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

1. You are tasked with creating a development environment from a production database in Snowflake. The production database is named 'PROD DB' and contains several schemas, including 'CUSTOMER DATA' and 'PRODUCT DATA'. You want to create a clone of the 'PROD DB' database named 'DEV DB', but you only need the 'CUSTOMER DATA' schema for development purposes and all the data should be masked with a custom UDF 'MASK EMAIL' for 'email' column in 'CUSTOMER' table. The 'email' column is VARCHAR. Which of the following sequences of SOL statements would achieve this in Snowflake? Note: UDF MASK EMAIL already exists in the account.

A)

B)

C)

D)

E)

2. A data engineer is using Snowpark Python to build a data pipeline. They need to define a UDF that uses a pre-trained machine learning model stored as a file in a Snowflake stage. The UDF should receive batches of data for scoring. Which of the following is the MOST efficient way to implement this, minimizing data transfer and execution time?

A) Load the model from the stage into a DataFrame, then use 'df.mapPartitionS to apply the model to each partition.
B) Use '@vectorized' decorator from Snowpark to process each batch of data passed to the UDF and load the model inside it. Specify the appropriate data types in the decorator.
C) Create a UDF with gudf(packages=['snowflake-snowpark-python', 'scikit-learn'], input_types=[ArrayType(StringType())], return_type=FloatType(), replace=True, is_permanent=True, and load the model within the UDF's initialization using 'session.file.get' .
D) Use 'session.read.parquet' to load the model file directly into a Snowpark DataFrame and then use 'DataFrame.foreach' to process each row.
E) Create a UDF that reads the model from the stage for each row that is passed to it using 'session.file.get' inside the UDF's execution logic.

3. You are tasked with building a robust data quality monitoring system for a Snowflake data pipeline. The pipeline processes customer order data and loads it into a 'CUSTOMER ORDERS table. You need to implement checks to ensure that certain critical columns (e.g., 'ORDER ID, 'CUSTOMER ID', 'ORDER DATE, meet specific data quality requirements (e.g., not null, valid format, within acceptable range). You want to design a flexible and scalable solution that allows you to easily add, modify, and monitor data quality rules. Select the options to implement that and scale efficiently Assume there is a central Data Quality table for each metrics

A) Create a series of individual SQL scripts, each checking a specific data quality rule for a specific column, and schedule these scripts to run using Snowflake tasks.
B) Utilize Snowflake's native Data Governance features, such as data masking and row-level security, to enforce data quality rules.
C) Build a set of custom Snowflake Native Apps to monitor and report on data quality. Each App will focus on one or more critical tables or data quality checks
D) Develop a parameterized stored procedure that accepts the table name, column name, data quality rule definition, and threshold values as input parameters. This procedure then dynamically constructs and executes the SQL query to check the data quality rule.
E) Implement a Snowpark Python UDF that leverages a data quality library (e.g., Great Expectations) to define and execute data quality rules. The UDF takes a DataFrame representing the data to be checked and returns a DataFrame containing the data quality check results.

4. You have a table 'EMPLOYEE DATA' containing Personally Identifiable Information (PII), including 'salary' and 'email'. You need to implement column-level security such that: 1) The 'salary' column is only visible to users in the 'FINANCE ROLE. 2) The 'email' column is masked with a SHA256 hash for all users except those in the 'HR ROLE. You create the following masking policies:

Which of the following SQL statements correctly applies these masking policies to the 'EMPLOYEE DATA table?

A) ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY mask email;
B) CREATE OR REPLACE TAG employee_data.salary VALUE 'mask_salary'; CREATE OR REPLACE TAG employee_data.email VALUE 'mask_email';
C) ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask_salary ON COLUMN salary; ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask email ON COLUMN email;
D) ALTER TABLE EMPLOYEE_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;
E) ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY = mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY = mask email;

5. You have an external table named in Snowflake that points to a set of CSV files in an AWS S3 bucket. The CSV files have a header row, and the data is comma-separated. However, some of the files in the S3 bucket are gzipped. You need to define the external table to correctly read both compressed and uncompressed files. Which of the following SQL statements BEST achieves this?

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

Solutions: