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Explanation:

Explanation

Option C is the correct solution because it provides:

Real-time processing: Using Pub/Sub for streaming data ingestion and Bigtable for real-time querying of voting results
Batch processing: Using Dataflow to load data into BigQuery for historical analysis and reporting
Cost optimization: Shutting down the Bigtable instance when voting concludes to avoid unnecessary costs
Scalability: Pub/Sub and Dataflow can handle high-volume streaming data efficiently

Why other options are incorrect:

Option A (Memorystore): Redis-based Memorystore is not ideal for large-scale voting data and lacks the analytical capabilities of BigQuery
Option B (Cloud Functions + BigQuery only): Missing real-time processing capabilities and the ability to handle high-volume streaming data efficiently
Option D (Cloud SQL): Relational databases like PostgreSQL are not optimized for high-volume streaming data and real-time analytics at scale

This solution leverages Google Cloud's serverless and managed services to create a scalable, cost-effective voting system that supports both real-time and batch processing requirements.

Explanation:

Explanation

Option C is the correct solution because it provides:

Real-time processing: Using Pub/Sub for streaming data ingestion and Bigtable for real-time querying of voting results
Batch processing: Using Dataflow to load data into BigQuery for historical analysis and reporting
Cost optimization: Shutting down the Bigtable instance when voting concludes to avoid unnecessary costs
Scalability: Pub/Sub and Dataflow can handle high-volume streaming data efficiently

Why other options are incorrect:

Option A (Memorystore): Redis-based Memorystore is not ideal for large-scale voting data and lacks the analytical capabilities of BigQuery
Option B (Cloud Functions + BigQuery only): Missing real-time processing capabilities and the ability to handle high-volume streaming data efficiently
Option D (Cloud SQL): Relational databases like PostgreSQL are not optimized for high-volume streaming data and real-time analytics at scale

This solution leverages Google Cloud's serverless and managed services to create a scalable, cost-effective voting system that supports both real-time and batch processing requirements.

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NO.15

A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open. After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

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Last updated: September 29, 2026 at 14:56

Create a Memorystore instance with a high availability (HA) configuration

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Write votes to a Pub Sub topic and have Cloud Functions subscribe to it and write votes to BigQuery

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Write votes to a Pub/Sub topic and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludes

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Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas

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