Google Cloud Professional Data Engineer Exam

Which method helps in managing resource allocation for high-frequency data processing tasks in BigQuery efficiently?

1. Batch processing.

2. Interactive queries.

3. Partitioned tables.

4. Routine queries.

The correct answer is: Batch processing.

The most suitable method for managing resource allocation for high-frequency data processing tasks in BigQuery is through partitioned tables. Partitioned tables enhance query performance and efficiency by allowing data to be divided into segments based on a particular column, most commonly a timestamp. This means that queries can be limited to specific partitions instead of scanning the entire dataset, resulting in reduced compute costs and faster execution times. For high-frequency data processing tasks where data is ingested continuously and needs to be analyzed promptly, partitioned tables enable more effective use of resources. They help in reducing the amount of data scanned and facilitate quicker access to the relevant segments of data, which is crucial for tasks that require near real-time processing. Batch processing generally involves processing a large amount of data at once rather than handling individual requests in real-time and is less optimal for high-frequency tasks. Interactive queries are intended for user-driven needs and may not be designed for efficiency in high-frequency scenarios where constant, automated data processing is required. Routine queries may provide consistency in terms of execution but do not inherently focus on resource allocation or efficiency for high-frequency workloads. Utilizing partitioned tables is the most effective way to handle resource allocation for high-frequency data processing in BigQuery.