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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake table called 'ORDERS' with a column named 'ORDER DETAILS that contains JSON data'. You need to create a UDF to extract a specific value from the JSON and return it. The JSON structure is consistent: '{'customer': {'name': 'John Doe', 'email': '[email protected]'}, 'items': [{'item id': 1, 'quantity': 2}, {'item id': 2, 'quantity': You want to extract the customer's email address. Which of the following UDF definitions is the most efficient and correct?

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

2. You're using Snowpark Python to transform data in a Snowflake table called 'employee_data' which includes columns , 'department, 'salary' , and 'performance_rating'. You need to identify the top 3 highest-paid employees within each department based on their salary, but only for departments where the average performance rating is above 4.0. Which of the following approaches using Snowpark efficiently combines window functions, filtering, and aggregations to achieve this?

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

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 will always return a value, even if the input 'unit is invalid, due to the implicit type conversion in JavaScript.
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 UDF is not vulnerable to SQL injection because Javascript UDFs in Snowflake are sandboxed and prevent direct SQL execution.
D) The 'SECURE' keyword ensures that the UDF's code is completely hidden from all users, including those with the ACCOUNTADMIN role.
E) The UDF is vulnerable to SQL injection because the 'unit' parameter is not validated against a predefined list of acceptable values.

4. You have a Snowflake table, 'CUSTOMER ORDERS', with columns like 'CUSTOMER ID', 'ORDER DATE', 'ORDER AMOUNT', and 'REGION'. A Bl dashboard relies on a query that aggregates data from this table, but the query performance is consistently slow. The query frequently filters by 'ORDER DATE and groups by 'REGION'. Based on the following 'EXPLAIN' output, which combination of techniques should be considered to improve the performance the most?

A) Create a materialized view that pre-aggregates the data by 'ORDER DATE and 'REGION'
B) Redesign the dashboard to minimize the data being displayed at once to the user.
C) Increase the virtual warehouse size to 'LARGE or 'XLARGE.
D) Cluster the 'CUSTOMER ORDERS table on 'ORDER DATE' and 'REGION'.
E) Create an index on the 'ORDER DATE column.

5. You are tasked with optimizing a data pipeline that loads data from an external cloud storage location into Snowflake, transforms it, and then loads it into reporting tables. The pipeline is experiencing intermittent performance issues. You want to proactively identify and address these issues. Which of the following monitoring techniques and Snowflake features would be MOST effective for continuous monitoring and performance optimization?

A) Utilize Snowflake's System Functions to periodically query performance views (e.g., 'QUERY_HISTORY, ' and write aggregated metrics to a dedicated monitoring table. Configure a scheduled task to generate alerts based on predefined thresholds.
B) Implement custom logging and monitoring using Snowflake Scripting and User-Defined Functions (UDFs) to capture granular performance metrics at each stage of the pipeline and push notifications via external functions to a monitoring service.
C) Enable Snowflake's Auto-Suspend and Auto-Resume features on the warehouse. This is the most efficient way to manage resources and optimize costs, indirectly addressing performance concerns.
D) Focus exclusively on optimizing SQL queries and data transformations. Monitoring is unnecessary since Snowflake automatically handles performance optimization.
E) Rely solely on Snowflake's default query history and resource monitors. These automatically track performance and usage, providing sufficient insight without additional configuration.

Solutions: