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

1. You are tasked with designing a solution to load semi-structured data (JSON) from an AWS S3 bucket into a Snowflake table using Snowpipe and the REST API. The data in S3 is constantly being updated, and you need to ensure that only new or modified files are loaded into Snowflake. Which of the following steps are essential for implementing an efficient and cost-effective solution?

A) Use the 'VALIDATION MODES copy option with 'RETURN_ALL RESULTS = TRUE to validate all data being loaded into the Snowflake table.
B) Configure Snowpipe to automatically detect new files in the S3 bucket using event notifications, but manually refresh the pipe using SYSTEM $PIPE STATUS periodically to ensure that all files are processed.
C) Configure auto-ingest using SQS queue and SNOWPIPE object. No need to manually call the REST API endpoint for data loading.
D) Configure an S3 event notification to trigger a REST API call to the Snowpipe endpoint whenever a new or modified file is added to the S3 bucket. The API call should include the file name in the request.
E) Create a Snowflake external function that polls the S3 bucket every minute, checks for new files using the LIST command, and then calls the Snowpipe REST API endpoint for each new file.

2. You are tasked with implementing column-level security on the 'EMPLOYEE table to restrict access to the 'SALARY column. Only users with the 'HR ROLE' should be able to view the actual salary. All other users should see NULL. You create a masking policy as follows:

What additional steps are necessary to enforce this policy?

A) Grant the SELECT privilege on the EMPLOYEE table to the HR ROLE.
B) Apply the masking policy to the EMPLOYEE table: ALTER TABLE EMPLOYEE SET MASKING POLICY salary_mask;
C) Grant the APPLY MASKING POLICY privilege to the HR_ROLE.
D) Apply the masking policy to the SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask;
E) Grant OWNERSHIP on the masking policy to the HR ROL

3. You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

A) Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.
B) Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.
C) Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.
D) Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
E) Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.

4. Consider a scenario where you have a large dataset of sensor readings stored in a Snowflake table called 'SENSOR DATA'. You need to build an external function to perform complex calculations on these readings using a custom Python library hosted on AWS Lambda'. The calculation requires significant computational resources, and you want to optimize the data transfer between Snowflake and the Lambda function. The following SQL is provided: CREATE OR REPLACE EXTERNAL FUNCTION ARRAY) RETURNS ARRAY VOLATILE MAX BATCH ROWS = 2000 RETURNS NULL ON NULL INPUT API INTEGRATION = aws_lambda_integration AS 'arn:aws:lambda:us-east-1:123456789012:function:sensorProcessor'; Which of the following options would further optimize the performance and reduce data transfer costs, assuming the underlying Lambda function is correctly configured and functional?

A) Reduce the number of columns passed to the external function by performing pre-aggregation or filtering on the data within Snowflake before calling the function.
B) Increase the 'MAX BATCH ROWS' parameter to the maximum allowed value to send larger batches of data to the external function. Ensure Lambda function memory is increased appropriately.
C) Compress the data before sending it to the external function and decompress it within the Lambda function. Update the Lambda function to compress the array of results before sending it back to Snowflake and use Snowflake+s functions to decompress it.
D) Convert the input data to a binary format (e.g., using 'TO_BINARY and FROM_BINARY' functions in Snowflake) before sending it to the Lambda function, and decode it in Lambda to reduce the size of the data being transmitted.
E) Rewrite the custom Python library in Java and create a Snowflake User-Defined Function (UDF) instead of using an external function.

5. A data engineer observes that a daily data transformation pipeline in Snowflake, which processes data from external stage 's3://my- bucket/raw_dataP , is consistently taking longer to complete. Upon investigation, the engineer finds that the COPY INTO statement is the bottleneck. The COPY INTO statement is as follows:

Which of the following could be the root cause of the performance degradation and how would you address them? Select two options.

A) The virtual warehouse used for the COPY INTO operation is undersized. Increase the virtual warehouse size to improve performance.
B) Snowflake automatically optimizes COPY INTO operations. No specific action is needed.
C) The PATTERN '. .csv' is inefficient. Refine the PATTERN to be more specific, targeting only the necessary files, potentially using date-based partitioning. If date partitioning is used, leverage partition pruning by including the appropriate date criteria in the COPY INTO statement.
D) The external stage contains a large number of small files. Snowflake's COPY INTO statement performs best with fewer, larger files. Consolidate the small files into larger files before loading.
E) The 'ON_ERROR = 'CONTINUE" option is causing the COPY INTO statement to perform additional error handling, slowing down the process. Remove the 'ON ERROR clause to improve performance.

Solutions: