Cloud Digital Leader Google Cloud Products and Services Practice Question
A company is building a real-time leaderboard for an online game using Google Cloud. They need a database that can handle millions of updates per second with low latency and serve the current top scores. Which TWO services should they use together? (Choose 2)
⚠ Common exam trap
GCDL often tests whether candidates recognize that Bigtable alone cannot serve sorted leaderboard queries efficiently, so answers that omit Redis (or substitute Firestore/BigQuery) fail the low-latency read requirement.
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
✓
Cloud Bigtable
Cloud Bigtable (C) is correct because it is a fully managed, horizontally scalable NoSQL database designed for high-throughput, low-latency workloads, making it suitable for ingesting millions of score updates per second and durably storing the leaderboard data. Memorystore for Redis (D) is correct because it provides an in-memory data store with sub-millisecond latency, ideal for serving the current top scores in real time and handling rapid ranking operations. Together, Bigtable handles massive write throughput while Redis serves the hot leaderboard reads. Cloud SQL (A) is not appropriate because it is a relational database that cannot scale to millions of updates per second with low latency. Firestore (B) is not ideal because, while it is a NoSQL document database, it is optimized for mobile/web app synchronization rather than extreme write throughput. BigQuery (E) is not suitable because it is an analytics data warehouse designed for large-scale queries, not real-time transactional updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL is a relational database with a single-primary write model, which caps effective write throughput at roughly tens of thousands of transactions per second at the high end. Even with read replicas, all writes must serialize through the primary, making it impossible to absorb millions of concurrent writes per second for a real-time leaderboard. Additionally, maintaining such a write spike requires aggressive connection pooling and sharding that undermines the SQL simplicity without ever guaranteeing the needed latency ceiling.
- ✗
Firestore
Why it's wrong here
Firestore is a document database with a per-document write limit of about 1 write per second and a sustained database-wide write ceiling that scales with documents but is not designed for millions of writes per second in a single hot key space. A real-time leaderboard constantly updates a small set of high-score records, creating severe write contention on those documents and causing hot-spot throttling or quota exhaustion. Firestore is better suited for moderate, distributed write patterns and near-real-time sync, not ultra-high-frequency updates to shared ranking keys.
- ✓
Cloud Bigtable
Why this is correct
Cloud Bigtable is a NoSQL wide-column database engineered for high-throughput, low-latency writes of millions of rows per second when scaling with CPU and storage nodes. Its distributed storage engine appends writes to SSTables with memtable buffering, giving consistent single-digit-millisecond latencies and linear write scaling across a cluster without a single bottleneck. For a real-time leaderboard, Bigtable's row-key design (like inverted scores or user IDs) paired with its massive write capacity makes it a proven choice for globally hot update streams.
- ✓
Memorystore for Redis
Why this is correct
Memorystore for Redis provides an in-memory data structure server whose sorted set operations (ZADD, ZRANGEBYSCORE, ZREVRANK) are O(log N) and can handle millions of operations per second across a single Redis instance with sub-millisecond latency. These sorted sets are literally built for leaderboard ranking, allowing instantaneous insertion of new scores and O(log N) rank lookups without requiring external computation. While Memorystore can hit millions of operations per second on a single instance, it is limited by available memory and instance size, but for a leaderboard dataset that fits in RAM it is an equally correct and extremely fast option.
- ✗
BigQuery
Why it's wrong here
BigQuery is an analytical columnar warehouse designed for massively parallel scans of petabytes, not transactional row-level updates at millions of writes per second. Each streaming insert into BigQuery is batched and subject to quota and latency constraints, making it unsuitable for real-time score writes that must be immediately reflected in the leaderboard. BigQuery is better used to analyze historical leaderboard data or run complex aggregations after the fact, not to serve as the live write front-end for a leaderboard.
Go deeper
Related to this question
Learn chapter
High Availability: 99.9% vs 99.99% vs 99.999%
Key term
Cloud SQL
Cloud SQL is a fully managed relational database service that lets you set up, maintain, and scale SQL databases (like MySQL, PostgreSQL, and SQL Server) in the cloud without managing the underlying infrastructure.
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.
About these practice questions
One of 848 original GCDL practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This GCDL practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the GCDL exam.