Google Professional Data Engineer exam dumps

Google Professional Data Engineer practice question 149 of 279

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

Google Professional Data Engineer Question 149

Select 3Google Cloud Platform

Your company wants to build a data pipeline to process and analyze e-commerce transaction data in near real-time. The business requirements include ensuring high scalability, low latency, fault tolerance, and the ability to generate daily sales reports. As a Data Engineer, what key factors should you consider when mapping these business requirements to a Google Cloud solution?

  1. A

    Choose a managed stream processing service like Dataflow to handle real-time data processing.

  2. B

    Use BigQuery for storing and analyzing large volumes of processed data.

  3. C

    Deploy a self-managed Apache Kafka cluster on Compute Engine instances for data ingestion.

  4. D

    Design the pipeline to use Cloud Storage for real-time transaction processing.

  5. E

    Implement a fault-tolerant architecture with Pub/Sub for data ingestion and message durability.

Show answer and explanation

Correct answers: A, B, E

Explanation

When mapping business requirements to a Google Cloud solution, it is important to choose managed services that align with the requirements of scalability, low latency, fault tolerance, and reporting. Dataflow is ideal for real-time stream processing, BigQuery supports scalable analytics and reporting, and Pub/Sub ensures reliable and fault-tolerant data ingestion. Self-managed solutions like Apache Kafka and inappropriate services like Cloud Storage for real-time processing do not align with the specified requirements.

  • A. Correct.

    Dataflow is a fully managed stream and batch processing service that is highly scalable and suitable for handling real-time data pipelines. It aligns with the requirement of low latency and fault tolerance.

  • B. Correct.

    BigQuery is an ideal choice for storing and analyzing large datasets, including daily sales reports. It provides scalability and fast query performance, meeting the reporting requirement.

  • C. Incorrect.

    While Apache Kafka is a powerful tool for data ingestion, deploying it on Compute Engine requires significant management overhead. A managed service like Pub/Sub is a better fit for scalability and fault tolerance.

  • D. Incorrect.

    Cloud Storage is not designed for real-time transaction processing. It is more suited for batch processing, archival, or storing raw data.

  • E. Correct.

    Pub/Sub is a managed message broker that guarantees message durability and supports fault-tolerant ingestion, aligning with the business need for scalability and reliability.

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