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

1. You are developing a Secure UDF in Snowflake to encrypt sensitive customer data'. The UDF should only be accessible by authorized roles. Which of the following steps are essential to properly secure the UDF?

A) Using the 'SECURE keyword when creating the UDF to prevent viewing the UDF definition.
B) Using masking policies instead of Secure UDFs is the recommended approach for data security
C) Setting the 'SECURITY INVOKER clause when creating the UDF to execute the UDF with the privileges of the caller.
D) Ensuring that the UDF is owned by a role with appropriate permissions and limiting access to this role.
E) Granting the EXECUTE privilege on the UDF only to the roles that require access.

2. A financial institution needs to tokenize sensitive customer data (credit card numbers) stored in a Snowflake table named 'CUSTOMER_DATA before it's consumed by a downstream reporting application. The institution uses an external tokenization service accessible via a REST API. Which of the following approaches is the MOST secure and scalable way to implement tokenization during data loading, minimizing exposure of the raw credit card data within Snowflake?

A) Use a Snowflake UDF (User-Defined Function) written in Java that calls the external tokenization API directly. Create a masking policy that utilizes the UDF and applies it to the credit card number column.
B) Load the raw data into a staging table, then create a Snowflake Task that executes a stored procedure. The stored procedure calls the external tokenization API using 'SYSTEM$EXTERNAL_FUNCTION_REQUEST' for each row and updates the original table with the tokenized values.
C) Utilize Snowflake's Snowpipe to ingest the data directly. Inside a COPY INTO statement, use an external function to call the tokenization service during the ingestion process to tokenize the data before it's loaded into the target table.
D) Use Snowflake's Data Sharing feature to securely share the raw data with the downstream application, instructing them to perform the tokenization within their own environment.
E) Load the raw data directly into the 'CUSTOMER DATA' table. Create a masking policy that utilizes a UDF that calls the external tokenization API directly to tokenize the credit card number values on read.

3. Your team is implementing Snowpipe Streaming to ingest data from multiple IoT devices into a Snowflake table. Each device sends data continuously, and it's crucial to minimize latency. However, you observe that some records are occasionally missing in the target table. You have verified that the devices are sending all data and the client-side application using Snowpipe Streaming is functioning correctly. Which of the following is the MOST likely cause of the missing records, and how can you address it?

A) The Sequence Token is not being managed correctly, leading to potential gaps or out-of-order delivery; ensure correct implementation of the Sequence Token mechanism in your ingestion code.
B) The Snowflake virtual warehouse is not scaled up sufficiently to handle the ingestion rate; increase the virtual warehouse size.
C) There is a delay in Snowflake's metadata refresh causing some new tables to be temporarily unavailable, retry after some time or manually refresh metadata.
D) The Snowpipe Streaming client is exceeding its buffer capacity, leading to data loss; increase the buffer size on the client-side.
E) Duplicate records are being dropped due to Snowflake's internal deduplication mechanism; configure the table to allow duplicates.

4. You have a 'WEB EVENTS' table that stores user activity on a website. It includes columns like 'USER ID, 'EVENT TYPE , EVENT TIMESTAMP, and 'PAGE URL'. You need to create a materialized view that calculates the number of distinct users visiting each page daily. You are also tasked with minimizing the impact on the underlying 'WEB EVENTS' table during materialized view refreshes, as other critical processes rely on it. Which of the following strategies would provide the MOST efficient solution, considering both performance and concurrency?

A) Create a materialized view and schedule regular, small batch refreshes to minimize lock contention and resource consumption on the 'WEB_EVENTS' table.
B) Create a standard materialized view that calculates the distinct user count per page daily directly from the 'WEB EVENTS table without any special configuration.
C) Create a task that truncates and reloads the materialized view daily. This ensures data consistency and prevents incremental refresh issues.
D) Create a materialized view and configure it to incrementally refresh, leveraging Snowflake's automatic refresh capabilities without any explicit scheduling.
E) Create a materialized view with a 'REFRESH COMPLETE strategy to ensure full data consistency after each refresh, even though it may lock the underlying table.

5. A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

Solutions: