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Candidates who crack the DEA-C02 examination of the Snowflake DEA-C02 certification validate their worth in the sector of information technology. The Snowflake DEA-C02 credential is evidence of their talent. Reputed firms hire these talented people for high-paying jobs. To get the SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) certification, it is essential to clear the SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) test. For this task, you need to update SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) preparation material to get success.

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q17-Q22):

NEW QUESTION # 17
You are designing a continuous data pipeline to load data from AWS S3 into Snowflake. The data arrives in near real-time, and you need to ensure low latency and minimal impact on your Snowflake warehouse. You plan to use Snowflake Tasks and Streams. Which of the following approaches would provide the most efficient and cost-effective solution for this scenario, considering data freshness and resource utilization?

A. Configure an AWS SQS queue to receive S3 event notifications whenever a new file is uploaded. Use a Lambda function triggered by the SQS queue to invoke a Snowflake stored procedure. This stored procedure executes a COPY INTO command to load the specific file into Snowflake. Use 'ON ERROR = CONTINUE' during COPY INTO.
B. Create a single, root Snowflake Task that triggers every 5 minutes, executing a COPY INTO command to load all new data from the S3 bucket into a staging table, followed by a MERGE statement to update the target table. Use 'VALIDATE ( STAGE NAME '0'.////' before COPY INTO.
C. Create a Pipe object in Snowflake using Snowpipe and configure the S3 bucket for event notifications to the Snowflake-provided SQS queue. Monitor the Snowpipe status using 'SYSTEM$PIPE STATUS and address any errors by manually retrying failed loads with 'ALTER PIPE REFRESH;'
D. Create a Stream on the target table and a Snowflake Task that runs every minute. The task executes a MERGE statement to apply changes from the Stream to the target table, filtering the Stream data using the 'SYSTEM$STREAM GET TABLE TIMESTAMP function to process only newly arrived data since the last task execution. Use 'WHEN SYSTEM$STREAM HAS to run the Task.
E. Create a Stream on the target table and a Snowflake Task. The task executes a COPY INTO command into a staging table when the Stream has data and then a MERGE statement. Schedule the task to run continuously with 'WHEN SYSTEM$STREAM HAS but limit the 'WAREHOUSE SIZE' to

Answer: C

Explanation:
Snowpipe is specifically designed for continuous data ingestion with minimal latency. It leverages event notifications and serverless compute resources, making it more efficient than polling-based approaches (Task + Stream) or Lambda function invocations. The use of 'SYSTEM$PIPE STATUS' for monitoring and 'ALTER PIPE ... REFRESH' for manual retries provides better control and error handling compared to manual COPY INTO commands and MERGE statements. Option A is inefficient, B is complex, C might have performance issues due to high concurrency and E requires more coding and Stream-related management.

NEW QUESTION # 18
Which of the following statements are true regarding using Dynamic Data Masking and Column-Level Security in Snowflake? (Select all that apply)

A. Dynamic Data Masking policies can reference external tables directly without requiring special grants.
B. Dynamic Data Masking is applied at query runtime, while Column-Level Security through views or roles is applied when the object is created.
C. Using both Dynamic Data Masking and Column-Level Security (e.g. views) on the same column is redundant and will result in an error.
D. Column-Level Security via views provides more fine-grained control over data access compared to Dynamic Data Masking.
E. Dynamic Data Masking can be used to apply different masking rules based on the user's role, IP address, or other contextual factors.

Answer: B,E

Explanation:
Option A is correct because Dynamic Data Masking applies policies at query runtime based on context, while view-based security is defined when the view is created. Option B is correct because Dynamic Data Masking uses contextual functions like 'CURRENT and to tailor masking. Option C is incorrect; masking policies offer fine-grained control. Option D is incorrect; referencing external objects require appropriate grants. Option E is incorrect; While using both is possible, care must be taken to ensure that masking happens correctly.

NEW QUESTION # 19
A data engineering team is building a real-time data pipeline in Snowflake. Data arrives continuously and needs to be processed with minimal latency. The team is using Snowflake Streams and Tasks for incremental data processing. However, they are encountering issues where the tasks are sometimes skipped or delayed, leading to data inconsistencies. Which combination of actions would BEST address these issues and ensure reliable near real-time data processing?

A. Adjust the ' ERROR_INTEGRATION' parameter on the task definition to send notifications when tasks fail. This allows for manual intervention but does not prevent skipping.
B. Configure the tasks to run using a serverless compute model (Snowflake-managed compute). Ensure the parameter is set to a higher value and implement error handling within the task using TRY/CATCH blocks.
C. Monitor the 'TASK HISTORY view regularly to identify skipped or delayed tasks and manually re-run them as needed. This is a reactive approach and does not prevent future occurrences.
D. Increase the warehouse size to ensure sufficient compute resources. This will prevent tasks from being skipped due to resource contention.
E. Disable task scheduling and rely solely on Snowflake's Auto-Resume feature for warehouses. This simplifies the pipeline and reduces the chance of errors.

Answer: B

Explanation:
Option C is the best solution. Serverless compute allows Snowflake to automatically manage resources for the tasks, ensuring they are not skipped due to insufficient compute. Setting 'SUSPEND TASK AFTER NUM FAILURES' avoids immediate suspension after a transient failure, and TRY/CATCH allows for robust error handling. Increasing warehouse size (A) may help, but serverless provides better elasticity. B only provides notification. D is incorrect as disabling tasks removes automation. E is a reactive approach.

NEW QUESTION # 20
You are using Snowpipe to load data from an AWS S3 bucket into Snowflake. The data files are compressed using GZIP and are being delivered frequently. You have observed that the pipe's backlog is increasing and data latency is becoming unacceptable. Which of the following actions could you take to improve Snowpipe's performance? (Select all that apply)

A. Reduce the number of columns in the target Snowflake table. Fewer columns reduce the overhead of data loading.
B. Check if the target table has any active clustering keys defined which could be causing slow down
C. Optimize the file size of the data files in S3. Smaller files are processed faster by Snowpipe.
D. Ensure that the S3 event notifications are correctly configured and that there are no errors in the event delivery mechanism.
E. Increase the virtual warehouse size associated with the pipe.

Answer: B,D,E

Explanation:
Increasing the warehouse size allows Snowpipe to process more data in parallel (A). Correct S3 event notification setup ensures that Snowpipe is promptly notified of new files (C). Snowpipe picks up data only when notified using SNS service, if notifications delay, data loading latency will increase. Active clustering keys can slow down ingest when data is not well-sorted. It needs to re-arrange the table constantly as data comes in(E). While optimizing file size can help to some extent, drastically reducing the number of columns in a target table is usually not a practical approach to improve Snowpipe performance (D). While small files may seem to be better, small files also can cause problems related to too many files to be loaded. Having larger files that can be split to several chunks of parallel data loading will be better.

NEW QUESTION # 21
You are tasked with migrating data from a legacy SQL Server database to Snowflake. One of the tables, 'ORDERS' , contains a column 'ORDER DETAILS that holds concatenated string data representing multiple order items. The data is formatted as 'iteml :qtyl ;item2:qty2;...'. You need to transform this string data into a JSON array of objects, where each object represents an item with 'name' and 'quantity' fields. Which of the following steps and functions would you use in Snowflake to achieve this transformation, in addition to loading the data?

A. Use ' to extract item names and quantities, then use 'ARRAY_CONSTRUCT and 'OBJECT_CONSTRUCT to create the JSON array.
B. Use 'SPLIT with ';' as delimiter, then apply 'SPLIT again with ':' as delimiter. Finally, construct the JSON array using 'ARRAY_AGG' and 'OBJECT CONSTRUCT
C. Use ' STRTOK TO ARRAY' to split the string into an array, then iterate through the array using a JavaScript UDF to create the JSON objects.
D. Use to split the string into rows, then use 'SPLIT to separate item name and quantity, and finally use 'OBJECT_CONSTRUCT and to create the JSON array.
E. Utilize a Java UDF to parse the string and directly generate the JSON array.

Answer: B,D

Explanation:
Options A and D correctly outline the process. (A) and multiple 'SPLIT calls (D) are valid approaches to break down the concatenated string. Then, 'OBJECT_CONSTRUCT builds the individual JSON objects, and aggregates them into a JSON array. While Javascript or Java UDFs (C, E) could solve the problem, they are generally less efficient than Snowflake's built-in functions. (B) might work but is overkill for this simple splitting task, also you would still need to combine the extracted arrays for items and quantities.

NEW QUESTION # 22
......

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