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

1. 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) 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.
B) 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
C) 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.
D) 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.
E) Broadcast the Spark DataFrame to all executor nodes, then use a UDF to execute the 'UPDATE' statement for each row directly from Spark.

2. You are tasked with building a data pipeline using Snowpark to process sensor data from IoT devices. The data arrives in near real-time as JSON payloads, and you need to transform and load it into a Snowflake table named 'SENSOR DATA'. The transformation logic involves extracting specific fields, converting data types, and filtering out records based on a timestamp. Consider performance optimization for large data volumes. Which of the following approaches, in combination, would be MOST efficient for this scenario?

A) Creating an external table pointing to the JSON data in cloud storage and using Snowpark DataFrames to read the external table, apply transformations, and load the result into 'SENSOR DATA'.
B) Using a Snowpark Python UDF to parse JSON and perform transformations, loading the result into a temporary table, and then merging into 'SENSOR DATA'.
C) Employing Snowpipe to ingest the raw JSON data into a VARIANT column in a staging table, followed by a Snowpark DataFrame operation using 'functions.get' to extract and transform the data, and finally loading into 'SENSOR DATA'
D) Using a stored procedure written in Java to parse the JSON data and insert directly into the "SENSOR DATA' table.
E) Leveraging Snowflake's native JSON parsing functions within a SQL transformation step implemented as a Snowpark DataFrame operation, combined with a Snowpipe for initial data ingestion into a staging table.

3. A data provider wants to create a Listing in the Snowflake Marketplace. They want to ensure that consumers can only access the data in a secure and controlled manner. The provider needs to restrict data access based on specific roles within the consumer's Snowflake account and track data usage. Which of the following steps are NECESSARY to achieve these requirements?

A) Grant direct access to the underlying tables and views to the consumer's roles.
B) Implement a secure view with a 'WHERE clause that filters data based on the consumer's context and share the view in the Listing.
C) Create a Reader Account and share the data through that account, managing access directly.
D) Implement row-level security policies on the shared tables and views to filter data based on consumer roles. Share the tables and views in the Listing. Monitor usage through Snowflake's account usage views.

4. A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

A) Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.
B) Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.
C) Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
D) Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.
E) Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.

5. A healthcare provider stores patient data in Snowflake, including 'PATIENT ID', 'NAME, 'MEDICAL HISTORY , and 'INSURANCE ID. They need to comply with HIPAA regulations. As a data engineer, you need to ensure that PHI (Protected Health Information) is masked appropriately based on user roles. Which of the following steps are NECESSARY to achieve this using Snowflake's data masking features and RBAC? (Select all that apply)

A) Enforce multi-factor authentication (MFA) for all users accessing the Snowflake environment to enhance security and prevent unauthorized access to sensitive data.
B) Identify the columns containing PHI and create appropriate masking policies for each column (e.g., masking 'NAME, 'MEDICAL HISTORY, INSURANCE_ID).
C) Grant the 'OWNERSHIP privilege on the 'PATIENT table to the 'ACCOUNTADMIN' role, ensuring complete control and management of the data by the administrator.
D) Apply the created masking policies to the corresponding columns in the patient data tables, ensuring that the masking policies are designed to reveal only the necessary information based on the user's role (e.g., doctors see full medical history, nurses see limited medical history, admins see de-identified data).
E) Create custom roles representing different user groups within the organization (e.g., 'DOCTOR, 'NURSE, 'ADMIN') and grant them the necessary privileges to access the data, including 'SELECT on the tables and views containing patient data.

Solutions: