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Design and Plan Database Solutions practice questions

Practise Google Professional Cloud DevOps Engineer Design and Plan Database Solutions practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

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20 questionsDomain: Design and Plan Database Solutions

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What the exam tests

What to know about Design and Plan Database Solutions

Design and Plan Database Solutions questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Design and Plan Database Solutions exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Design and Plan Database Solutions questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice

Read the full Design and Plan Database Solutions explanation →

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

Trap 1: Firestore

Firestore is a document database suited for mobile/web apps with hierarchical data. It does not scale to millions of QPS for time-series workloads.

Trap 2: Cloud Spanner

Spanner is a globally distributed relational database optimised for ACID transactions, not single-digit ms time-series reads at millions of QPS.

Trap 3: BigQuery

BigQuery is an analytics data warehouse with query latency measured in seconds. It is not designed for millisecond lookups at IoT scale.

Study all Design and Plan Database Solutions common traps →

A
Firestore
Why wrong: Firestore is a document database suited for mobile/web apps with hierarchical data. It does not scale to millions of QPS for time-series workloads.
B
Cloud Spanner
Why wrong: Spanner is a globally distributed relational database optimised for ACID transactions, not single-digit ms time-series reads at millions of QPS.
C
Cloud Bigtable
Bigtable is the correct choice: wide-column NoSQL, designed for time-series and IoT workloads, single-digit ms latency, and scales to millions of QPS with additional nodes.
D
BigQuery
Why wrong: BigQuery is an analytics data warehouse with query latency measured in seconds. It is not designed for millisecond lookups at IoT scale.

Design and Plan Database Solutions practice questions

What to know about Design and Plan Database Solutions

Common Design and Plan Database Solutions exam traps

Design and Plan Database Solutions questions

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

You are designing a schema for Cloud Spanner and need to avoid write hotspots. Which primary key design strategy is recommended?

A company is migrating an on-premise PostgreSQL database to Cloud SQL. The current database has a connection pool with 200 connections and uses 64 GB RAM. According to Cloud SQL best practices, what is the maximum recommended max_connections setting?

You need to design a Cloud Bigtable row key for a time-series application that records user activity. The most common queries filter by user_id and then by timestamp (most recent first). Which row key design is MOST appropriate?

You are designing a database schema for Cloud SQL (MySQL) for an OLTP application. Which normal form is typically recommended to avoid update anomalies?

A company is migrating a relational database to Cloud Bigtable. The source schema has a customers table and an orders table with a foreign key. Which data model approach is recommended for Bigtable?

You are running a Cloud Spanner instance and notice that a secondary index is causing performance issues for write operations. The index includes all columns of the table. Which Spanner feature can reduce the storage and write overhead of the index?

A company needs to run complex analytical queries on large datasets (petabytes) with SQL support and high scalability. The data is stored in CSV files in Cloud Storage. Which Google Cloud service is MOST suitable?

You are designing a Cloud Bigtable schema for a time-series application where the most common write pattern is high-throughput writes (10,000 writes per second) and the row key starts with a timestamp. Write throughput is lower than expected. What is the most likely cause?

A company uses Cloud SQL for PostgreSQL and needs to add a full-text search capability to a table with product descriptions. Which index type should be used?

You are planning a Cloud Bigtable cluster for a workload requiring 100,000 reads per second and 50,000 writes per second. The data will be stored on HDD. How many nodes are needed for the projected throughput? (Assume each node provides 10,000 QPS for reads or writes.)

You are designing a schema for Cloud Spanner and need to model a one-to-many relationship between Customers and Orders. Which THREE features or practices should you consider? (Choose three.)

A company is migrating an on-premises OLTP application to Google Cloud. The application requires high concurrency (up to 5,000 simultaneous connections) and uses a relational schema with strong transactional integrity. Which database service is the MOST suitable?

A data engineer needs to run complex analytical queries on terabytes of data with sub-second query latency. The data is stored in Google Cloud Storage and updated daily. Which database service should they use?

You are designing a Bigtable schema for a time-series application that records sensor readings every second. Queries always filter by device ID and time range. To avoid hotspotting and ensure recent data is retrieved quickly, which row key design is MOST effective?

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