Google Professional Cloud Developer Question 185
Select 2Google Cloud PlatformYou are designing a serverless application on Google Cloud that processes large volumes of real-time streaming data generated by IoT sensors. The data must be processed, stored, and analyzed with minimal latency and cost. Which combination of Google Cloud services should you use to handle the data volume effectively?
- A
Cloud Pub/Sub for ingestion, Cloud Dataflow for processing, and BigQuery for storage and analysis
- B
Cloud Functions for ingestion, Cloud Spanner for processing, and Cloud SQL for storage
- C
Cloud Pub/Sub for ingestion, Cloud Dataproc for processing, and Cloud Storage for archival storage
- D
Cloud Pub/Sub for ingestion, Cloud Dataflow for processing, and Cloud Storage for archival storage
- E
Firebase Realtime Database for ingestion, Cloud Run for processing, and Bigtable for storage
Show answer and explanation
Correct answers: A, D
Explanation
Handling large volumes of real-time streaming data requires services that are scalable, cost-effective, and optimized for the nature of the data. Cloud Pub/Sub is designed for high-throughput ingestion, Cloud Dataflow is ideal for real-time and batch processing of streaming data, and BigQuery or Cloud Storage can handle storage and analysis depending on the use case. The correct combinations in this scenario are Cloud Pub/Sub, Cloud Dataflow, and BigQuery for analysis, or Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage for archival needs.
- A. Correct.
This is a correct combination as Cloud Pub/Sub is ideal for high-throughput ingestion, Cloud Dataflow is suitable for scalable real-time processing, and BigQuery can efficiently handle storage and analysis of large datasets.
- B. Incorrect.
This option is incorrect because Cloud Functions is not designed for high-throughput ingestion, and Cloud Spanner is a relational database not suitable for processing streaming data.
- C. Incorrect.
This option is partially correct in terms of using Cloud Pub/Sub for ingestion and Cloud Dataproc for processing. However, Cloud Storage is better suited for archival rather than real-time analytics and querying.
- D. Correct.
This is a correct combination as Cloud Pub/Sub is effective for ingestion, Cloud Dataflow for real-time data processing, and Cloud Storage can serve as an affordable solution for archival of large datasets.
- E. Incorrect.
This option is incorrect because Firebase Realtime Database is not designed for high-volume ingestion, and Cloud Run is not optimized for stream processing at scale.