DEA-C02 - PERFECT EXAM SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) FORMAT

DEA-C02 - Perfect Exam SnowPro Advanced: Data Engineer (DEA-C02) Format

DEA-C02 - Perfect Exam SnowPro Advanced: Data Engineer (DEA-C02) Format

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Tags: Exam DEA-C02 Format, DEA-C02 Test Sample Online, Practice DEA-C02 Exam Pdf, Pass DEA-C02 Test Guide, DEA-C02 Free Practice Exams

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q274-Q279):

NEW QUESTION # 274
Consider a scenario where you have a Snowflake table named 'CUSTOMER DATA' containing customer IDs (INTEGER) and encrypted credit card numbers (VARCHAR). You need to create a secure JavaScript UDF to decrypt these credit card numbers using a custom encryption key stored securely within Snowflake's internal stage, and then mask all but the last four digits of the decrypted number for data protection. Which of the following actions are necessary to ensure both functionality and security while adhering to Snowflake's best practices for UDF development and security?

  • A. Store the encryption key directly within the JavaScript UDF code as a string variable.
  • B. Store the encryption key in a separate file on an internal stage accessible only by the UDF's service account and load the key from the file within the UDF at runtime.
  • C. Pass the encryption key as an argument to the UDF each time it is called.
  • D. Encrypt the key using a weaker encryption algorithm before storing it in an internal stage to balance security and performance.
  • E. Use Snowflake's Secure Vault (Secret) feature to store the encryption key and retrieve it securely within the UDF.

Answer: B,E

Explanation:
Options B and D are the correct answers. Option B - Storing the encryption key in a file on an internal stage, accessible only by the UDF's service account, is a secure way to manage the key. Option D - Snowflake's Secure Vault (Secret) feature is designed specifically for securely storing and managing sensitive information like encryption keys. This is the most recommended approach. Options A and C are insecure and should be avoided. Option E defeats the purpose of encryption.


NEW QUESTION # 275
A large e-commerce company uses Snowflake to store website clickstream data in a table named 'WEB EVENTS'. This table is partitioned using the 'EVENT DATE column. The company needs to analyze user behavior across different devices. A common query involves joining 'WEB EVENTS' with a smaller 'USER DEVICES' table (containing user-to-device mappings) to determine the device type for each event. However, the performance of this join operation is poor, especially when filtering 'WEB EVENTS' by a specific date range. The 'USER DEVICES table is small enough to fit in memory. What is the most effective approach to optimize this query for performance?

  • A. Broadcast the 'USER DEVICES table to all compute nodes before performing the join. (Hint: Consider using 'BROADCAST hint)
  • B. Use a 'LATERAL FLATTEN' function to process the data in parallel.
  • C. Convert the 'WEB EVENTS' table to use a VARIANT data type and query with JSON path expressions.
  • D. Use a standard 'JOIN' operation between 'WEB_EVENTS' and USER_DEVICES' without any modifications.
  • E. Create a materialized view that pre-joins 'WEB_EVENTS' and 'USER_DEVICES' tables without filtering

Answer: A

Explanation:
Since the 'USER_DEVICES' table is small, broadcasting it to all compute nodes allows Snowflake to perform a local join, avoiding network transfers and significantly improving performance. Using the 'BROADCAST hint will make use of this functionality. Standard join will not be efficient. 'LATERAL FLATTEN' is for semi-structured data. While Materialized views improves performance, here Broadcasting is the most cost effective. Using VARIANT data type and JSON path expression slows down the query.


NEW QUESTION # 276
You are tasked with building a Snowpipe to ingest JSON data from an AWS S3 bucket into a Snowflake table named 'SALES DATA'. The data is landing in the bucket frequently, and you want to use Snowpipe's auto-ingest feature. However, you are observing significant latency in data appearing in your Snowflake table after it lands in S3, despite verifying that S3 event notifications are correctly configured and the Snowflake event queue is receiving them. You've already checked that the pipe is enabled and has the necessary permissions. The Snowflake Pipe definition is as follows:

What is the MOST LIKELY reason for this delay, and what steps can you take to further troubleshoot?

  • A. Snowpipe auto-ingest only supports CSV files. Convert your JSON data to CSV format before loading.
  • B. The Snowflake virtual warehouse associated with the pipe is undersized. Increase the warehouse size to improve ingestion performance.
  • C. The S3 bucket is not in the same region as the Snowflake account. Ensure the S3 bucket and Snowflake account are in the same region to reduce network latency.
  • D. There is a backlog of files in the internal Snowflake queue waiting to be processed. Monitor the 'SYSTEM$PIPE STATUS' function and consider increasing the 'MAX CONCURRENCY' parameter (if applicable, based on underlying infrastructure considerations) on the pipe definition.
  • E. Snowflake's internal metadata cache is out of sync. Run 'ALTER PIPE SALES PIPE to refresh the cache.

Answer: D

Explanation:
While warehouse size (A) can impact performance, latency in auto-ingest scenarios is more frequently tied to the processing of files within Snowflake's internal queue. The 'SYSTEM$PIPE STATUS function provides insights into the queue's state. Increasing 'MAX CONCURRENCY' (if supported and resource constraints allow) can help process more files concurrently. Option C is incorrect as Snowpipe supports JSON. Option D, regional disparity would impact performance but is less likely to cause significant delays if the configuration is correct. Option E is incorrect; 'ALTER PIPE ... REFRESH' primarily applies to schema evolution or changes in the underlying data structure, not queue processing issues.


NEW QUESTION # 277
You've created a JavaScript stored procedure using Snowpark to transform data'. The stored procedure is failing, and you suspect an issue with how Snowpark is handling null values during a join operation. Given two Snowpark DataFrames, and 'df2 , what is the expected behavior when performing an inner join on a column containing null values in both DataFrames, and how can you mitigate potential issues?

  • A. The inner join will exclude rows where the join column is null in either DataFrame. To include these rows, you must use a full outer join instead.
  • B. The inner join will treat null values as equal, resulting in rows where the join column is null in both DataFrames being included in the result. To avoid this, you should filter out null values before the join.
  • C. The inner join will automatically exclude rows where the join column is null in either DataFrame. There is no need for explicit null handling.
  • D. The behavior of the inner join with null values is undefined and may vary depending on the data types and the specific version of Snowpark. Explicit null handling is always required.
  • E. Inner Join will not throw an error, and will exclude the rows where join column is null. If you need to join records with null values, pre-processing dataframes using to replace null with a valid sentinel value before performing the join is one way to handle this.

Answer: E

Explanation:
Option E is correct. Inner joins in Snowpark, like in standard SQL, exclude rows where the join column is null in either DataFrame. While an error isn't thrown, the rows are silently dropped. To handle null values in join columns, pre-processing using .na.fill()' is a common approach. Filling NA with sentinel values provides a workaround, allowing you to effectively include records where the join column was originally null. Using .na.fill' makes the join possible. Options A, B, C, and D are incorrect as they misrepresent the standard behavior of inner joins with null values.


NEW QUESTION # 278
You have a table 'ORDERS in your Snowflake database. You are implementing a new data transformation pipeline. Before deploying the pipeline to production, you want to validate the changes in a development environment. You decide to use Time Travel to create a snapshot of the 'ORDERS' table before the transformation and compare it with the transformed data'. Which sequence of SQL commands would best facilitate this validation, assuming your development database and schema structure mirrors production?

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: B

Explanation:
Option D is the most complete and reliable approach. It first creates a backup table in production using Time Travel before the transformation. Then, clones both the original (pre-transformation) table and the transformed table into the development environment. Finally, it compares these cloned tables to validate the transformation. The use of LAST_QUERY_ID() would not be suitable since the clone is required in the same session, while TIMESTAMP based approach is less reliable due to the lack of synchronisation on when the query was executed.


NEW QUESTION # 279
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