Cloud Digital Leader Google Cloud Products and Services Practice Question
A startup is building a mobile app that needs to store user profiles and preferences with low latency. The data is unstructured and frequently read/written. Which TWO Google Cloud database services are most suitable? (Choose 2)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Bigtable
Firestore is a NoSQL document database optimised for mobile/web apps with low-latency reads/writes. Bigtable is also NoSQL but designed for high-throughput time-series data. Memorystore is a cache, not a primary database. Cloud SQL is relational. The best options are Firestore for user profiles and preferences (unstructured, low-latency) and Bigtable if the scale is very high, but typically Firestore is the go-to for mobile. However, the question asks for TWO; Bigtable is also NoSQL and can be used for user data but is overkill. Alternatively, Firestore and Memorystore can be combined: Firestore as primary, Memorystore as cache. But Memorystore is a cache, not a database. The question says 'database services'. So likely Firestore and Bigtable are both NoSQL databases. But for user profiles, Firestore is more appropriate. I'll select Firestore and Bigtable as two NoSQL options.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Bigtable
Why this is correct
Bigtable is a fully managed, wide-column NoSQL database designed for massive scale, handling millions of writes per second with sub-10ms latency. It is ideal for high-throughput, time-series, or IoT data where each user profile is keyed by a row key and columns can be dynamically added. However, its strengths are in analytical and operational workloads with large volumes, not necessarily the best fit for a simple mobile app user profile, yet it works if data volume is extremely high and latency is critical.
- ✗
Memorystore
Why it's wrong here
Memorystore is an in-memory data store (Redis/Memcached) that provides microsecond latency but does not persist data by default; it is used for caching session data or frequently accessed objects, not as a durable system of record. Relying on it as the primary store risks data loss on failure or restarts, so it cannot replace a persistent database for user profiles. It complements Firestore or Bigtable by caching reads, but is wrong as the sole storage layer.
- ✗
Cloud Spanner
Why it's wrong here
Cloud Spanner is a horizontally scalable relational database that offers strong consistency, ACID transactions, and SQL at global scale. For a startup storing simple user profiles, it requires predefined schemas, fixed columns, and higher cost per node, making it overkill and more expensive than necessary. While it supports high scalability, its complexity and operational overhead are not justified for lightweight, unstructured mobile app data.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL uses a relational schema requiring predefined tables and fixed columns, which prevents the storage of unstructured data without constant migrations. It is an ideal selection for applications requiring ACID compliance and complex joins on structured datasets, such as financial transaction systems or ERP software where data integrity and relational consistency are paramount.
- ✓
Firestore
Why this is correct
Firestore is a NoSQL document database built for mobile and web apps, offering flexible, hierarchical data models with real-time sync and offline support. It automatically scales and provides client SDKs that simplify storing and querying user profiles, avoiding schema migrations. With low latency and free-tier options, it is a natural fit for this use case, while Bigtable would be chosen only if the data volume becomes extremely large or the access patterns are more analytical.
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Cloud Digital Transformation
Key term
Bigtable
Bigtable is Google's fully managed, scalable NoSQL database service designed for large analytical and operational workloads, handling petabytes of data with low latency.
Key term
Memorystore
A fully managed in-memory data store service that provides Redis and Memcached for caching, session storage, and real-time data processing.
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