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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?

A) Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
B) Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.
C) Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
D) Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
E) Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.

2. A data engineer is tasked with processing a large dataset of customer orders using Snowpark Python. The dataset contains a column stored as a string in 'YYYY-MM-DD HH:MI:SS' format. They need to create a new DataFrame with only the orders placed in the month of January 2023. Which of the following code snippets achieves this most efficiently, considering potential data volume and query performance?

A)

B)

C)

D)

E)

3. Consider the following Snowflake UDTF definition written in Python:

Which of the following statements are TRUE regarding the deployment and usage of this UDTF?

A) The UDTF will automatically be available in all schemas across all databases in the Snowflake account.
B) The return type of the generator 'yield' must strictly adhere to the declared output schema , or errors will occur during execution.
C) The UDTF needs to be registered using 'session.udtf.register' or 'create or replace function' with the 'imports' clause referencing the Python file, and the handler' specifying the function name.
D) The library needs to be explicitly installed and configured within the UDTF's environment using a Snowpark session.
E) The UDTF can be called directly in SQL using 'SELECT FROM TABLE(process_json(VARlANT COLUMN));' without any prior registration.

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 Python UDF that loads a pre-trained machine learning model (e.g., using scikit-learn) and performs predictions on the input data.
C) A SQL UDF containing a series of nested CASE statements to implement the entire scoring algorithm.
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. You are designing a Snowflake alert system for a data pipeline that loads data into a table named 'ORDERS'. You want to trigger an alert if the number of rows loaded per hour falls below a threshold, indicating a potential issue with the data source. You need to create an alert that is triggered based on the count of rows. Consider the code snippet below and the additional requirements. Assume that the table exists and the connection is successful.

A) Create a Snowflake task that runs every hour, executes a query to count the rows loaded in the past hour and triggers an alert using 'SYSTEM$SEND_EMAIC if the count is below the threshold. No need to create a Snowflake alert.
B) Create a Snowflake Alert that executes a SQL query to count the number of rows loaded into the 'ORDERS table within the last hour. Configure the alert to trigger when the count is below the defined threshold. Use a Notification Integration to send alerts to a monitoring system.
C) You cannot create alerts based on a rolling hourly window within Snowflake. Alerts can only be based on fixed time intervals.
D) Use Snowflake's Resource Monitor feature and adjust the credit quota to trigger an alert if the credit usage exceeds the threshold for the virtual warehouse processing data pipeline, indirectly indicating that performance is degraded or data volume has changed significantly.
E) Create a Snowflake Stream on the 'ORDERS' table. Then create an Alert that triggers based on the metadata column, comparing it to the threshold value. This allows for real-time monitoring of data changes.

Solutions: