The answer is Amazon DynamoDB with a Global Secondary Index on (owner, creation_date) and a filter on tags. This is correct because DynamoDB delivers single-digit millisecond latency at any scale, which directly meets the low-latency search requirement for document metadata stored alongside S3 objects. By modeling the GSI’s partition key on owner and sort key on creation_date, you enable efficient queries for “all documents owned by user X created after date Z,” and then apply a filter expression on tags to refine results without sacrificing performance. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your ability to choose a NoSQL solution over relational databases like RDS or Aurora, which would introduce join overhead and slower scans. A common trap is selecting DynamoDB without a GSI, forcing a full table scan; always remember that GSIs enable targeted query patterns. Memory tip: “GSI on owner-date, filter the tags” — the index handles the heavy lifting, the filter just trims the output.
DBS-C01 Workload-Specific Database Design Practice Question
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 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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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
RDS can work, but DynamoDB is more suitable for low-latency, scalable metadata lookups.
✗
Amazon ElastiCache for Redis with sorted sets for tags.
Why it's wrong here
Not durable; metadata would be lost on failure.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
DynamoDB's GSI allows you to query on non-primary key attributes with the same low-latency performance as the base table, but it is eventually consistent by default. The filter expression on tags is applied after the query retrieves items matching the key condition, so it does not reduce the read capacity consumed; for large datasets, you may need to consider a composite key design or a secondary index on tags to avoid full scans. In practice, this pattern works well when the number of tags per document is small and the query volume is high, but for complex multi-tag filtering, you might use DynamoDB's sparse indexes or a search service like Amazon OpenSearch.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DBS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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