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[General] DEA-C02 Demotesten & DEA-C02 Dumps Deutsch

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【General】 DEA-C02 Demotesten & DEA-C02 Dumps Deutsch

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 Prüfungsfragen mit Lösungen (Q328-Q333):328. Frage
A provider account is sharing a database named 'SHARED DB' through a share named 'MY SHARE. The consumer account has created a database named 'CONSUMER DB' from the share. The provider account revokes access to a table named 'SALES DATA within 'SHARED DB'. What will happen when a user in the consumer account attempts to query 'CONSUMER DB.SHARED SCHEMA.SALES DATA'?
  • A. The query will be cached based on the initial access, so users can continue query previous result based on same SQL
  • B. The query will be automatically re-routed to another available share containing 'SALES DATA'.
  • C. The query will fail with an error message indicating that the table does not exist or the user does not have privileges.
  • D. The query will execute successfully, but the user will receive an empty result set.
  • E. The query will execute successfully, but only return data that existed before the access was revoked.
Antwort: C
Begründung:
When access to a shared object is revoked in the provider account, the consumer account loses access to that object. Subsequent queries in the consumer account will fail with an error message indicating insufficient privileges or that the object does not exist. Snowflake does not automatically redirect queries to other shares or provide cached result once access is revoked.

329. Frage
You have a table 'CUSTOMERS' with columns 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, and 'EMAIL'. You need to transform this data into a semi-structured JSON format and store it in a VARIANT column named 'CUSTOMER DATA' in a table called 'CUSTOMER JSON'. The desired JSON structure should include a root element 'customer' containing 'id', 'name', and 'contact' fields. Which of the following SQL statements, used in conjunction with a CREATE TABLE and INSERT INTO statement for CUSTOMER JSON, correctly transforms the data?

  • A. Option B
  • B. Option A
  • C. Option D
  • D. Option C
  • E. Option E
Antwort: B
Begründung:
The correct answer constructs the JSON structure using nested 'OBJECT_CONSTRUCT functions. Option A directly creates a Snowflake VARIANT, which can be inserted into the 'CUSTOMER_DATR column. While many other approaches exist that involve parsing or converting to and from string values, those approaches are unnecessary because OBJECT_CONSTRUCT supports the correct desired behavior directly.

330. Frage
You are designing a data recovery strategy for a critical table 'CUSTOMER DATA' in your Snowflake environment. The data in this table is highly sensitive, and regulatory requirements mandate a retention period of at least 90 days for potential audits. You need to configure the Time Travel retention period to meet these requirements. What is the maximum supported Time Travel retention period, and how would you set it at the table level?
  • A. The maximum retention period is 90 days for Enterprise Edition or higher. You can set it using: 'ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 90;'
  • B. The maximum retention period is 365 days. You can set it using: ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 365;'
  • C. The maximum retention period is 90 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 90;'
  • D. The maximum retention period depends on your Snowflake edition and can be set at the account level only.
  • E. The maximum retention period is 7 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 7;'
Antwort: A
Begründung:
For Snowflake Enterprise Edition (or higher), the maximum Time Travel retention period is 90 days. The 'ALTER TABLE ... SET DATA RETENTION_TIME IN DAYS' command allows setting the retention period at the table level. Option D is partially correct about editions impacting limits, but incorrect about account-level settings only.

331. Frage
You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?
  • A. Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
  • B. Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
  • C. Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.
  • D. Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
  • E. Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.
Antwort: E
Begründung:
Option E is the most effective solution. Salting breaks up data skew before repartitioning. Automatic clustering on the transformed table optimizes future queries. Repartitioning redistributes the data across Snowflake's processing nodes, and Automatic Clustering will help in maintaining performance as the data changes in TRANSFORMED_SALES table over time. Option A, without salting, may still be inefficient due to the initial skew. Option B improves query performance but doesn't address the initial transformation skew. Option C is incorrect because 'pandas' in Snowpark does not automatically handle data skew at the Snowflake level. Option D is a costly workaround that doesn't fundamentally solve the skew problem.

332. Frage
You've created a JavaScript UDF in Snowflake to perform complex string manipulation. You need to ensure this UDF can handle a large volume of data efficiently. The UDF is defined as follows:

When testing with a large dataset, you observe poor performance. Which of the following strategies, when applied independently or in combination, would MOST likely improve the performance of this UDF?
  • A. Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.
  • B. Replace the JavaScript UDF with a SQL UDF that uses built-in Snowflake string functions like 'REGEXP REPLACE and 'REPLACE. SQL UDFs are generally more optimized within Snowflake's execution engine.
  • C. Pre-compile the regular expressions used within the JavaScript UDF outside of the function and pass them as constants into the function. JavaScript regex compilation is expensive, and pre-compilation can reduce overhead.
  • D. Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.
  • E. Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.
Antwort: A,B,C
Begründung:
Options A, C and E can all contribute to better performance. SQL UDFs benefit from Snowflake's optimized execution engine for standard operations, making them often faster than JavaScript UDFs for string manipulation when possible. Pre-compiling regular expressions (Option C) avoids redundant compilation steps during each UDF invocation. Converting to a Java UDF (Option E) gives more control over efficiency compared to JS. The option D may help, but the performance gain is not guaranteed and is more related to resource availability than the UDF's efficiency. The option B is not valid since the size of input STRING won't matter the javascript engine.

333. Frage
......
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