DBS-C01 Workload-Specific Database Design Practice Question
A company is designing a document management system using Amazon S3 and needs to store metadata such as document ID, owner, creation date, and tags. The metadata must be searchable with low latency, supporting queries like 'Find all documents owned by user X with tag Y created after date Z'. Which AWS database service is most suitable for storing and querying this metadata?
⚠ Common exam trap
It's easy for candidates to choose a relational database like PostgreSQL because they think normalized schemas are required for complex queries, but DynamoDB's GSI and filter expressions can handle this access pattern more efficiently at scale without the overhead of joins.
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
✓
Amazon DynamoDB with a GSI on (owner, creation_date) and a filter on tags.
Amazon DynamoDB is the most suitable choice because it provides single-digit millisecond latency for queries at any scale, which meets the low-latency search requirement. By creating a Global Secondary Index (GSI) on (owner, creation_date), you can efficiently query documents by owner and date range, and then apply a filter expression on tags to narrow results. This schema avoids the overhead of joins and normalization, making it ideal for high-throughput metadata lookups.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon DynamoDB with a GSI on (owner, creation_date) and a filter on tags.
Why this is correct
DynamoDB provides fast queries and flexible indexing.
- ✗
Amazon Redshift Spectrum querying metadata stored in S3 as CSV.
Why it's wrong here
High latency, not suitable for low-latency queries.
- ✗
Amazon RDS for PostgreSQL with a normalized schema.
Why it's wrong here
While Amazon RDS for PostgreSQL supports JSONB indexing for tag queries, its row‑store architecture cannot match the sub‑millisecond latency of a purpose‑built NoSQL key‑value or document store when scanning millions of metadata records with composite filters. It is tempting because PostgreSQL offers robust SQL joins and ACID compliance, making it the correct choice for transactional workloads requiring complex relational queries across multiple tables.
- ✗
Amazon ElastiCache for Redis with sorted sets for tags.
Why it's wrong here
Not durable; metadata would be lost on failure.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.