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

1. You are designing a data protection strategy for a Snowflake database. You need to implement dynamic data masking on the 'CREDIT CARD' column in the 'TRANSACTIONS' table. The requirement is that users with the 'FINANCE ADMIN' role should see the full credit card number, while all other users should see only the last four digits. You have the following masking policy:

What is the next step to apply this masking policy to the 'CREDIT CARD' column?

A)

B)

C)

D)

E)

2. You have a table 'EVENTS' containing application event data with columns 'EVENT ID, 'USER ID, 'EVENT TYPE, and EVENT DETAILS (VARCHAR). The 'EVENT DETAILS column contains comma-separated key-value pairs (e.g., 'location=USA,device=mobile,os=iOS'). Your objective is to transform this structured data into a VARIANT column named EVENT JSON' in a new table 'EVENTS JSON'. The data in EVENT DETAILS has inconsistent key-value pairs across different rows. Which of the following methods are the most efficient and scalable to parse the key-value pairs in 'EVENT DETAILS' and construct the JSON objects?

A) Use only REGEXP EXTRACT ALL' with appropriate regular expressions to extract all keys and values into arrays, then use a JavaScript UDF to combine them into a JSON object.
B) Use a combination of 'SPLIT, 'REGEXP_REPLACE and 'OBJECT_CONSTRUCT within a user-defined function (UDF) to parse the string and build the JSON object.
C) Utilize to split the key-value pairs into rows, then use 'REGEXP_EXTRACT to extract the key and value. Finally, use 'OBJECT_CONSTRUCT and to construct the JSON object.
D) Use a Java UDF that iterates through the string, splitting it based on commas and equals signs, and then constructs a JSON object using a JSON library.
E) Use 'SPLIT to split the key-value pairs into an array, then use a LATERAL FLATTEN to create rows from array, then use 'SPLIT again to split each row by '='. Finally, construct the JSON using 'OBJECT CONSTRUCT.

3. You are using Snowpipe to ingest data from Azure Blob Storage into a Snowflake table. You have successfully set up the pipe and configured the event notifications. However, you notice that duplicate records are appearing in your target table. After reviewing the logs, you determine that the same file is being processed multiple times by Snowpipe. Which of the following strategies can you implement to prevent duplicate data ingestion, assuming you cannot modify the source data in Azure Blob Storage to include a unique ID or timestamp?

A) Implement idempotent logic within a Snowflake stored procedure that is triggered by a task after the data is loaded by Snowpipe. The stored procedure should identify and remove duplicate rows based on all other columns in the table.
B) Use a data masking policy with the 'MASK' function to obfuscate duplicate records based on their similarity, making them effectively invisible to downstream queries.
C) Configure the Snowpipe definition with the 'PURGE = TRUE parameter. This will ensure that each file is only processed once.
D) Create a Snowflake stream on the target table and use it to incrementally load data into a separate, deduplicated table using a merge statement with conditional logic to insert or update records based on a combination of columns.
E) Modify the Azure Event Grid subscription configuration to filter events based on file size or creation time to avoid resending events for already processed files.

4. A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

A) Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.
B) Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
C) Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.
D) Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.
E) Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.

5. A data engineer is using the Snowflake Spark connector to write data to a Snowflake table. The write operation fails consistently with the error 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: String '. ' is too long (maximum is 16777216)'. Which of the following is the most likely cause and how can it be resolved using Spark Connector?

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

Solutions: