Snowflake DEA-C02 실제 시험문제 및 답변

문제1
A data engineering team has deployed an external function that leverages a cloud-based machine learning model. They are experiencing intermittent errors and performance degradation, and they suspect issues with the external function's batching and error handling. Which of the following steps would BEST address these issues and improve the reliability and performance of the external function? (Select TWO)

A. Decrease the timeout period for the external function to quickly fail and retry in case of errors.

B. Implement robust error handling within the external function's code to catch exceptions and return meaningful error messages to Snowflake.

C. Remove the "RETURNS NULL ON NULL INPUT clause from the external function definition to force all inputs to be processed.

D. Increase the 'MAX BATCH_ROWS' parameter of the external function to send larger batches of data to the remote service.

E. Monitor the external function's execution metrics using Snowflake's query history and the remote service's monitoring tools to identify bottlenecks and errors.

정답: B,E

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문제2
You're designing a Snowpark data transformation pipeline that requires running a Python function on each row of a large DataFrame. The Python function is computationally intensive and needs access to external libraries. Which of the following approaches will provide the BEST combination of performance, scalability, and resource utilization within the Snowpark architecture?

A. Load the DataFrame into a Pandas DataFrame using and then apply the Python function using Pandas DataFrame operations.

B. Use 'DataFrame.foreach(lambda row: my_python_function(row))' to iterate through each row and apply the Python function.

C. Create a Snowpark UDTF using gudtf(output_schema=StructType([StructField('result', StringType())]), and apply it to the DataFrame using with a lateral flatten operation.

D. Define a stored procedure in Snowflake and use it to execute the Python code on each row by calling it in a loop.

E. Create a Snowpark UDF using input_types=[StringType()], return_type=StringType())' and apply it to the DataFrame using

정답: C,E

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문제3
You have a table named 'TRANSACTIONS which is frequently queried by 'TRANSACTION_DATE and 'CUSTOMER ID. You want to define a clustering strategy for this table. You are aware that defining multiple clustering keys is possible. Given the following considerations, which of the following clustering strategies would provide the BEST performance AND minimize reclustering costs, assuming both columns have similar cardinality and are equally used in WHERE clauses? (Assume cost optimization is the most critical factor if performance difference is minimal.)

A. Create two separate tables: one clustered by 'TRANSACTION DATE and another clustered by 'CUSTOMER ID', and use appropriate views to redirect queries to the correct table.

B.

C.

D.

E.

정답: B,E

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문제4
You are responsible for ensuring data consistency across multiple Snowflake tables involved in a financial reporting system. You've noticed discrepancies in aggregate calculations between a 'TRANSACTIONS" table and a summary table 'MONTHLY REPORTS'. The 'TRANSACTIONS' table is frequently updated via streams and tasks. Which combination of the following strategies would be MOST effective in identifying and resolving these inconsistencies in near real-time?

A. Utilize Snowflake's Time Travel feature to compare the ' TRANSACTIONS' table and 'MONTHLY _ REPORTS' table at a specific point in time and identify the changes that led to the discrepancies.

B. Implement data validation checks within the data pipeline (streams and tasks) that update the 'TRANSACTIONS' table to reject transactions that violate predefined business rules.

C. Create a Snowflake alert that triggers when the difference in the total 'SALE_AMOUNT between the 'TRANSACTIONS' table and 'MONTHLY REPORTS' exceeds a predefined threshold within a specified time window.

D. Use Snowflake's row access policies to restrict access to the 'TRANSACTIONS' table, forcing users to only access the 'MONTHLY REPORTS table.

E. Implement a Snowflake task that periodically recalculates the 'MONTHLY_REPORTS' table from the 'TRANSACTIONS table and compares the results with the existing data, logging any discrepancies. Use a smaller warehouse size to minimize cost.

정답: A,B,C

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문제5
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 D

B. Option E

C. Option C

D. Option A

E. Option B

정답: B

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문제6
You are tasked with implementing column-level security on the 'EMPLOYEE table to restrict access to the 'SALARY column. Only users with the 'HR ROLE' should be able to view the actual salary. All other users should see NULL. You create a masking policy as follows:

What additional steps are necessary to enforce this policy?

A. Grant the SELECT privilege on the EMPLOYEE table to the HR ROLE.

B. Grant OWNERSHIP on the masking policy to the HR ROL

C. Apply the masking policy to the EMPLOYEE table: ALTER TABLE EMPLOYEE SET MASKING POLICY salary_mask;

D. Apply the masking policy to the SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask;

E. Grant the APPLY MASKING POLICY privilege to the HR_ROLE.

정답: D

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문제7
You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

A. Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.

B. Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.

C. Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.

D. Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.

E. Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.

정답: C

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문제8
A data engineer is tasked with creating an external table that points to a directory in AWS S3 containing CSV files. The files have a header row and are comma-delimited. The engineer executes the following DDL statement:

A. The statement will fail because the 'SKIP HEADER property is invalid and the 'skip_header' parameter should be used in the 'CREATE FILE FORMAT statement.

B. The statement will succeed if a file format named 'csv_format' with the specified properties already exists.

C. The statement will fail because external tables cannot be created directly from CSV files without defining a stage.

D. The statement will fail because the FIELD_DELIMITER property should be 'DELIMITER.

E. The statement will fail because the 'LOCATION' is missing the protocol (e.g., 's3:/P).

정답: A

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문제9
You are designing a data sharing solution for a multi-tenant application where each tenant's data must be isolated. You have a 'sales' table with a 'tenant_id' column. You need to implement row-level security to ensure that each tenant can only access their own data when querying the shared table. Which of the following approaches, considering performance and security, is the MOST suitable for implementing this row-level filtering in Snowflake?

A. Create a separate VIEW for each tenant, filtering by 'tenant_id'. Grant each tenant access only to their respective view.

B. Create a scheduled task that duplicates the sales table into a new table for each tenant, filtering by the tenant_id.

C. Implement a row access policy on the 'sales' table that filters data based on the 'tenant_id' column and the current role or user context.

D. Implement a user-defined function (UDF) that checks the current user's tenant ID and returns a boolean value indicating whether the row should be visible. Use this UDF in a WHERE clause in every query.

E. Use Snowflake's data masking policies to mask all data for tenants other than the one currently querying the table.

정답: C

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문제10
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. Wrap each data quality check in a try-catch block and use 'println' statements to log error messages to the Snowflake console.

B. 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'.

C. 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.

D. 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.

E. 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.

정답: E

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