Google Professional Data Engineer
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NO.15
A. Create a Memorystore instance with a high availability (HA) configuration
B. Write votes to a Pub Sub topic and have Cloud Functions subscribe to it and write votes to BigQuery
C. 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
Answer: C
D. Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas
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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.
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