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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 1

You're designing a Snowpark data transformation pipeline that requires running a Python function on each row of a large DataFrame. The Python function is computationally intensive and needs access to external libraries. Which of the following approaches will provide the BEST combination of performance, scalability, and resource utilization within the Snowpark architecture?

A. Create a Snowpark UDTF using gudtf(output_schema=StructType([StructField('result', StringType())]), and apply it to the DataFrame using with a lateral flatten operation.
B. Define a stored procedure in Snowflake and use it to execute the Python code on each row by calling it in a loop.
C. Load the DataFrame into a Pandas DataFrame using and then apply the Python function using Pandas DataFrame operations.
D. Use 'DataFrame.foreach(lambda row: my_python_function(row))' to iterate through each row and apply the Python function.
E. Create a Snowpark UDF using input_types=[StringType()], return_type=StringType())' and apply it to the DataFrame using

Question 2

You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?

A. Option E
B. Option A
C. Option B
D. Option D
E. Option C

Question 3

A data engineer is tasked with migrating data from a large on-premise Hadoop cluster to Snowflake using Spark. The Hadoop cluster contains nested JSON dat a. To optimize performance and minimize data transformation in Spark, what is the most efficient approach to read the JSON data into a Spark DataFrame and write it directly to a Snowflake table?

A. Read the JSON data as strings and utilize Snowflake's 'PARSE JSON' function within a Spark SQL query to transform and load the data into a variant column in Snowflake.
B. Define a schema manually in Spark, then read the JSON data into a Spark DataFrame. Use the Snowflake Spark connector to write the data to Snowflake, specifying the schema explicitly.
C. Use the Snowflake Spark connector with the 'inferSchema' option set to 'true' when reading the JSON data. This allows Spark to automatically infer the schema and write directly to Snowflake.
D. Read the JSON data as text files, then use Spark to parse and flatten the JSON structure before writing to Snowflake using the Snowflake JDBC connector.
E. Use the 'STORAGE_INTEGRATION' feature in Snowflake to directly access the JSON files in Hadoop (via an external stage) and load the data without using Spark at all.

Question 4

You are tasked with creating a JavaScript UDF in Snowflake to parse JSON data containing nested arrays of objects. The UDF needs to extract specific values from these nested objects and return them as a comma-separated string. Given the JSON structure below, and the requirement to extract the 'value' field from each object within the 'items' array located inside each element of the 'data' array, which of the following JavaScript UDF definitions will correctly achieve this, assuming the input JSON is passed as a string?

A. Option E
B. Option A
C. Option B
D. Option D
E. Option C

Question 5

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 NUMBER and use a transformation within the COPY INTO statement to handle missing prices: 'TRANSFORMATION = (price =
B. 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')))'.
C. 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.
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 VARCHAR. During data loading use the NULLIFEMPTY function within the COPY INTO statement: 'TRANSFORMATION=

Solutions: