QUIZ SNOWFLAKE - DEA-C02 - SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) NEWEST NEW EXAM CAMP

Quiz Snowflake - DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02) Newest New Exam Camp

Quiz Snowflake - DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02) Newest New Exam Camp

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q321-Q326):

NEW QUESTION # 321
You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?

  • A. Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.
  • B. JavaScript UDF utilizing regular expressions for simple string replacements.
  • C. SQL UDF with nested CASE statements for address standardization.
  • D. Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.
  • E. Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.

Answer: D

Explanation:
Python UDTFs with Anaconda packages offer the best balance of flexibility, performance, and ease of use for complex data transformations requiring external libraries. Snowflake's integration with Anaconda allows for the seamless use of popular data science and engineering libraries, making Python UDTFs ideal for tasks like address standardization. Java can be useful, but the overhead of JAR management and potentially less efficient integration with Snowflake's execution engine can be a disadvantage. SQL and JavaScript offer limited expressiveness for complex tasks requiring external libraries. While Scala is powerful, it can present a steeper learning curve and may not be as widely adopted as Python within the Snowflake ecosystem for UDTFs.


NEW QUESTION # 322
Which of the following statements are accurate regarding the differences between SQL UDFs and Java UDFs in Snowflake? (Select two)

  • A. SQL UDFs and Java UDFs are interchangeable, and there is no performance difference between them.
  • B. Java UDFs always execute faster than SQL UDFs due to JVM optimizations.
  • C. SQL UDFs can only be used for simple transformations and cannot execute external calls, while Java UDFs can perform complex logic and interact with external services via libraries.
  • D. Java UDFs are deprecated and should not be used; instead, SQL UDFs are recommended for all scenarios.
  • E. SQL UDFs are defined using SQL code within Snowflake, whereas Java UDFs require uploading a JAR file containing the compiled Java code.

Answer: C,E

Explanation:
SQL UDFs are suitable for simpler transformations within Snowflake and cannot make external calls. They are defined directly using SQL code. Java UDFs, on the other hand, offer more flexibility by allowing complex logic implementation, interaction with external services/libraries via JAR files, and custom code. Java UDFs are generally perform better when complex transformations are needed, where SQL UDFs can become cumbersome. Performance depends on the workload. Option B is wrong becuase SQL UDFs are more performant for simpler tasks. Option D is wrong becuase its highly dependant on workload, where options E is wrong as Java UDFs are very useful and not deprecated.


NEW QUESTION # 323
You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, 'task B' , depends on the successful completion of 'task A'. 'task_B' occasionally fails due to transient network issues. To ensure the pipeline's robustness, you need to implement a retry mechanism for 'task_B' without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for 'task_B' is as follows:

  • A. Utilize Snowflake's external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.
  • B. Embed the retry logic directly within the stored procedure called by 'task_B'. The stored procedure should catch exceptions related to network issues, introduce a delay using 'SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.
  • C. Create a separate task, 'task_C', that is scheduled to run immediately after 'task will check the status of 'task_BS in the TASK HISTORY view. If 'task_B' failed, 'task_c' will re-enable 'task_B' and suspend itself. Use the parameter on 'task_B' to limit the number of retries.
  • D. Leverage Snowflake's event tables like QUERY_HISTORY and TASK _ HISTORY in the ACCOUNT_USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
  • E. Modify the task definition of 'task_B' to include a SQL statement that checks for the success of 'task_R in the TASK_HISTORY view before executing the main logic. If 'task_A' failed, use ' SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.

Answer: B

Explanation:
Option C is the most efficient and self-contained approach using native Snowflake features. Embedding the retry logic within the stored procedure called by 'task_ff allows for fine-grained control over the retry process, exception handling, and delay implementation. The retry count limit prevents infinite loops. Option A, while technically feasible, involves querying the TASK HISTORY view, which can be less efficient. Option B requires creating and managing an additional task. Option D introduces external dependencies, making the solution more complex. Option E does not address the retry mechanism.


NEW QUESTION # 324
You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

  • A. Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
  • B. Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
  • C. Set 'snowflake.ingest.file.name' to an existing file in a stage.
  • D. Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.
  • E. Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.

Answer: B

Explanation:
The correct answer is E. Currently, the Snowflake Kafka connector does not directly support automatic schema evolution. You cannot configure the connector to automatically alter the Snowflake table schema based on changes in the Kafka topic's data structure. You must manually manage schema changes in the Snowflake table to align with the structure of the data being ingested from Kafka. Option D will simply throw errors as the configuration needed is not fully complete with data types. The connector does rely heavily on the VARIANT column and would not be able to evolve properly, and so, that function is not directly available.


NEW QUESTION # 325
You are working with a very large Snowflake table named 'CUSTOMER TRANSACTIONS which is clustered on 'CUSTOMER ID and 'TRANSACTION DATE. After noticing performance degradation on queries that filter by 'TRANSACTION AMOUNT and 'REGION' , you decide to explore alternative clustering strategies. Which of the following actions, when performed individually, will LEAST likely improve query performance specifically for queries filtering by 'TRANSACTION AMOUNT and 'REGION', assuming you can only have one clustering key?

  • A. Creating a search optimization on 'TRANSACTION_AMOUNT' and 'REGION' columns.
  • B. Dropping the existing clustering key and clustering on 'TRANSACTION_AMOUNT' and 'REGION'.
  • C. Adding ' TRANSACTION_AMOUNT and 'REGIO!V to the existing clustering key while retaining 'CUSTOMER_ID and 'TRANSACTION_DATE
  • D. Creating a materialized view that pre-aggregates data by 'TRANSACTION_AMOUNT and 'REGION'.
  • E. Creating a new table clustered on 'TRANSACTION_AMOUNT and 'REGION', and migrating the data.

Answer: C

Explanation:
Adding 'TRANSACTION_AMOUNT and 'REGION' to the existing clustering key while retaining 'CUSTOMER ID and 'TRANSACTION_DATE (option D) is the LEAST likely to improve performance for queries filtering by 'TRANSACTION_AMOUNT and 'REGION' Clustering is most effective when the order of columns in the clustering key matches the order in which they are filtered in the query. Because the query filters on 'TRANSACTION_AMOUNT and 'REGION', these columns should be the leading columns in the clustering key for optimal pruning. Since the leading keys are and , snowflake would still read significant amount of unnecessary data for filtering. A, C, and E all address having 'TRANSACTION_AMOUNT and 'REGION' as keys, and B addresses caching.


NEW QUESTION # 326
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