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

1. You are developing a data transformation pipeline in Snowpark Python to aggregate website traffic data'. The raw data is stored in a Snowflake table named 'website_events' , which includes columns like 'event_timestamp' , 'user_id', 'page_urr , and 'event_type'. Your goal is to calculate the number of unique users visiting each page daily and store the aggregated results in a new table named Considering performance and resource efficiency, select all the statements that are correct:

A) Applying a filter early in the pipeline to remove irrelevant 'event_type' values can significantly reduce the amount of data processed in subsequent aggregation steps.
B) Defining the schema for the table before writing the aggregated results is crucial for ensuring data type consistency and optimal storage.
C) Using followed by is an efficient approach to calculate unique users per page per day.
D) Using is the most efficient method for writing the aggregated results to Snowflake, regardless of data size.
E) Caching the 'website_eventS DataFrame using 'cache()' before performing the aggregation is always beneficial, especially if the data volume is large.

2. You are using the Snowflake Spark connector to update records in a Snowflake table based on data from a Spark DataFrame. The Snowflake table 'CUSTOMER' has columns 'CUSTOMER ID' (primary key), 'NAME, and 'ADDRESS'. You have a Spark DataFrame with updated 'NAME and 'ADDRESS' values for some customers. To optimize performance and minimize data transfer, which of the following strategies can you combine with a temporary staging table to perform an efficient update?

A) Write the Spark DataFrame to a temporary table in Snowflake. Then, execute an 'UPDATE statement in Snowflake joining the temporary table with the 'CUSTOMER table using the 'CUSTOMER_ID to update the 'NAME and 'ADDRESS' columns. Finally, drop the temporary table.
B) Iterate through each row in the Spark DataFrame and execute an individual 'UPDATE statement against the 'CUSTOMER table in Snowflake. Use the 'CUSTOMER_ID in the 'WHERE clause.
C) Broadcast the Spark DataFrame to all executor nodes, then use a UDF to execute the 'UPDATE' statement for each row directly from Spark.
D) Write the Spark DataFrame to a temporary table in Snowflake using MERGE. Use the WHEN MATCHED clause for Update the target table based on updates from staging table and finally drop the staging table
E) Use Spark's foreachPartition to batch update statements and execute on each partition. This will help with efficient data transfer and avoid single row based updates.

3. You are developing a data pipeline to ingest customer feedback data from a third-party service using the Snowflake REST API. This service imposes rate limits, and exceeding them results in temporary blocking. To handle this, you implement exponential backoff with jitter. Which of the following code snippets BEST demonstrates how to correctly implement exponential backoff with jitter when calling the Snowflake REST API in Python, assuming data)' is a function that makes the API call and raises an exception on rate limiting?

A)

B)

C)

D)

E)

4. You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

A) Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.
B) Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.
C) Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
D) Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
E) Set 'snowflake.ingest.file.name' to an existing file in a stage.

5. You have a VARIANT column named 'raw_data' in a Snowflake table 'eventS , containing nested JSON data'. You need to extract specific fields Cevent_id', 'timestamp' , and 'user.user_id') and load them into a relational table 'structured_events' with columns 'event_id' , 'timestamp' , and 'user_id', respectively. However, some entries may be missing the 'user' object. Which of the following SQL statements will achieve this while handling missing 'user' objects gracefully and ensuring data integrity, and also efficiently handle potentially large JSON payloads?

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

Solutions: