feature with the default data retention period.
E) Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.

2. You have a table named 'ORDERS' with a column 'ORDER DETAILS' that contains JSON data'. You want to extract a specific nested value ('customer id') from this JSON data using a SQL UDE The JSON structure varies, and sometimes the 'customer id' field might be missing. You need to create a UDF that handles missing fields gracefully and returns NULL if 'customer id' is not found. Also, You are looking for a performant solution that is highly scalable. Which of the following SQL UDF definitions is most appropriate?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame 'df_products' with columns 'product id', 'category', and 'price'. You need to perform the following transformations in a single, optimized query using Snowpark Python: 1. Filter for products in the 'Electronics' or 'Clothing' categories. 2. Group the filtered data by category. 3. Calculate the average price for each category. 4. Rename the aggregated column to 'average_price'. Which of the following code snippets demonstrates the most efficient way to achieve this?

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

4. A retail company wants to store product data in a Snowflake VARIANT column. The product data is currently in a relational table called 'PRODUCTS' with columns 'PRODUCT ID', 'PRODUCT NAME, 'CATEGORY, 'PRICE, and 'DISCOUNT. They want to create a JSON structure where each product is represented as a JSON object, and the entire result set is a JSON array. Which of the following SQL statements will achieve this transformation most efficiently?

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

5. A provider account is sharing a database named 'SHARED DB' through a share named 'MY SHARE. The consumer account has created a database named 'CONSUMER DB' from the share. The provider account revokes access to a table named 'SALES DATA within 'SHARED DB'. What will happen when a user in the consumer account attempts to query 'CONSUMER DB.SHARED SCHEMA.SALES DATA'?

A) The query will fail with an error message indicating that the table does not exist or the user does not have privileges.
B) The query will be automatically re-routed to another available share containing 'SALES DATA'.
C) The query will be cached based on the initial access, so users can continue query previous result based on same SQL
D) The query will execute successfully, but the user will receive an empty result set.
E) The query will execute successfully, but only return data that existed before the access was revoked.

Solutions: