
Answer-first summary for fast verification
Answer: Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.
The correct answer is C because BigQuery is the most suitable solution for HRL's requirements. BigQuery excels at handling large-scale historical data storage cost-effectively, supports schema evolution by allowing column additions without downtime, and enables efficient querying for model training on specific seasons through partitioning. It also integrates well with Google's ML services for predictive analytics. Option A (Firestore) is less optimal as it's a document database better for real-time applications rather than analytical workloads. Option B (Cloud Spanner) is overkill and expensive for this use case, as it's designed for globally distributed transactional workloads. Option D (Cloud SQL) lacks the scalability needed for growing telemetry data and managing multiple database instances adds unnecessary complexity.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
For the Helicopter Racing League (HRL) case study, considering the need for a cost-effective solution to store all historical race data like telemetry, train models exclusively on the previous season's data, and plan for future growth in data volume and complexity, what data solution should you propose?
A
Use Firestore for its scalable and flexible document-based database. Use collections to aggregate race data by season and event.
B
Use Cloud Spanner for its scalability and ability to version schemas with zero downtime. Split race data using season as a primary key.
C
Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.
D
Use Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Use separate database instances for each season.