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

1. 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) 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.
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) Grant direct access to the underlying tables and views to the consumer's roles.

2. A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?

A) Periodically clone the ' SALES DATA' table to a separate historical table.
B) Increase the parameter for the 'SALES_DATX table.
C) Decrease the parameter for the 'SALES_DATA' table.
D) Create a materialized view that pre-computes the results of common time travel queries.
E) Implement data clustering on a column that is frequently used in time travel query filters.

3. A healthcare provider wants to share patient data with a research organization, but must ensure that researchers only have access to records from a specific region ('REGION A') and only see anonymized data'. You have a 'patients' table with columns 'patient_id' , 'region', 'dob', 'medical history', and 'ssn'. Which of the following steps would be MOST effective and secure for implementing row- level filtering and data masking for this data sharing scenario, minimizing administrative overhead and maximizing query performance?

A) Create a policy tag on the 'region' column restricting access to 'REGION_A' and apply masking policies directly on the 'patients' table. Grant access to the patientS table to the research organization's role.
B) Create a VIEW on the 'patients' table with a WHERE clause filtering by 'region = and use masking policies to redact sensitive fields. Grant access to the view to the research organization's role.
C) Create a stored procedure that filters the 'patients' table based on the region and applies masking policies. The stored procedure writes the filtered and masked data to a new table. Grant access to the new table to the research organization's role.
D) Create a secure VIEW on the 'patients' table with a WHERE clause filtering by 'region = 'REGION_A" and use masking policies to redact sensitive fields. Grant access to the secure view to the research organization's role.
E) Create a standard VIEW on the 'patients' table with a WHERE clause filtering by 'region = 'REGION_A". Create a separate table containing only the anonymized data for 'REGION_A' and grant access to this separate table to the research organization's role.

4. A financial services company is implementing Snowflake. They have a table 'CUSTOMER DATA' containing sensitive information like 'CREDIT CARD NUMBER, 'SSN', and 'ADDRESS'. They need to ensure that: 1) Analysts can only see the last four digits of the 'CREDIT CARD NUMBER. 2) Data scientists require full access to the 'ADDRESS' but should not see the 'SSN'. 3) A dedicated compliance role should be able to view all data in its original format for auditing purposes. Which of the following is the MOST efficient and secure approach to implement this using Snowflake's data masking and RBAC?

A) Create masking policies on the 'SSN' , and 'ADDRESS' columns. Use conditional masking expressions based on the CURRENT ROLE() function to determine what data to show to each role (analysts, data scientists, compliance).
B) Use data encryption for the entire 'CUSTOMER_DATA table and provide decryption keys to specific roles based on their access requirements. Provide the compliance role with the master key.
C) Create separate views for analysts and data scientists, applying masking policies within the views, and grant access to these views based on their respective roles. Additionally, grant the compliance role direct access to the base table.
D) Replicate the CUSTOMER_DATA table three times, once for each user group (Analysts, Data Scientist and Compliance). Mask sensitive information by altering the data with the respective masking function.
E) Create dynamic data masking policies on each sensitive column in the 'CUSTOMER_DATA table, associating these policies with specific roles using Snowflake's tag-based masking. Grant roles only the privileges needed to select the columns based on their requirements.

5. You have a table named 'EVENT LOGS with columns including 'EVENT ID, 'EVENT TIMESTAMP', 'USER ID, 'EVENT_TYPE, and 'EVENT DATA (which stores JSON data). Users frequently query the table filtering by specific key-value pairs within the 'EVENT DATA column. Which of the following approaches will BEST improve query performance when filtering on values inside the JSON column, considering the use of search optimization?

A) Enable search optimization directly on the 'EVENT DATA' column.
B) Increase the warehouse size.
C) Create a materialized view that extracts the key-value pairs from the ' EVENT_DATX column and enable search optimization on the materialized view's columns.
D) Extract the frequently queried key-value pairs from the 'EVENT_DATR JSON into separate virtual columns and enable search optimization on these virtual columns.
E) Convert the ' EVENT_DATX column to a VARCHAR column and enable search optimization on it.

Solutions: