Professional Cloud Architect · Free Practice Question Hard
Question 19
You are a cloud architect for a data analytics company that processes large volumes of structured and unstructured data. The data is ingested from various sources, processed, and stored for analysis. The company requires a scalable and high-performance storage solution that supports large datasets and provides efficient access for running complex queries. Data needs to be retained for several years due to compliance requirements, but access frequency decreases significantly after the first year. Which storage strategy would best fulfill the company’s needs?
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A
Store all data in Google Cloud Bigtable, using one table per dataset. Implement row key design to optimize read performance and use Garbage Collection policies to manage data retention.
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B
Utilize Google Cloud Filestore with HDD storage for raw data, and Google Cloud BigQuery for processed datasets. Implement snapshots in Filestore for backup and use lifecycle policies in BigQuery to manage long-term storage costs.
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C
Deploy Google Cloud BigQuery for storing all datasets, taking advantage of partitioned tables to optimize query performance. Use table expiration to automatically manage data retention based on compliance requirements.
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D
Use Google Cloud Storage with a regional bucket and Standard storage class for all datasets. Implement Object Lifecycle Management to transition data to Nearline storage after one year and delete it after five years.
Reveal correct answer
Correct answer: C
A.
While Google Cloud Bigtable is excellent for low-latency access to large datasets, it is not optimized for complex queries across large datasets, which are better suited to BigQuery. Additionally, Garbage Collection in Bigtable is more focused on managing versioned data rather than compliance-driven retention.
B.
While Filestore provides scalable file storage, it’s not well-suited for the kind of large-scale data analytics performed by BigQuery. Combining Filestore with BigQuery adds unnecessary complexity and costs without significant benefits. Snapshots and lifecycle policies in this context do not provide the same robust data management as BigQuery’s native capabilities.
C.
Google Cloud BigQuery is designed for large-scale data storage and analytics, providing high-performance query capabilities and scalability. Partitioned tables help optimize query performance by allowing you to access only the relevant portions of data, reducing costs and improving efficiency. Table expiration automates data lifecycle management, ensuring compliance with retention policies while keeping storage costs in check.
D.
Google Cloud Storage is more suited for object storage, not for scenarios requiring frequent, complex querying of large datasets. The use of Nearline storage after one year might not be optimal, as it is better suited for infrequently accessed data, and Standard storage is not designed for the high-performance analytics required by the company.
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