Google Professional Cloud Architect
Get started today
Ultimate access to all questions.
The Helicopter Racing League (HRL) is migrating their existing service to a new platform to expand their use of managed AI and ML services for race predictions, and to improve content delivery to emerging regions. HRL's current environment involves storing historical race data and performing video encoding and transcoding in the cloud. HRL aims to keep all historical race records, train predictive models using only the previous season's data, and accommodate future data growth. Considering the need for a cost-effective solution that aligns with HRL's business requirements and the goals expressed by CEO S. Hawke, what should you do?
The Helicopter Racing League (HRL) is migrating their existing service to a new platform to expand their use of managed AI and ML services for race predictions, and to improve content delivery to emerging regions. HRL's current environment involves storing historical race data and performing video encoding and transcoding in the cloud. HRL aims to keep all historical race records, train predictive models using only the previous season's data, and accommodate future data growth. Considering the need for a cost-effective solution that aligns with HRL's business requirements and the goals expressed by CEO S. Hawke, what should you do?
Explanation:
The correct answer is C: Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season. BigQuery is a highly scalable and cost-effective data warehouse that is well-suited for handling large volumes of data and performing complex queries. By partitioning the race data based on season, HRL can efficiently manage and query historical records. BigQuery's flexibility in schema evolution allows adding new columns as data collection requirements grow over time, making it a strong fit for HRL's needs.