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[Hardware] 100% Pass Snowflake - DEA-C02 - Updated SnowPro Advanced: Data Engineer (DEA-C02

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【Hardware】 100% Pass Snowflake - DEA-C02 - Updated SnowPro Advanced: Data Engineer (DEA-C02

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q121-Q126):NEW QUESTION # 121
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. 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.
  • B. Execute 'SHOW TABLES' and check the 'clustering_key' column to ensure that the table is indeed clustered on 'EVENT _ TIMESTAMP'.
  • C. Use the SYSTEM$CLUSTERING_INFORMATIOW function to get the 'average_overlaps' for the table and 'EVENT_TIMESTAMP' column. A low value indicates good clustering.
  • D. Run ' EXPLAIN' on the slow query and examine the 'partitionsTotal' and 'partitionsScanned' values. A significant difference indicates effective clustering.
  • E. Run 'SYSTEM$ESTIMATE QUERY COST to estimate the query cost to see if the clustering is impacting the cost.
Answer: D
Explanation:
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.

NEW QUESTION # 122
You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?
  • A. Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
  • B. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
  • C. Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
  • D. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
  • E. Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.
Answer: A,D
Explanation:
To comprehensively troubleshoot COPY INTO performance, you need data on the copy operation itself (COPY HISTORY), overall account and data validation. The COPY_HISTORY view provides details about each COPY INTO execution, including the file size, load time, and any errors encountered. Query profiling offers detailed insight into the internal operations of the COPY INTO command, revealing bottlenecks. Monitoring S3 for throttling ensures that the data source isn't limiting performance. Using helps correlate storage growth with load times. LOAD_HISTORY doesn't exist, 'VALIDATE function is for data validation not performance. While warehouse CPU utilization is useful, it doesn't provide the specific details needed to diagnose COPY INTO issues. External network monitoring is also less relevant than checking for S3 throttling and analyzing Snowflake's internal telemetry data.

NEW QUESTION # 123
Consider the following Snowflake UDTF definition written in Python:

Which of the following statements are TRUE regarding the deployment and usage of this UDTF?
  • A. The return type of the generator 'yield' must strictly adhere to the declared output schema , or errors will occur during execution.
  • B. The library needs to be explicitly installed and configured within the UDTF's environment using a Snowpark session.
  • C. The UDTF needs to be registered using 'session.udtf.register' or 'create or replace function' with the 'imports' clause referencing the Python file, and the handler' specifying the function name.
  • D. The UDTF can be called directly in SQL using 'SELECT FROM TABLE(process_json(VARlANT COLUMN));' without any prior registration.
  • E. The UDTF will automatically be available in all schemas across all databases in the Snowflake account.
Answer: A,C
Explanation:
UDTFs in Snowflake require explicit registration using 'session.udtf.register' or 'create or replace function' command, defining the location of the source code (Python file) and the function to be executed. Also, data types of values which is 'yield'ed in the body of UDTF must strictly adhere with Output schema. Libraries from 'snowflake.snowpark' are usually available and does not needs explicit configuration. UDTFs are schema-bound, not automatically available everywhere. Direct call to UDTF without creation isn't possible.

NEW QUESTION # 124
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. The use of both techniques increases query performance drastically.
  • B. Masking policies and external tokenization provide independent layers of security. If one is compromised, the other still provides protection.
  • C. 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.
  • D. Combining masking policies and external tokenization allows for complete elimination of PCl data from the Snowflake environment, even during processing.
  • E. 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.
Answer: B,E
Explanation:
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

NEW QUESTION # 125
You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

  • A. Option C
  • B. Option E
  • C. Option D
  • D. Option B
  • E. Option A
Answer: C
Explanation:
Option D is the most efficient and correct. It uses 'snowflake.snowpark.functions' to correctly reference the columns using and 'dateadd()" for date arithmetic. Option A and C attempts to use native python date functions, and Option E passes a SQL string directly to the filter, bypassing Snowpack's function calls. and 'filter()' are functionally equivalent in Snowpark. Option B, while technically correct, uses 'dateadd' with which is better suited for Snowflake SQL than Snowpark operations. Option D keeps the entire filtering logic within Snowpark.

NEW QUESTION # 126
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