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最新的 SnowPro Advanced DEA-C02 免費考試真題 (Q113-Q118):

問題 #113
You are troubleshooting a slowly performing query in Snowflake that aggregates data from a large ORDERS table (10 billion rows) partitioned by ORDER DATE. The query execution plan shows significant 'Remote Spill to Disk'. Which of the following actions would be MOST effective in reducing the spill and improving query performance? Assume all statistics are up-to-date and the data is properly clustered by ORDER_DATE.

A. Increase the value of the parameter. This allows the warehouse to scale up further if needed.
B. Optimize the query to leverage data pruning based on ORDER DATE by ensuring the query filters on a specific or limited range of ORDER DATE values.
C. Increase the virtual warehouse size. This will provide more memory for the query to execute.
D. Rewrite the query to use window functions instead of aggregate functions.
E. Reduce the number of columns selected in the query, only selecting those that are essential for the aggregation.

答案：B

解題說明：
Remote Spill to Disk indicates that Snowflake is running out of memory to process the query. Increasing the virtual warehouse size (A) can help, but focusing on data pruning (E) is often more effective. By filtering on ORDER_DATE, you drastically reduce the amount of data that needs to be processed, minimizing memory usage and the need to spill to disk. Options B, C, and D are less likely to directly address the spill issue. doesn't exist. Window functions don't inherently resolve spills. Reducing selected columns can help slightly but is usually less impactful than data pruning.

問題 #114
A data engineer is tasked with creating a Listing to share a large dataset stored in Snowflake. The dataset contains sensitive Personally Identifiable Information (PII) that must be masked for certain consumer roles. The data engineer wants to use Snowflake's dynamic data masking policies within the Listing to achieve this. Which of the following approaches is the MOST secure and maintainable way to implement this requirement, assuming that the consumer roles are pre-defined and known?

A. Create multiple versions of the shared tables, each with different masking applied. The data engineer must manually manage which version each consumer can access.
B. Implement an external function that masks the data based on the consumer's role and share this function in the Listing. Use this external function in a view shared through the listing.
C. Apply dynamic data masking policies directly to the base tables containing the PII and share these tables in the Listing. Policies should use the function to determine when to mask the data.
D. Create a view that applies conditional masking using 'CASE' statements based on the function and share the view in the Listing.

答案：C

解題說明：
Applying dynamic data masking policies directly to the base tables is the MOST secure and maintainable approach. This ensures that the masking is applied consistently across all access methods. Using the function within the policy allows for dynamic masking based on the consumer's role. Views can provide masking but doesn't give the same protection as the base tables can be accessed directly. Data masking policies are applied transparently and require less maintenance than views with complex CASE statements or UDFs. Also, the approach using Snowflake Secret Manager adds unnecessary complexity.

問題 #115
A data engineering team is running a series of complex analytical queries against a large Snowflake table. They notice that query performance is inconsistent, with some queries running much slower than others. After investigation, they determine that the queries are not properly leveraging the data clustering. Which of the following actions could improve the query performance related to the data clustering? Select all that apply.

A. Execute SYSTEM$CLUSTERING INFORMATION( ' )' y to assess the impact of clustering on query performance and determine if re-clustering is needed.
B. Increase the virtual warehouse size to improve the performance of all queries, regardless of clustering.
C. Run 'ALTER TABLE DROP CLUSTERING KEY to remove the existing clustering key and allow Snowflake to automatically cluster the data.
D. Run 'ALTER TABLE CLUSTER BY (columnl, column2Y to explicitly define a clustering key on the table.
E. Run 'SHOW TABLES LIKE ' ' and review the 'clustering_information' column to understand the current clustering depth and benefit.

答案：A,D,E

解題說明：
Option A is correct because explicitly defining a clustering key allows Snowflake to optimize data organization for common query patterns. Option C is correct because understanding the current clustering state helps diagnose if clustering is effective. Option E is correct, using SYSTEM$CLUSTERING INFORMATION is a crucial method to determine reclustering needs. Option B can worsen performance, especially if the automatic clustering does not align with common query patterns. Option D may improve performance to a small extend but doesn't directly address the clustering issue.

問題 #116
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. 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.
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. 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.
D. 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.
E. Wrap each data quality check in a try-catch block and use 'println' statements to log error messages to the Snowflake console.

答案：C

解題說明：
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.

問題 #117
You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

A. Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.
B. Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.
C. Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
D. Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.
E. Increase the number of Snowflake virtual warehouses to handle the increased load.

答案：B,C

解題說明：
B and C are correct. Implementing client-side retry logic with exponential backoff (B) prevents overwhelming the service during transient errors. Using a message queue like Kafka (C) buffers the data, smoothing out traffic spikes and providing better resilience. A is less effective as scaling warehouses won't directly address client-side issues like retry logic and buffering. D can help but is not as effective as a buffering mechanism or robust retry strategy. E is incorrect as Snowpipe Streaming is designed for lower latency than classic Snowpipe.

問題 #118
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