DEA-C02 Exam Dumps | You are working on a Snowpark Python application that needs to process a stream of data from Kafka, perform

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Question 140/149

You are working on a Snowpark Python application that needs to process a stream of data from Kafka, perform real-time aggregations, and store the results in a Snowflake table. The data stream is highly variable, with occasional spikes in traffic that overwhelm your current Snowpark setup, leading to significant latency in processing. Which of the following strategies, either individually or in combination, would be MOST effective to handle these traffic spikes and ensure near real-time processing?

A. Implement a message queuing system (e.g., RabbitMQ, Kafka) between Kafka and your Snowpark application to buffer incoming data during traffic spikes.

B. Configure the Snowflake warehouse used by your Snowpark application to use auto-suspend and auto-resume with a short auto-suspend time to minimize costs during periods of low traffic.

C. Use Snowpark's async actions (e.g., to offload data processing to separate threads or processes, allowing your main Snowpark application to continue receiving data.

D. Implement dynamic warehouse scaling. Utilize Snowflake's Resource Monitors and the ability to programmatically resize warehouses through Snowpark. Monitor the queue depth or latency of your Snowpark application, and dynamically scale up the warehouse size when thresholds are exceeded. Then, scale it back down when traffic subsides.

E. Use 'CACHE RESULT for all queries in snowpark that use Kafka

Correct Answer: A,D

Options A and D offer the best approach. Implementing a message queue (A) provides a buffer for incoming data during spikes, preventing your Snowpark application from being overwhelmed. Dynamic warehouse scaling (D) allows you to automatically increase the compute resources available to your Snowpark application when needed, ensuring it can handle the increased workload. Auto suspend/resume (B) is good for cost optimization but doesn't address the processing capacity during spikes. Async actions (C) can help, but are not as scalable or resilient as a proper message queue combined with dynamic warehouse scaling. Caching results (E) is irrelevant since the data from Kafka is always changing.

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