Google Cloud Professional Data Engineer Exam 2025 – 400 Free Practice Questions to Pass the Exam

Choosing data lifecycle management techniques and selecting the appropriate storage classes is essential for ensuring efficient storage of data in Google Cloud. Data lifecycle management allows engineers to automate the transition of data between different storage classes based on its age, frequency of access, and other criteria. This means that less frequently accessed data can be moved to lower-cost storage options, helping to optimize storage costs while ensuring that the most critical data remains accessible in faster, more expensive storage.

Google Cloud Storage offers various storage classes—such as Standard, Nearline, Coldline, and Archive—each designed for different use cases regarding access frequency and availability needs. By effectively utilizing these storage classes in conjunction with lifecycle management policies, a Data Engineer can achieve a more cost-effective and efficient data storage solution overall.

Data compression techniques may reduce the physical space required to store data, but relying solely on them does not take into account the broader context of storage efficiency, such as access patterns and cost management. Limiting data storage to a single region can hinder data availability and redundancy strategies, making the system less resilient. Relying on automatic scaling addresses compute resource needs but does not explicitly pertain to optimizing data storage strategies. Thus, integrating lifecycle management with appropriate storage class selection is the most holistic approach for efficient