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

1. You are developing a data pipeline in Snowflake that processes sensitive customer data'. You need to implement robust data governance controls, including column-level security and data masking. Which of the following combinations of Snowflake features, when used together, provides the MOST comprehensive solution for achieving this?

A) Dynamic tables and masking policies.
B) Row-level security policies and data masking policies.
C) Object tagging, column-level security policies (using views), and masking policies.
D) Data masking policies and network policies.
E) Row access policies and data masking policies on base tables, supplemented with object tagging and column-level security policies on views that grant limited access to specific user roles.

2. A financial services company, 'Acme Finance', wants to share aggregated, anonymized transaction data with a research firm, 'Data Insights', through a Snowflake Data Clean Room. Acme Finance needs to ensure that Data Insights can only analyze the data using pre- defined aggregate functions and cannot access the raw, underlying transactional details. Acme Finance has already created a secure view to share the aggregated data'. Which of the following steps are necessary to grant Data Insights access to the data securely while enforcing the required restrictions?

A) Grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
B) Create an external function that Data Insights can call to execute pre-approved aggregate functions on the underlying data. Grant USAGE on the function to Data Insights' role and create a secure view that uses that function.
C) Create a row access policy that restricts the rows returned based on the role used by Data Insights. Then, grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
D) Create a masking policy that only allows aggregate functions to be executed by Data Insights' role and apply it to the relevant columns in the underlying table. Then, grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
E) Create a share object and grant USAGE privilege on the database containing the secure view to the share. Then, grant SELECT privilege on the secure view to the share. Finally, share the share with Data Insights' Snowflake account using their account identifier.

3. You are tasked with building a data pipeline to process image metadata stored in JSON format from a series of URLs. The JSON structure contains fields such as 'image_url', 'resolution', 'camera_model', and 'location' (latitude and longitude). Your goal is to create a Snowflake table that stores this metadata along with a thumbnail of each image. Given the constraints that you want to avoid downloading and storing the images directly in Snowflake, and that Snowflake's native functions for image processing are limited, which of the following approaches would be most efficient and scalable?

A) Create a Snowflake stored procedure that iterates through each URL, downloads the JSON metadata using 'SYSTEM$URL_GET, extracts the image URL from the metadata, downloads the image using 'SYSTEM$URL_GET , generates a thumbnail using SQL scalar functions, and stores the metadata and thumbnail in a Snowflake table.
B) Create a Snowflake external table that points to an external stage which holds the JSON metadata files. Develop a spark process to fetch image URL, create thumbnails and store as base64 encoded strings in an external stage, create a view using the external table and generated thumbnails data
C) Store just the 'image_url' in snowflake. Develop a separate application using any programming language to pre generate the thumbnails and host those at publicly accessible URLs. Within Snowflake, create a view to generate the links for image and thumbnail using 'CONCAT.
D) Create a Python-based external function that fetches the JSON metadata and image from their respective URLs. The external function uses libraries like PIL (Pillow) to generate a thumbnail of the image and returns the metadata along with the thumbnail's Base64 encoded string within a JSON object.
E) Create a Snowflake view that selects from a table containing the metadata URLs, using 'SYSTEM$URL GET to fetch the metadata. For each image URL found in the metadata, use a JavaScript UDF to generate a thumbnail. Embed the thumbnail into a VARCHAR column as a Base64 encoded string.

4. You need to implement a data masking solution in Snowflake for a table 'CUSTOMER DATA' containing PII. The requirement is to mask the email address based on the user's role: if the user is in 'ANALYST ROLE , the email address should be partially masked (e.g., 'a @example.com'), otherwise, it should be fully masked (e.g., @ .com'). Which of the following masking policy definitions and subsequent actions will correctly implement this?

A) Create a masking policy 'email_mask' using 'REGEXP_REPLACE to replace the first part of the email with asterisks if the current role is not 'ANALYST_ROLE' , otherwise use 'LEFT and ' REGEXP_REPLACE to mask only part of the username. Apply this policy to the 'EMAIL ' column of 'CUSTOMER DATA'.
B) Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, return original value. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.
C) Create a masking policy 'email_mask' that always fully masks the email address. Grant the 'UNMASK' privilege on the 'EMAIL' column to the 'ANALYST ROLE
D) Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, fully mask using 'REGEXP REPLACE. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.
E) Create two separate masking policies, one for 'ANALYST_ROLE' and one for all other roles. Apply both policies to the 'EMAIL' column of 'CUSTOMER DATA'. Grant the 'APPLY MASKING POLICY privilege on the 'CUSTOMER DATA' table to the 'ANALYST_ROLE.

5. You are building a data pipeline in Snowflake using Snowpark Python. As part of the pipeline, you need to create a dynamic SQL query to filter records from a table named 'PRODUCT REVIEWS based on a list of product categories. The list of categories is passed to a stored procedure as a string argument, where categories are comma separated. The filtered data needs to be further processed within the stored procedure. Which of the following approaches are MOST efficient and secure ways to construct and execute this dynamic SQL query using Snowpark?

A) Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.
B) Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.
C) Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.
D) Using Snowpark's on the list of product categories after converting them into a Snowflake array, and then using 'session.sql()' to execute the query.
E) Using the Snowpark "functions.lit()' function to create literal values from the list of product categories and incorporating them into the SQL query, then use 'session.sql()' to run it.

Solutions: