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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q322-Q327):

NEW QUESTION # 322
You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)

A. Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.
B. All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.
C. External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.
D. Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.
E. Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.

Answer: B,C,D

Explanation:
Cloning a schema clones all objects within it (tables, views, UDFs, etc.). Cloning uses metadata cloning initially, so it doesn't immediately duplicate the data. Sequences resume from where they left off (if not used cloned schema). Tasks and streams are not cloned when cloning a schema, unless the clone is done at the database level. External tables are cloned, but the data files remain in their original cloud storage location.

NEW QUESTION # 323
A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A. Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.
B. Contact Snowflake Support and request restoration from Fail-safe.
C. Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
D. Use the 'UNDROP DATABASE PRODUCTION command.
E. Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'

Answer: C

Explanation:
Fail-safe is a last resort for data recovery managed entirely by Snowflake. Users cannot directly access or restore data from Fail- safe. Options B and C are valid for Time Travel, but fail if the data falls outside of that window. Option A is partially correct; you contact Snowflake support, who then might use fail-safe if appropriate, but option E is the most accurate answer. Option D uses Time Travel, which may not work.

NEW QUESTION # 324
You're tasked with building an external function in Snowflake that calls an API to enrich customer data with geographical information (latitude and longitude) based on their IP address. The API endpoint requires an API key passed in the headen Your external function definition looks like this: "'sql CREATE OR REPLACE EXTERNAL FUNCTION VARCHAR) RETURNS VARIANT VOLATILE MAX BATCH ROWS = 100 RETURNS NULL ON NULL INPUT API INTEGRATION = AS 'https://api.example.com/geo'; Which of the following steps are essential to ensure the external function correctly passes the API key to the external service, handles rate limiting from the API, and correctly parses the JSON response from the external service (Assume the API returns a JSON object with 'latitude' and 'longitude' fields)?

A. Implement robust logging within the external service (e.g., AWS Lambda or Azure Function) to capture details of requests, responses, and any errors encountered. Monitor these logs regularly to identify and address potential issues.
B. Ensure the Snowflake user executing the function has the 'USAGE' privilege on the API integration object.
C. Modify the 'enrich_ip' function to directly embed the API key within the API endpoint URL as a query parameter.
D. Create a Snowflake API integration object that includes the 'ALLOWED_HOSTS parameter pointing to 'https://api.example.com', and configure the service to return retry-after header on rate limit and ensure retryPolicy is set to 3 retries.
E. Modify the external function code within the external service (e.g., AWS Lambda or Azure Function) to read the API key from an environment variable, construct the appropriate request headers (including the API key), implement exponential backoff with jitter for handling rate limits based on retry-after header if received, and ensure proper error handling for JSON parsing failures and return Snowflake compatible results.

Answer: A,B,D,E

Explanation:
The correct answers are A, B, C and E. Option A is necessary to establish trust between Snowflake and the external service. Option B ensures the API key is securely passed, rate limiting is handled gracefully, proper exception handling is implemented, and JSON is properly parsed. Option C provides proper privleges. Option E provide proper monitoring. Option D is incorrect because embedding API keys in the URL is a security vulnerability.

NEW QUESTION # 325
You have a table 'EMPLOYEE with a column 'SALARY'. You need to mask the 'SALARY column for all roles EXCEPT for the PAYROLL ADMIN' role. You create the following masking policy:

You then apply this masking policy to the 'SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask; However, users who are not granted the 'PAYROLL _ADMIN' role are still seeing the actual salary values. What is the MOST likely reason for this and how can you resolve it?

A. Option D
B. Option E
C. Option B
D. Option C
E. Option A

Answer: E

Explanation:
The most common reason for masking policies not to function as expected is due to direct privileges on the underlying table bypassing the policy. Even with a masking policy in place, if a user has direct SELECT privilege on the table, they will see the unmasked data. Revoking the SELECT privilege on the table forces users to access data through roles where masking policies apply. Option C, UNMASK privilege could also be a cause, but Option A is more likely as it's a common oversight during setup.

NEW QUESTION # 326
You're designing a Snowpark Scala stored procedure that must execute a series of complex data quality checks on a Snowflake table.
These checks involve multiple steps, including validating data types, checking for null values, and verifying data consistency against external reference data'. You want to ensure that the stored procedure is resilient to errors, provides detailed logging, and can be easily monitored. Which of the following approaches would be the MOST robust and scalable for handling errors and logging within this Snowpark Scala stored procedure?

A. Use Scala's 'Try' monad to handle exceptions, mapping successes to informational messages and failures to error messages. Log these messages using Snowflake's event tables.
B. Implement a custom logging framework within the Scala stored procedure that writes detailed logs to a dedicated Snowflake table. Use try-catch blocks to handle exceptions and log error details, including timestamps, error codes, and relevant data values. Use Snowflake's 'SYSTEM$LAST QUERY ID()' function to track query lineage.
C. Rely on Snowflake's built-in error handling and logging mechanisms. If an error occurs, the stored procedure will automatically fail, and the error details can be retrieved from Snowflake's query history.
D. Use Scala's 'Option' type to handle potential null values and exceptions. Return a string message indicating success or failure for each check. Log these messages using 'System.out.println'.
E. Wrap each data quality check in a try-catch block and use 'println' statements to log error messages to the Snowflake console.

Answer: B

Explanation:
Option C provides the most robust and scalable solution. Writing logs to a dedicated Snowflake table allows for detailed analysis and monitoring of data quality checks. Using try-catch blocks provides granular error handling, and including timestamps and error codes in the logs facilitates troubleshooting. provides query lineage. Options A and B are insufficient because 'println' and 'System.out.println' log to the session's output but lack persistence and are difficult to analyze at scale. Option D relies on Snowflake's general error handling but lacks detailed custom logging. Option E relies on internal event tables which the developer does not have control or access to.

NEW QUESTION # 327
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