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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

A) Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
B) Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.
C) Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.
D) Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
E) Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.

2. You are designing a data recovery strategy for a critical table 'CUSTOMER DATA' in your Snowflake environment. The data in this table is highly sensitive, and regulatory requirements mandate a retention period of at least 90 days for potential audits. You need to configure the Time Travel retention period to meet these requirements. What is the maximum supported Time Travel retention period, and how would you set it at the table level?

A) The maximum retention period is 365 days. You can set it using: ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 365;'
B) The maximum retention period depends on your Snowflake edition and can be set at the account level only.
C) The maximum retention period is 90 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 90;'
D) The maximum retention period is 7 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 7;'
E) The maximum retention period is 90 days for Enterprise Edition or higher. You can set it using: 'ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 90;'

3. Consider the following Snowflake Javascript UDF designed to convert temperatures between Celsius and Fahrenheit:

Which statement regarding the UDF's security and behavior is MOST accurate?

A) The UDF is vulnerable to SQL injection because the 'unit' parameter is not validated against a predefined list of acceptable values.
B) While the ' SECURE keyword hides the UDF's source code from users without sufficient privileges, users with the ACCOUNTADMIN role can still view the source code.
C) The 'SECURE' keyword ensures that the UDF's code is completely hidden from all users, including those with the ACCOUNTADMIN role.
D) The UDF will always return a value, even if the input 'unit is invalid, due to the implicit type conversion in JavaScript.
E) The UDF is not vulnerable to SQL injection because Javascript UDFs in Snowflake are sandboxed and prevent direct SQL execution.

4. You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

A) Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
B) Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.
C) Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
D) Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
E) Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.

5. A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

A)

B)

C)

D)

E)

Solutions: