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

1. You are tasked with designing a solution to ingest a continuous stream of unstructured log data from various sources into Snowflake. The log data includes text, JSON, and XML formats. The goal is to efficiently store the data, allow for flexible querying, and minimize storage costs. Which of the following approaches would BEST address these requirements? (Select TWO)

A) Ingest all log data into a single VARIANT column in a Snowflake table.
B) Ingest all data 'as is' into a raw staging table. Then create a Task that use Python UDF to parse data and save it to different tables as required
C) Pre-process the log data to convert all formats into a standardized JSON format before ingestion.
D) Use Snowflake's external functions to parse the log data during query execution.
E) Create separate tables for each log data format (text, JSON, XML).

2.

A) The Snowflake external function is not correctly parsing the JSON response from the Lambda function. Implement a wrapper function in Snowflake to parse the JSON and extract the discount value before returning it.
B) The Lambda function is returning a string instead of a number. Modify the Lambda function to return the discount as a number (e.g., 'discount = 0.15' instead of 'discount = '0.15")
C) The 'RETURNS NULL ON NULL INPUT clause in the external function definition is causing the function to return NULL even when valid inputs are provided. Remove this clause.
D) The data types in the Lambda function and Snowflake function definition do not match. Specifically, the Lambda function expects strings while Snowflake is sending numbers and vice versa. Modify the Lambda function to handle numeric inputs and ensure the Snowflake function definition aligns with the expected output data type (FLOAT).
E) The Lambda function returns the discount within a nested JSON structure Tdata': [[discount]]}'. The Snowflake function is not designed to handle this. The lambda function should return '{'data':

3. A data engineering team is tasked with optimizing a complex query that joins three tables: 'ORDERS' , 'CUSTOMERS' , and 'PRODUCTS. The 'ORDERS' table contains millions of records and is frequently joined with 'CUSTOMERS' (containing customer demographics) and 'PRODUCTS' (containing product details). The initial query uses standard JOIN syntax, but performance is slow. The query retrieves order details along with customer and product information, filtering by a specific date range in the 'ORDERS' table and a customer segment in the 'CUSTOMERS table. Which optimization strategy would be MOST effective for significantly improving query performance?

A) Increase the virtual warehouse size to X-LARGE without analyzing the query profile.
B) Apply clustering keys to the 'ORDERS table based on the date column used in the WHERE clause and clustering keys to the 'CUSTOMERS' table on the customer segment column. Also create appropriate indexes.
C) Create materialized views that pre-join the 'ORDERS', 'CUSTOMERS, and 'PRODUCTS tables and filter based on common criteria.
D) Replace the standard JOINs with LATERAL FLATTEN operations.
E) Convert the entire dataset into a single VARIANT column and query using JSON path expressions.

4. You are building a data pipeline in Snowflake that uses an external function to perform sentiment analysis on customer reviews stored in a table named 'CUSTOMER REVIEWS'. The external function 'sentiment_analyzer' is hosted on AWS Lambda and requires an API key for authentication. You want to ensure that the API key is securely passed to the Lambda function and prevent unauthorized access. Which of the following approaches represents the MOST secure and recommended method to manage the API key?

A) Store the API key in a Snowflake table with restricted access and retrieve it within the external function's logic.
B) Store the API key directly in the external function definition as a string literal within the 'AS' clause.
C) Create a Snowflake secret object to store the API key and reference it in the external function definition using the 'USING' clause and 'SYSTEM$GET SECRET function.
D) Pass the API key as a parameter to the external function each time it is called.
E) Embed the API key directly into the AWS Lambda function's environment variables, avoiding any transmission from Snowflake.

5. You're tasked with building a data pipeline using Snowpark Python to incrementally load data into a target table 'SALES SUMMARY from a source table 'RAW SALES. The pipeline needs to ensure that only new or updated records from 'RAW SALES are merged into 'SALES SUMMARY' based on a 'TRANSACTION ID'. You want to use Snowpark's 'MERGE' operation for this, but you also need to handle potential conflicts and log any rejected records to an error table 'SALES SUMMARY ERRORS'. Which of the following approaches offers the MOST robust and efficient solution for handling errors and ensuring data integrity within the MERGE statement?

A) Use the 'WHEN MATCHED THEN UPDATE' clause to update existing records and the 'WHEN NOT MATCHED THEN INSERT clause to insert new records. Implement a separate process to periodically compare 'SALES_SUMMARY with 'RAW_SALES' to identify and log any inconsistencies.
B) Employ the 'MERGE statement with 'WHEN MATCHED THEN UPDATE' and 'WHEN NOT MATCHED THEN INSERT clauses, and use a stored procedure that executes the 'MERGE statement and then conditionally inserts rejected records into the 'SALES SUMMARY ERRORS' table based on criteria defined within the stored procedure. This will use the table function on the output.
C) Incorporate an 'ELSE clause in the 'MERGE' statement to capture records that do not satisfy the update or insert conditions due to data quality issues. Use this 'ELSE clause to insert rejected records into 'SALES SUMMARY ERRORS'
D) Utilize the 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses with a 'WHERE' condition in each clause to filter out potentially problematic records. Log these filtered records to using a separate 'INSERT statement after the 'MERGE operation.
E) Use a single 'MERGE statement with 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses. Capture rejected records by leveraging the ' SYSTEM$PIPE STATUS function after the 'MERGE operation to identify rows that failed during the merge.

Solutions: