This DBS-C01 practice question tests your understanding of workload-specific database design. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A startup is building a social media application that requires a database to store user relationships (followers, following) and support graph queries. The data volume is expected to grow to tens of terabytes. Which AWS database service is most suitable for this workload?
Amazon RDS for MySQL with self-joins.
Why wrong: Self-joins do not scale well for graph queries.
Amazon Redshift.
Why wrong: Redshift is for analytical queries, not transactional graph.
Amazon DynamoDB with adjacency list design.
Why wrong: DynamoDB is not optimized for graph traversals.
Amazon Neptune.
Neptune is a purpose-built graph database.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon Neptune.
Amazon Neptune is a fully managed graph database service optimized for storing and querying highly connected data, such as social media user relationships (followers, following). It supports both property graph and RDF models, enabling efficient graph traversal queries using Gremlin or SPARQL, which is ideal for this workload. Neptune scales to tens of terabytes and provides low-latency query performance for complex graph patterns, making it the most suitable choice.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon RDS for MySQL with self-joins.
Why it's wrong here
Self-joins do not scale well for graph queries.
Amazon Redshift.
Why it's wrong here
Redshift is for analytical queries, not transactional graph.
Amazon DynamoDB with adjacency list design.
Why it's wrong here
DynamoDB is not optimized for graph traversals.
Amazon Neptune.
Why this is correct
Neptune is a purpose-built graph database.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often choose DynamoDB (Option C) because they associate it with NoSQL scalability, but they overlook that adjacency list designs in DynamoDB require multiple queries and client-side logic for graph traversals, making it unsuitable for deep or multi-hop relationship queries at scale.
Detailed technical explanation
Amazon Neptune uses a shared-storage architecture with up to 15 read replicas and automatic failover, and it stores data as vertices and edges with properties, enabling efficient index-free adjacency traversal. Under the hood, Neptune leverages a custom query engine that optimizes graph patterns using techniques like vertex-centric indexing, which avoids expensive join operations common in relational databases. In a real-world scenario, a social media app with millions of users and billions of follow relationships can execute a 'friends-of-friends' query in milliseconds using Gremlin's repeat() step, whereas a relational database would require recursive CTEs or multiple self-joins that degrade performance.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Neptune. — Amazon Neptune is a fully managed graph database service optimized for storing and querying highly connected data, such as social media user relationships (followers, following). It supports both property graph and RDF models, enabling efficient graph traversal queries using Gremlin or SPARQL, which is ideal for this workload. Neptune scales to tens of terabytes and provides low-latency query performance for complex graph patterns, making it the most suitable choice.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DBS-C01 practice question is part of Courseiva's free Amazon Web Services 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 DBS-C01 exam.
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