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

1. You are building a data pipeline that involves ingesting data from AWS S3 into Snowflake using Snowpipe. The data arrives in small files frequently, and you are experiencing performance issues with delayed data availability. You need to optimize the pipeline for near real-time ingestion. Which combination of strategies will MOST effectively address this scenario?

A) Enable auto-ingest on the Snowpipe and increase the frequency of S3 event notifications to Snowflake. Combine this with clustering the target table on a relevant column to optimize query performance after loading.
B) Increase the warehouse size used for the Snowpipe load process and adjust the 'MAX FILE SIZE' parameter on the pipe definition to match the size of the incoming files.
C) Manually refresh the Snowpipe using the 'ALTER PIPE ... REFRESH' command every few minutes to force ingestion of new data.
D) Configure S3 event notifications to trigger Snowpipe only when a sufficient number of files have arrived in the S3 bucket, using a serverless function (like AWS Lambda) to manage the file count threshold.
E) Implement a micro-batching process using a third-party tool (like Apache Spark) to aggregate the small files into larger batches before loading them into S3, then configure Snowpipe to ingest the larger files.

2. You have implemented a row access policy on a 'products' table to restrict access based on the user's group. The policy uses a mapping table 'user_groups' to determine which products a user is allowed to see. After implementing the policy, users are reporting significant performance degradation when querying the 'products' table. What are the MOST likely causes of this performance issue, and what steps can you take to mitigate them? Select all that apply.

A) The 'user_groups' table is not properly indexed, causing slow lookups during policy evaluation. Create an index on the 'username' and 'group' columns of the 'user_groups' table.
B) The users do not have sufficient privileges to access the 'user_groups' table. Grant the necessary SELECT privileges to the users on the 'user_groupS table.
C) The row access policy is causing full table scans on the 'products' table. Review the query patterns and consider adding clustering keys to the 'products' table to improve data access patterns.
D) The row access policy is interfering with Snowflake's data pruning capabilities. Ensure that the policy expression can be evaluated efficiently by Snowflake's query optimizer by using the 'USING' clause of the ROW ACCESS POLICY.
E) The row access policy is overly complex and contains computationally expensive functions. Simplify the policy logic and avoid using UDFs or complex subqueries within the policy definition.

3. You need to unload data from a Snowflake table named 'CUSTOMER DATA to an AWS S3 bucket The data should be unloaded in Parquet format, partitioned by the 'CUSTOMER REGION' column, and automatically compressed with GZIP. Furthermore, you only want to unload customers whose 'REGISTRATION DATE is after '2023-01-01'. Which of the following 'COPY INTO' statement correctly achieves this?

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

4. You are designing a data pipeline in Snowflake to process IoT sensor data'. The data arrives in JSON format, and you need to extract specific nested fields using a Snowpark UDF for performance reasons. Which of the following statements are true regarding best practices and limitations when working with complex JSON data and Snowpark UDFs (Python or Scala)? (Select all that apply)

A) The maximum size of the JSON document that can be processed by a Snowpark UDF is directly limited by the maximum size of the UDF code itself (typically a few MB), requiring chunking strategies for large JSON payloads.
B) For highly complex JSON structures, consider using a Scala UDF with a robust JSON parsing library like Jackson or Gson for potentially better performance and control over error handling compared to Python UDFs.
C) When working with Snowpark Python UDFs, it's recommended to use the 'json' module in Python to parse the JSON data within the UDF, as it's optimized for Snowflake's internal JSON representation.
D) Ensure the UDF is idempotent, meaning it produces the same output for the same input, as Snowflake might execute UDFs multiple times for optimization purposes.
E) Leverage Snowflake's built-in 'PARSE_JSON' function and 'GET_PATH' function outside of the UDF as much as possible before passing the data to the UDF to reduce the complexity within the UDF itself.

5. A data engineer is using Snowpark Scala to create a UDF that calculates the distance between two geographical coordinates (latitude and longitude) using the Haversine formula'. The function should accept four 'Double' values (latl, lonl , lat2, lon2) and return the distance in kilometers as a 'Double'. The UDF must be named 'haversine distance'. What is the correct Scala code to define and register this UDF with Snowflake, including the import statements required for using Snowpark functions?

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

Solutions: