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

1. You are using Snowpipe with an external function to transform data as it is loaded into Snowflake. The Snowpipe is configured to load data from AWS SQS and S3. You observe that some messages are not being processed by the external function, and the data is not appearing in the target table. You have verified that the Snowpipe is enabled and the SQS queue is receiving notifications. Analyze the following potential causes and select all that apply:

A) The data being loaded into Snowflake does not conform to the expected format for the external function. Validate the structure and content of the data before loading it into Snowflake.
B) The IAM role associated with the Snowflake stage does not have permission to invoke the external function. Verify that the role has the necessary permissions in AWS IAM.
C) The AWS Lambda function (or other external function) does not have sufficient memory or resources to process the incoming data volume, leading to function invocations being throttled and messages remaining unprocessed.
D) The Snowpipe configuration is missing a setting that allows the external function to access the data files in S3. Ensure that the storage integration is configured to allow access to the S3 location.
E) The external function is experiencing timeouts or errors, causing it to reject some records. Review the external function logs and increase the timeout settings if necessary.

2. You are tasked with creating a JavaScript stored procedure in Snowflake to perform a complex data masking operation on sensitive data within a table. The masking logic involves applying different masking rules based on the data type and the column name. Which approach would be the MOST secure and maintainable for storing and managing these masking rules? Assume performance is not your primary concern but code reuse and maintainability is the most important thing.

A) Using external stages and pulling the masking rules from a configuration file during stored procedure execution.
B) Storing the masking rules in a separate Snowflake table and querying them within the stored procedure.
C) Storing masking logic in Javascript UDFs and calling these UDFs dynamically within the stored procedure based on column names and datatype
D) Hardcoding the masking rules directly within the JavaScript stored procedure.
E) Defining the masking rules as JSON objects within the stored procedure code.

3. Consider the following Snowflake SQL API call to execute a stored procedure:

A) Set the parameter to and retrieve the result set directly from the API response.
B) Include the stored procedure's fully qualified name (database.schema.procedure_name) in the 'statement' parameter.
C) Set the 'warehouse' parameter in the SQL API request to ensure the stored procedure uses a specific warehouse size.
D) Use the parameter to specify which external functions are allowed to be called by the procedure.
E) The stored procedure should handle the error handling for network disruptions and automatically retry.

4. You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

A) Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.
B) Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
C) Increase the number of Snowflake virtual warehouses to handle the increased load.
D) Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.
E) Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.

5. You are working with a Snowpark DataFrame named 'customer data' that contains sensitive Personally Identifiable Information (PII). The DataFrame has columns such as 'customer id', 'name', 'email' , and 'phone number'. Your task is to create a new DataFrame that only contains 'customer id' and a hash of the 'email' address for anonymization purposes, while also filtering out any customers whose 'customer id' starts with 'TEMP'. Which of the following approaches adheres to best practices for data security and efficiency in Snowpark, using secure hashing algorithms provided by Snowflake?

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

Solutions: