The answer is Amazon DynamoDB with on-demand capacity. This is the correct choice because DynamoDB is a fully managed NoSQL database that natively handles high-velocity streaming JSON data with varying schemas, requiring no upfront schema definition or capacity provisioning. Its on-demand capacity mode automatically scales read and write throughput to accommodate unpredictable traffic spikes, making it both cost-effective and scalable for real-time querying and later batch processing via DynamoDB Streams integrated with AWS Glue or EMR. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of when to choose a NoSQL solution over relational databases for schema-less, high-velocity ingestion—a common trap is selecting Amazon RDS or Redshift for JSON data, which require rigid schemas and are less cost-effective for unpredictable workloads. Memory tip: think “JSON = NoSQL, on-demand = no provisioning,” and remember that DynamoDB Streams is the key enabler for batch processing without additional infrastructure.
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 startup is building a social media analytics application that ingests high-velocity streaming data from multiple sources. The data consists of JSON objects with varying schemas. The application needs to store this data for real-time querying and later batch processing. Which AWS database solution is most cost-effective and scalable for this workload?
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 on-demand capacity.
Amazon DynamoDB with on-demand capacity is the most cost-effective and scalable solution for this workload because it is a fully managed NoSQL database that can handle high-velocity streaming data with varying JSON schemas without requiring schema definition or provisioning. Its on-demand capacity mode automatically scales to accommodate unpredictable traffic spikes, making it ideal for real-time querying and batch processing via features like DynamoDB Streams and integration with AWS Glue or EMR.
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 ElastiCache for Redis with persistence enabled.
Why it's wrong here
ElastiCache is a cache, not a primary data store for durability.
✓
Amazon DynamoDB with on-demand capacity.
Why this is correct
DynamoDB handles high-velocity writes and varying schemas, and is cost-effective for unpredictable workloads.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon RDS for MySQL with multiple read replicas.
Why it's wrong here
Relational database requires fixed schema and is not optimal for varying schemas.
✗
Amazon Redshift with auto-ingest from Kinesis.
Why it's wrong here
Redshift is for analytics, not real-time ingestion with varying schemas.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Amazon Redshift (Option D) because they associate streaming data with data warehousing, but Redshift is optimized for batch analytics on structured data, not for real-time ingestion and querying of schema-less JSON, making DynamoDB the correct choice for this specific workload.
Detailed technical explanation
How to think about this question
DynamoDB's on-demand capacity mode uses a pay-per-request pricing model that scales instantly to handle millions of requests per second, making it ideal for spiky, unpredictable workloads. Its support for JSON documents via the Document API and DynamoDB Streams enables real-time processing (e.g., with Lambda) and batch export to S3 for later analytics, while the lack of a fixed schema allows ingestion of varying JSON objects without transformation overhead.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 on-demand capacity. — Amazon DynamoDB with on-demand capacity is the most cost-effective and scalable solution for this workload because it is a fully managed NoSQL database that can handle high-velocity streaming data with varying JSON schemas without requiring schema definition or provisioning. Its on-demand capacity mode automatically scales to accommodate unpredictable traffic spikes, making it ideal for real-time querying and batch processing via features like DynamoDB Streams and integration with AWS Glue or EMR.
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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