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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called 'SALES DATA', which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the 'SALES DATA' table and identify potential improvements?

A) Use the 'SHOW TABLES command to view the clustering key defined on the table. Verify that the clustering key is appropriate for the query workload.
B) Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.
C) Examine the query profile in the Snowflake web interface to identify stages that are scanning large amounts of data. Check if these stages are benefiting from clustering.
D) Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.
E) Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.

2. You are tasked with processing streaming data in Snowflake using Snowpark Python. The raw data arrives in a DataFrame raw events' with the following schema: 'event id: string', 'event_time: timestamp', 'user id: string', and 'event data: string'. You need to perform the following data transformations: 1 . Extract a specific value from the JSON 'event_data' using the 'get' function to find the 'product_id' and create a new column named 'product id' of type STRING. 2. Filter the DataFrame to include only events where the is NOT NULL and the is within the last hour. 3. Aggregate the filtered data to count the number of events per 'product id'. Which of the following code snippets correctly performs these transformations in an efficient and performant manner?

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

3. You are tasked with creating a Snowpark Java stored procedure to calculate a complex, custom rolling average for a time series dataset. This rolling average requires access to external libraries for statistical calculations. Which of the following steps are necessary to successfully deploy and execute this stored procedure?

A) All of the above.
B) Create a stored procedure in Snowflake, specifying the fully qualified path to the JAR file in the stage, the handler class, and the return type.
C) Package the Java code and all necessary external libraries into a single JAR file.
D) Upload the JAR file to a Snowflake stage.
E) Grant the necessary privileges on the stage and the database to the role executing the stored procedure.

4. Consider the following scenario: You are ingesting JSON data from an external stage into Snowflake. The JSON data contains an array of objects, where each object represents a product with attributes like 'product id', 'name', and 'price'. However, sometimes the 'price' field is missing entirely from some product objects. You want to load this data into a Snowflake table with columns 'product_id', 'name', and 'price' (defined as NUMBER). How can you handle the missing 'price' field gracefully during the COPY INTO operation, ensuring that missing prices are represented as NULL in the Snowflake table without causing errors?

A) Define the 'price' column in the Snowflake table as VARIANT. After the data is loaded, create a view that extracts the price using the 'GET' function and converts it to NUMBER using 'TO NUMBER. Handle NULL values in the view using 'price')), NULL, TO 'price')))'.
B) Define the 'price' column in the Snowflake table as NUMBER and use the DEFAULT NULL clause. The COPY INTO statement will automatically insert NULL values for missing fields.
C) Define the 'price' column in the Snowflake table as VARCHAR. During data loading use the NULLIFEMPTY function within the COPY INTO statement: 'TRANSFORMATION=
D) Pre-process the JSON data before loading into Snowflake, adding a 'price': null field to any product object missing the price.
E) Define the 'price' column in the Snowflake table as NUMBER and use a transformation within the COPY INTO statement to handle missing prices: 'TRANSFORMATION = (price =

5. Consider the following Snowflake SQL API call to execute a stored procedure:

A) Set the parameter to and retrieve the result set directly from the API response.
B) Set the 'warehouse' parameter in the SQL API request to ensure the stored procedure uses a specific warehouse size.
C) Use the parameter to specify which external functions are allowed to be called by the procedure.
D) The stored procedure should handle the error handling for network disruptions and automatically retry.
E) Include the stored procedure's fully qualified name (database.schema.procedure_name) in the 'statement' parameter.

Solutions: