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Google Professional Cloud Developer practice question 194 of 481

Professional Cloud Developer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Cloud Developer Question 194

Select 2Google Cloud Platform

Your team is building a cloud-based application that processes high-frequency customer transactions in real time. These transactions need to be processed quickly and securely, while the application also supports detailed analytics on historical transaction data for business intelligence. Which of the following strategies would be the most appropriate for handling this use case?

  1. A

    Use Cloud Spanner for online transaction processing (OLTP) and BigQuery for data warehousing and analytics.

  2. B

    Leverage Firestore to handle both real-time transactions and analytics in a single database.

  3. C

    Implement Cloud SQL for transactional processing and export data to BigQuery for analytics.

  4. D

    Use Memorystore for both transactional operations and data warehousing needs.

  5. E

    Utilize BigQuery for both real-time transaction processing and historical data analytics.

Show answer and explanation

Correct answers: A, C

Explanation

In a scenario where both online transaction processing (OLTP) and data warehousing/analytics are required, a combination of purpose-built tools is ideal. Cloud Spanner or Cloud SQL can handle OLTP workloads effectively while BigQuery can be used for analyzing historical data. This ensures both real-time processing and analytical needs are met using Google Cloud's specialized services.

  • A. Correct.

    Cloud Spanner is highly suitable for OLTP because of its scalability, consistency, and strong transactional support, while BigQuery is optimized for data warehousing and analytics. This combination effectively addresses both real-time transaction processing and historical analytics.

  • B. Incorrect.

    Firestore is a NoSQL database optimized for hierarchical and real-time applications, but it is not designed for heavy-duty OLTP or complex data warehousing and analytics use cases.

  • C. Correct.

    Cloud SQL is a relational database service that is well-suited for transactional workloads. Exporting its data to BigQuery allows for advanced analytics, making this a valid strategy.

  • D. Incorrect.

    Memorystore is an in-memory database service optimized for caching and real-time data retrieval but is not suitable for OLTP or data warehousing and analytics.

  • E. Incorrect.

    BigQuery is a powerful data warehousing tool for analytics but is not designed for real-time transactional processing, making it unsuitable for the OLTP requirements in this scenario.

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