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

1. You have a large dataset of JSON documents stored in AWS S3, each document representing a customer order. You want to ingest these documents into Snowflake using Snowpipe and transform the nested 'address' field into separate columns in your target table. Considering data volume, complexity, and cost efficiency, which approach is MOST suitable?

A) Create an external table on the S3 bucket and then use CREATE TABLE AS SELECT (CTAS) to transform the data.
B) Use a COPY INTO statement with a transform clause to flatten the 'address' field during ingestion.
C) Use Snowpipe with a user-defined function (UDF) written in Python to parse the JSON and flatten the 'address' field.
D) Use Snowpipe to ingest the raw JSON data into a VARIANT column, then create a view that flattens the 'address' field.
E) Pre-process the JSON documents using an external compute service (e.g., AWS Lambda) to flatten the 'address' field before ingesting into Snowflake via Snowpipe.

2. Consider the following Python Snowpark stored procedure designed to enrich customer data:

What are potential drawbacks or limitations of this approach, and what improvements can be made to enhance its scalability and maintainability? (Select all that apply)

A) The operation retrieves all rows into the client's memory, which can be inefficient for large datasets. Consider using 'write' operation to store results to table.
B) Defining a UDF within the stored procedure creates unnecessary overhead as the UDF is recompiled every time the stored procedure runs. It's more efficient to define the UDF separately and call it from the procedure.
C) The code is perfectly fine and doesn't require any improvements, as it effectively enriches customer data using a UDF and Snowpark DataFrame operations.
D) Fetching data from an external API inside a UDF can introduce performance bottlenecks and potential network issues. Snowflake recommends minimizing external dependencies within UDFs.
E) Hardcoding the SQL query within the stored procedure makes it inflexible. Using Snowpark DataFrame operations instead would improve maintainability.

3. A company stores raw clickstream data in AWS S3. They need to query this data occasionally (less than once per day) for ad-hoc analysis and auditing purposes without ingesting it into Snowflake. Which of the following approaches is MOST suitable and cost- effective, and which considerations regarding data freshness are crucial?

A) Create an external table pointing directly to the S3 bucket. The data freshness depends on manually refreshing the metadata using 'ALTER EXTERNAL TABLE ... REFRESH'. Automate this refresh with a scheduled task.
B) Load the data into a Snowflake table using COPY INTO and schedule a daily task to refresh the table. Data freshness is guaranteed by the scheduled task.
C) Create an external table pointing directly to the S3 bucket. Data freshness is automatically maintained by Snowflake whenever the external table is queried. No additional configuration is required.
D) Create an internal stage in Snowflake and regularly copy the data from S3 into the internal stage using a Snowpipe. Query the data in the internal stage. Data freshness depends on the Snowpipe configuration.
E) Create an external table pointing directly to the S3 bucket. The data freshness depends on the automatic refresh enabled using the object. Ensure a metadata notification service (e.g., SQS) is configured to trigger the metadata refresh whenever new files are added to S3.

4. You are tasked with optimizing a continuous data pipeline that loads data from an external stage into a Snowflake table using streams.
The pipeline is experiencing significant latency during peak hours. The stream is defined on a very large table with frequent updates and deletes. Which of the following strategies would be MOST effective in reducing the latency of the data pipeline, considering stream performance and cost implications?

A) Increase the size of the virtual warehouse used for loading data. This will provide more compute resources for processing the stream.
B) Implement a more aggressive pruning strategy on the base table to reduce the amount of data that the stream needs to track.
C) Implement a materialized view on top of the stream to pre-aggregate the data.
D) Reduce the RETENTION TIME of the stream. This will limit the amount of historical data tracked and improve performance.
E) Create multiple streams on the same base table, each filtering for specific types of changes (e.g., INSERT, UPDATE, DELETE).

5. Your company utilizes Snowflake Streams and Tasks for continuous data ingestion and transformation. A critical task, 'TRANSFORM DATA', consumes data from a stream 'RAW DATA STREAW on table 'RAW DATA' and loads it into a reporting table 'REPORTING TABLE. You observe that 'TRANSFORM DATA is failing intermittently with a 'Stream is stale' error. What steps can you take to diagnose and resolve this issue? Choose all that apply.

A) Ensure that the ' TRANSFORM DATA' task is consuming the stream data frequently enough to prevent the stream from becoming stale.
B) Use the "AT' or 'BEFORE clause when querying the stream to explicitly specify a point in time to consume data from.
C) Drop and recreate the stream and task to reset their states.
D) Increase the parameter at the database level to ensure Time Travel data is available for a longer period.
E) Modify the task definition to use the 'WHEN condition to prevent execution when the stream is empty.

Solutions: