Google Professional Machine Learning Engineer
Get started today
Ultimate access to all questions.
In the context of designing a scalable and fault-tolerant distributed computing system for a global e-commerce platform, which of the following strategies is MOST effective for ensuring high availability and data integrity across geographically dispersed data centers? Choose the best option.
In the context of designing a scalable and fault-tolerant distributed computing system for a global e-commerce platform, which of the following strategies is MOST effective for ensuring high availability and data integrity across geographically dispersed data centers? Choose the best option.
Explanation:
Correct Options: C. Data replication and A. Single-thread processing
Explanation:
Data replication is a cornerstone strategy for achieving fault tolerance in distributed systems, ensuring data availability and integrity by maintaining copies across multiple nodes. Single-thread processing, while not directly related to fault tolerance, can simplify system architecture but does not inherently provide fault tolerance. The combination of these strategies is not typically recommended as single-thread processing lacks the redundancy needed for fault tolerance.
Why other options are incorrect:
- B. Gradient boosting: This is a machine learning technique for improving model accuracy, not a fault tolerance strategy.
- D. Model regularization: This technique is used to prevent overfitting in machine learning models and is unrelated to fault tolerance in distributed systems.
- E. Both C and A: While data replication is effective for fault tolerance, single-thread processing does not contribute to fault tolerance and thus this option is incorrect.