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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineering team has deployed an external function that leverages a cloud-based machine learning model. They are experiencing intermittent errors and performance degradation, and they suspect issues with the external function's batching and error handling. Which of the following steps would BEST address these issues and improve the reliability and performance of the external function? (Select TWO)

A) Remove the "RETURNS NULL ON NULL INPUT clause from the external function definition to force all inputs to be processed.
B) Decrease the timeout period for the external function to quickly fail and retry in case of errors.
C) Monitor the external function's execution metrics using Snowflake's query history and the remote service's monitoring tools to identify bottlenecks and errors.
D) Increase the 'MAX BATCH_ROWS' parameter of the external function to send larger batches of data to the remote service.
E) Implement robust error handling within the external function's code to catch exceptions and return meaningful error messages to Snowflake.

2. You are tasked with creating a resilient data pipeline using Snowpark Python. The pipeline transforms data from a raw stage to a processed stage. A key transformation involves joining two DataFrames, 'dfl' and 'df2 , based on a common column, 'id'. You want to ensure that even if 'df2 is temporarily unavailable or contains unexpected data, the pipeline continues to process 'dfl' using a default value for missing data from 'df2. Which of the following approaches provides the best balance of resilience and data integrity? Assume you have defined a default dataframe 'df default' already.

A) Use 'broadcast hint on 'df2 before performing the join to reduce the chances of join failure, assuming 'df2 is a small dataframe.
B) Perform a 'left_outer' join of 'dfl' with 'df2. If 'df2 is unavailable or returns no data, replace 'df2' with a default DataFrame Cdf_default) and proceed with the join.
C) Perform a 'left_outer' join of 'dfl' with 'df2. If the join fails, catch the exception and proceed without the join.
D) Write a custom Python UDF that attempts to retrieve the corresponding data from 'df2 based on the 'id' column. If the retrieval fails for a particular ID, return a default value.
E) Use a 'try-except' block to catch any exceptions during the join operation. If an exception occurs, use the 'fillna()' method to replace missing values with the default data value.

3. Consider a scenario where you need to transform data in a Snowflake table using a complex custom transformation logic best implemented in Java'. You decide to use a Snowpark Java UDF. You've packaged your Java code into a JAR file and uploaded it to an internal stage named Which of the following steps are necessary and correctly ordered to deploy and use this Java UDF within Snowflake?

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

4. You have a Snowflake table, 'CUSTOMER ORDERS', with columns like 'CUSTOMER ID', 'ORDER DATE', 'ORDER AMOUNT', and 'REGION'. A Bl dashboard relies on a query that aggregates data from this table, but the query performance is consistently slow. The query frequently filters by 'ORDER DATE and groups by 'REGION'. Based on the following 'EXPLAIN' output, which combination of techniques should be considered to improve the performance the most?

A) Cluster the 'CUSTOMER ORDERS table on 'ORDER DATE' and 'REGION'.
B) Increase the virtual warehouse size to 'LARGE or 'XLARGE.
C) Create an index on the 'ORDER DATE column.
D) Create a materialized view that pre-aggregates the data by 'ORDER DATE and 'REGION'
E) Redesign the dashboard to minimize the data being displayed at once to the user.

5. You are tasked with optimizing a data pipeline that loads data from an external cloud storage location into Snowflake, transforms it, and then loads it into reporting tables. The pipeline is experiencing intermittent performance issues. You want to proactively identify and address these issues. Which of the following monitoring techniques and Snowflake features would be MOST effective for continuous monitoring and performance optimization?

A) Utilize Snowflake's System Functions to periodically query performance views (e.g., 'QUERY_HISTORY, ' and write aggregated metrics to a dedicated monitoring table. Configure a scheduled task to generate alerts based on predefined thresholds.
B) Implement custom logging and monitoring using Snowflake Scripting and User-Defined Functions (UDFs) to capture granular performance metrics at each stage of the pipeline and push notifications via external functions to a monitoring service.
C) Rely solely on Snowflake's default query history and resource monitors. These automatically track performance and usage, providing sufficient insight without additional configuration.
D) Enable Snowflake's Auto-Suspend and Auto-Resume features on the warehouse. This is the most efficient way to manage resources and optimize costs, indirectly addressing performance concerns.
E) Focus exclusively on optimizing SQL queries and data transformations. Monitoring is unnecessary since Snowflake automatically handles performance optimization.

Solutions: