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

Using BigQuery as a data warehouse offers significant advantages, particularly its ability to handle large datasets quickly and cost-effectively. BigQuery utilizes a serverless architecture, which means users do not need to manage infrastructure, allowing them to focus on analyzing data rather than on maintaining and scaling hardware. This system is optimized for large-scale data analytics, where it can automatically allocate resources as needed, meaning queries can run on massive datasets efficiently.

Additionally, BigQuery employs a columnar storage format and utilizes distributed computing, enabling fast query performance. It also allows for flexible pricing options, such as on-demand pricing or flat-rate pricing, which can lead to cost savings based on usage patterns. Users benefit from increased speed and efficiency in processing, as well as the cost-effectiveness of Google's infrastructure when dealing with various data workloads.

The other options do not highlight the key benefits of using BigQuery accurately. Real-time processing is a feature but not the primary benefit, extensive setup and maintenance are contrary to the serverless nature of BigQuery, and concerns about data security do not reflect the robust security features integrated within the platform.