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

1. You have created a Snowflake Iceberg table that points to data in an AWS S3 bucket. After some initial data ingestion, you realize that the schema in the Iceberg table does not perfectly match the schema of the underlying Parquet files in S3. Specifically, one of the columns in the Iceberg table is defined as 'VARCHAR , while the corresponding column in the Parquet files is stored as 'INT. What will be the most likely behavior when you query this Iceberg table in Snowflake?

A) Snowflake will automatically cast the SINT' data in the Parquet files to 'VARCHAR during query execution, and the query will succeed without any errors or warnings.
B) The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.
C) The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.
D) The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
E) Snowflake will attempt to cast the data, and if a cast fails (e.g., 'INT' value is too large to fit in 'VARCHAR), the query will return an error only for those specific rows. Other rows will be processed correctly.

2. You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

A) Use Snowpipe with auto-ingest, loading directly into the table with the 'VARIANT column. Define data quality checks with pre-load data transformation.
B) Create a view with nested 'FLATTEN' functions to extract the values from the 'VARIANT column. The view serves as the source for further transformations.
C) Load the entire JSON into a 'VARIANT column and then use SQL with nested 'FLATTEN' functions to extract the desired values during query time.
D) Use a 'COPY' command with a 'TRANSFORM' clause that uses JavaScript UDFs to parse the JSON and extract the values during the load process. Load the extracted values directly into the target columns.
E) Use a stored procedure to parse the JSON data and insert values into the table row by row.

3. You are tasked with creating an external function in Snowflake that calls a REST API. The API requires a bearer token for authentication, and the function needs to handle potential network errors and API rate limiting. Which of the following code snippets demonstrates the BEST practices for defining and securing this external function, including error handling?

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

4. You have a Snowflake table called 'RAW ORDERS that contains semi-structured JSON data in a column named 'ORDER DETAILS. You need to extract specific fields from the JSON data, perform some data type conversions, and then load the transformed data into a relational table named 'CLEAN ORDERS'. Your requirements are as follows: 1. Extract the (STRING) from the JSON and store it as 'ORDER ID (NUMBER). 2. Extract the (STRING) from the JSON and store it as 'CUSTOMER ID (NUMBER). 3. Extract the 'order_date' (STRING) from the JSON and store it as 'ORDER DATE' (DATE). 4. Extract (STRING) from the JSON and store it as 'TOTAL AMOUNT' (FLOAT). Which of the following Snowpark Python code snippets correctly transforms the data and loads it into the 'CLEAN ORDERS table using a combination of Snowpark DataFrame operations and SQL? Assume that session 'sp' is already initialized.

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

5. A data engineer is tasked with creating a Listing to share a large dataset stored in Snowflake. The dataset contains sensitive Personally Identifiable Information (PII) that must be masked for certain consumer roles. The data engineer wants to use Snowflake's dynamic data masking policies within the Listing to achieve this. Which of the following approaches is the MOST secure and maintainable way to implement this requirement, assuming that the consumer roles are pre-defined and known?

A) Create multiple versions of the shared tables, each with different masking applied. The data engineer must manually manage which version each consumer can access.
B) Apply dynamic data masking policies directly to the base tables containing the PII and share these tables in the Listing. Policies should use the function to determine when to mask the data.
C) Implement an external function that masks the data based on the consumer's role and share this function in the Listing. Use this external function in a view shared through the listing.
D) Create a view that applies conditional masking using 'CASE' statements based on the function and share the view in the Listing.

Solutions: