Title: DEA-C02 Fragen Und Antworten & DEA-C02 Deutsche [Print This Page] Author: raybell705 Time: yesterday 18:58 Title: DEA-C02 Fragen Und Antworten & DEA-C02 Deutsche Laden Sie die neuesten ZertPruefung DEA-C02 PDF-Versionen von Pr¨¹fungsfragen kostenlos von Google Drive herunter: https://drive.google.com/open?id=1fmyljDb1Bp7CEw9aDyylYbHcBGHQHUIE
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Snowflake DEA-C02 Deutsche & DEA-C02 Fragen BeantwortenZertPruefung bietet Ihnen die neuesten Schulungsunterlagen zur Snowflake DEA-C02 Zertifizierungspr¨¹fung. Die fleißigen IT-Experten von ZertPruefung aktualisieren ständig Schulungsunterlagen durch ihre eigene Kompetenz und Erfahrung, so dass die IT-Fachleute die Pr¨¹fung m¨¹hlos bestehen können. Das Snowflake DEA-C02 Zertifikat stellt eine immer wichtigere Stelle in der IT-Branche dar. Und immer mehr Leute haben sich an dieser Pr¨¹fung beteiligt. Und viele davon benutzen unsere Produkte von ZertPruefung und haben die Snowflake DEA-C02 Zertifizierungspr¨¹fung bestanden. Die Feedbacks von diesen Leute haben bewiesen, dass unsere Produkte von ZertPruefung eher zuverlässig sind. Snowflake SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 Pr¨¹fungsfragen mit Lösungen (Q194-Q199):194. Frage
You are designing a data protection strategy for a Snowflake environment that processes sensitive payment card industry (PCI) data'. You decide to use a combination of column-level security and external tokenization. Which of the following statements are TRUE regarding the advantages of using both techniques together? (Select TWO)
A. Masking policies and external tokenization provide independent layers of security. If one is compromised, the other still provides protection.
B. Combining masking policies and external tokenization allows for complete elimination of PCl data from the Snowflake environment, even during processing.
C. Column-level security can be used to restrict access to the tokenization UDF itself, ensuring that only authorized users can perform tokenization or detokenization operations.
D. The use of both techniques increases query performance drastically.
E. Tokenization ensures compliance with PCl DSS standards, while masking policies are primarily useful for internal access control and obfuscation for development environments. Using both doesn't increase security.
Antwort: A,C
Begr¨¹ndung:
Combining masking policies and external tokenization provides defense in depth. Masking policies control who can see sensitive data (or a masked version), while tokenization replaces the actual data with a non-sensitive representation. This means that even if a user bypasses the masking policy (e.g., through a vulnerability), they still won't see the actual PCl data. Also, you can use column-level security to control access to the tokenization and detokenization functions. Option A is incorrect, sensitive data exists until tokenized. Option C is incorrect as using both techniques strengthens security. Option E is Incorrect, as both the techniques impact query performance
195. Frage
You are building a data pipeline using Snowflake Tasks to orchestrate a series of transformations. One of the tasks, 'task _ transform data', depends on the successful completion of another task, 'task extract_data'. However, occasionally fails due to transient network issues. You want to implement a retry mechanism for 'task_extract data' without impacting the overall pipeline execution time significantly. Which of the following approaches is the most appropriate and efficient way to achieve this within the Snowflake Task framework?
A. Create a new root-level task that checks the status of 'task_extract_data'. If it failed, the root-level task will execute a copy of the 'task_extract data' task. After this, it updates the 'task_transform_data"s 'AFTER' condition to depend on the new task that retries extraction.
B. Use the 'AFTER keyword in the 'CREATE TASK' statement for 'task_transform_data' to only execute if succeeds on its first attempt. If fails, the entire pipeline will stop, ensuring data consistency.
C. Modify the task definition to call a stored procedure. The stored procedure implements a loop with a retry counter. Inside the loop, execute the data extraction logic. If an error occurs, catch the exception, wait for a few seconds, and retry the extraction. After a specified number of retries, raise an exception to signal task failure.
D. Implement a TRY...CATCH block within the task definition to catch any exceptions. Inside the CATCH block, use SYSTEM$WAIT to pause for a few seconds, then re- execute the core logic of the task. Repeat this process a limited number of times before failing the task permanently.
E. Configure the task with an error notification integration that sends alerts upon failure. Manually monitor these alerts and manually resume the task if it fails. Use 'ALTER TASK task extract data RESUME;'
Antwort: C
Begr¨¹ndung:
Implementing the retry logic within a stored procedure called by the task (B) provides the most controlled and efficient way to handle transient errors. The stored procedure can handle the retry attempts, waiting periods, and error handling without requiring manual intervention or significantly impacting the overall pipeline. A is less desirable because SYSTEM$WAIT is generally discouraged in task definitions. C relies on manual intervention and doesn't automate the retry. D doesn't address the need for retries. E is overly complex and unnecessary. It also defeats the purpose of using 'AFTER' keyword.
196. Frage
You are tasked with creating a system to monitor the data quality of a 'SALES DATA" table. The table is updated daily with new sales records, and you need to ensure that the 'SALE AMOUNT column always contains positive values. You decide to use Snowflake tasks and streams for this purpose. Consider the following Snowflake script. What is the most appropriate way to modify the 'SALES DATA' table so the task has a 'WHEN' clause that runs only if the 'SALE AMOUNT has negative values? Assume the stream 'SALES DATA STREAM' is properly configured on 'SALES DATA'.
A.
B.
C.
D.
E.
Antwort: C
Begr¨¹ndung:
Option C is the most efficient. It combines 'SYSTEM STREAM_HAS_DATA('SALES_DATA_STREAM')' to check if there are any changes in the stream with an 'EXISTS' clause to verify that at least one row in the stream has a 'SALE_AMOUNT' less than 0. This ensures that the task only runs when there are new records in the stream AND at least one of those records violates the data quality rule.
197. Frage
You have a large Snowflake table 'WEB EVENTS that stores website event data'. This table is clustered on the 'EVENT TIMESTAMP column. You've noticed that certain queries filtering on a specific 'USER ID' are slow, even though 'EVENT TIMESTAMP clustering should be helping. You decide to investigate further Which of the following actions would be MOST effective in diagnosing whether the clustering on 'EVENT TIMESTAMP is actually benefiting these slow queries?
A. Run 'SYSTEM$ESTIMATE QUERY COST to estimate the query cost to see if the clustering is impacting the cost.
B. Use the SYSTEM$CLUSTERING_INFORMATIOW function to get the 'average_overlaps' for the table and 'EVENT_TIMESTAMP' column. A low value indicates good clustering.
C. Run ' EXPLAIN' on the slow query and examine the 'partitionsTotal' and 'partitionsScanned' values. A significant difference indicates effective clustering.
D. Execute 'SHOW TABLES' and check the 'clustering_key' column to ensure that the table is indeed clustered on 'EVENT _ TIMESTAMP'.
E. Query the 'QUERY_HISTORY view to see the execution time of the slow query and compare it to the average execution time of similar queries without a 'USER filter.
Antwort: C
Begr¨¹ndung:
The ' EXPLAIN' command provides detailed information about the query execution plan. By examining the 'partitionsTotal' and 'partitionsScanned' values, you can directly see how many micro-partitions Snowflake considered vs. how many it actually scanned. A large difference suggests that the clustering is effectively pruning partitions based on the 'EVENT_TIMESTAMP' filter. While 'SYSTEM$CLUSTERING_INFORMATION' provides a general overview of clustering quality, it doesn't tell you how it's performing for a specific query. Looking at query history or checking that the clustering key is defined is useful for verifying basic setup but doesn't directly diagnose the effectiveness for slow queries.
198. Frage
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. Rely solely on Snowflake's default query history and resource monitors. These automatically track performance and usage, providing sufficient insight without additional configuration.
E. Focus exclusively on optimizing SQL queries and data transformations. Monitoring is unnecessary since Snowflake automatically handles performance optimization.
Antwort: A,B
Begr¨¹ndung:
Options B and C provide the most effective methods for continuous monitoring and performance optimization. Option B allows for highly customized and granular monitoring of the entire pipeline, enabling proactive issue identification through external notifications. Option C leverages Snowflake's built-in system functions and task scheduling to create a robust monitoring and alerting system. Option A is insufficient as default monitoring may not provide the granularity needed. Option D is incorrect because monitoring is crucial. Option E primarily focuses on cost optimization, not performance monitoring.
199. Frage
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