Google Associate Cloud Engineer Question 142
Select 2Google Cloud PlatformYou are tasked with deploying a data product that processes and analyzes large datasets on Google Cloud Platform. The data product must be scalable, handle batch processing, and integrate with BigQuery for analytics. Which combination of Google Cloud services should you use to efficiently deploy this data product?
- A
Google Cloud Dataflow and BigQuery
- B
Google Cloud Dataproc and Cloud Functions
- C
Google Compute Engine and Cloud Storage
- D
Google Cloud Pub/Sub and BigQuery
- E
Google Kubernetes Engine and Cloud SQL
Show answer and explanation
Correct answers: A, D
Explanation
For deploying a data product that requires scalable batch processing and integration with BigQuery for analytics, using Google Cloud Dataflow provides a managed solution for data processing tasks, and BigQuery can efficiently handle analytics. Additionally, Google Cloud Pub/Sub serves as a reliable messaging service to ingest data in real-time and can complement BigQuery for analytics. These combinations offer a robust and scalable solution for deploying a data product on Google Cloud.
- A. Correct.
Google Cloud Dataflow is a fully managed service for stream and batch processing that works well with BigQuery for analytics, making it suitable for scalable data processing tasks.
- B. Incorrect.
Google Cloud Dataproc is good for running Hadoop and Spark jobs, but Cloud Functions is not ideal for handling large-scale, batch processing workloads.
- C. Incorrect.
Google Compute Engine provides raw virtual machines and Cloud Storage is good for storing data, but they don't directly offer the high-level data processing and analytics capabilities required.
- D. Correct.
Google Cloud Pub/Sub is a messaging service that can handle real-time data ingestion, which complements BigQuery for analytics purposes.
- E. Incorrect.
Google Kubernetes Engine is a managed container service and Cloud SQL is a relational database service. While they can be used for various applications, they are not the most efficient combination for scalable batch processing and big data analytics.