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

1. You are building a data pipeline in Snowflake using Snowpark Python. As part of the pipeline, you need to create a dynamic SQL query to filter records from a table named 'PRODUCT REVIEWS based on a list of product categories. The list of categories is passed to a stored procedure as a string argument, where categories are comma separated. The filtered data needs to be further processed within the stored procedure. Which of the following approaches are MOST efficient and secure ways to construct and execute this dynamic SQL query using Snowpark?

A) Using Snowpark's on the list of product categories after converting them into a Snowflake array, and then using 'session.sql()' to execute the query.
B) Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.
C) Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.
D) Using the Snowpark "functions.lit()' function to create literal values from the list of product categories and incorporating them into the SQL query, then use 'session.sql()' to run it.
E) Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.

2. Consider a scenario where you have a Snowflake external table 'ext_logs' pointing to log files in an S3 bucket. The log files are continuously being updated, and new files are added frequently. You want to ensure that your external table always reflects the latest data available in S3. Which of the following actions and configurations are required or recommended to keep the external table synchronized with the underlying data source? (Select all that apply)

A) Periodically execute the 'ALTER EXTERNAL TABLE ext_logs REFRESH' command to update the metadata about the files in S3.
B) Create a Snowpipe that continuously loads data from the S3 bucket into a Snowflake table instead of using an external table.
C) Enable auto-refresh on the external table using the 'AUTO_REFRESH = TRUE parameter during creation.
D) Configure an event notification service (e.g., AWS SQS) to trigger an external table refresh whenever new files are added to S3.
E) Implement a stored procedure that periodically executes a query on the external table to force a metadata refresh.

3. You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

A) Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.
B) Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
C) Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
D) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
E) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.

4. You are designing a data pipeline that requires applying a complex scoring algorithm to customer data in Snowflake. This algorithm involves multiple steps, including feature engineering, model loading, and prediction. You want to encapsulate this logic within a reusable component and apply it to incoming data streams efficiently. Which of the following approaches is most suitable and scalable for implementing this scoring logic as a UDF/UDTF, considering real-time data processing and low latency requirements?

A) A JavaScript UDF that uses basic JavaScript functions to perform the entire scoring algorithm without external dependencies.
B) A SQL UDF containing a series of nested CASE statements to implement the entire scoring algorithm.
C) A Python UDF that loads a pre-trained machine learning model (e.g., using scikit-learn) and performs predictions on the input data.
D) A Java UDTF that leverages a custom Java library for feature engineering and model prediction, deployed as a JAR file to Snowflake's internal stage.
E) A Python UDTF using Snowpark, leveraging external libraries like 'torch' for accelerated calculations and ML model inference by GPU.

5. A Snowflake data warehouse is experiencing performance degradation. A critical reporting dashboard, which relies on a complex SQL query, is taking significantly longer to execute. You've examined the query profile and identified that a user-defined function (UDF) called 'PARSE ADDRESS, written in Python, is consuming a large portion of the execution time. The 'PARSE ADDRESS UDF is used to parse address strings into individual components (street, city, state, zip). What are the most effective strategies to improve the performance of this query, assuming it's difficult to rewrite the query without the UDF?

A) convert the Python UDF 'PARSE_ADDRESS' into a Java UDE
B) Create a materialized view that pre-calculates the output of the 'PARSE ADDRESS UDF for frequently queried address values and joins with the original table.
C) Utilize external functions with a more performant compute service (e.g. AWS Lambda) if the environment allows.
D) Rewrite the Python UDF 'PARSE ADDRESS in SQL using Snowflake's built-in string functions.
E) Increase the virtual warehouse size to compensate for the UDF's overhead.

Solutions: