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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You've created a JavaScript UDF in Snowflake to perform complex string manipulation. You need to ensure this UDF can handle a large volume of data efficiently. The UDF is defined as follows:

When testing with a large dataset, you observe poor performance. Which of the following strategies, when applied independently or in combination, would MOST likely improve the performance of this UDF?

A) Pre-compile the regular expressions used within the JavaScript UDF outside of the function and pass them as constants into the function. JavaScript regex compilation is expensive, and pre-compilation can reduce overhead.
B) Replace the JavaScript UDF with a SQL UDF that uses built-in Snowflake string functions like 'REGEXP REPLACE and 'REPLACE. SQL UDFs are generally more optimized within Snowflake's execution engine.
C) Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.
D) Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.
E) Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.

2. You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

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

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

4. You are designing a data pipeline that uses the Snowflake SQLAPI to execute a series of complex SQL queries. These queries involve multiple joins, aggregations, and user-defined functions (UDFs). You need to ensure that the pipeline is resilient to transient network errors and can handle a large volume of concurrent requests. Which of the following strategies would you implement to enhance the reliability and performance of your pipeline?

A) Disable query result caching in Snowflake to ensure that the pipeline always retrieves the latest data.
B) Implement exponential backoff and retry logic in your client application to handle transient errors when calling the SQL API.
C) Bundle all the SQL queries into a single, large SQL statement to reduce the number of API calls.
D) Increase the timeout value for the SQL API requests to accommodate potentially long-running queries.
E) Use the SQL API's asynchronous execution mode and poll for query status to handle long-running queries without blocking.

5. Consider a scenario where you have a Snowflake external table 'ext_logs' pointing to log files in an S3 bucket. The log files are continuously being updated, and new files are added frequently. You want to ensure that your external table always reflects the latest data available in S3. Which of the following actions and configurations are required or recommended to keep the external table synchronized with the underlying data source? (Select all that apply)

A) Configure an event notification service (e.g., AWS SQS) to trigger an external table refresh whenever new files are added to S3.
B) Create a Snowpipe that continuously loads data from the S3 bucket into a Snowflake table instead of using an external table.
C) Periodically execute the 'ALTER EXTERNAL TABLE ext_logs REFRESH' command to update the metadata about the files in S3.
D) Implement a stored procedure that periodically executes a query on the external table to force a metadata refresh.
E) Enable auto-refresh on the external table using the 'AUTO_REFRESH = TRUE parameter during creation.

Solutions: