SAP-C02 Design for New Solutions Practice Question
A startup is deploying a web application on Amazon EC2 instances behind an Application Load Balancer. The application stores session state in an Amazon DynamoDB table. To improve performance, the team wants to reduce latency for read-heavy workloads. Which design change would be MOST effective?
⚠ Common exam trap
Many exam-takers choose ElastiCache Redis (Option A) because it is a general-purpose cache, but they overlook that DAX is purpose-built for DynamoDB and eliminates the need for custom cache invalidation logic, making it the most effective and operationally simpler choice for this specific use case.
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
✓
Enable DynamoDB Accelerator (DAX) for the session table.
DynamoDB Accelerator (DAX) is a fully managed, in-memory cache specifically designed for Amazon DynamoDB. It reduces read latency from single-digit milliseconds to microseconds by caching frequently accessed items, making it ideal for read-heavy session state workloads without requiring application-level cache management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add an Amazon ElastiCache Redis cluster in front of DynamoDB to cache session data.
Why it's wrong here
Adding an ElastiCache Redis cluster would cache session data in memory, reducing read latency for DynamoDB queries. However, the stem specifies that the application already stores session state in DynamoDB; the performance bottleneck is read-heavy workloads on that table, not session retrieval. Redis is tempting because it excels at low-latency caching for frequently accessed key-value data, and would be correct if the goal were to offload repeated reads from a relational database or reduce DynamoDB read capacity costs.
- ✗
Use an Auto Scaling group to add more EC2 instances during peak hours.
Why it's wrong here
Adding EC2 instances scales compute capacity, but the read latency originates from the DynamoDB table, so the bottleneck remains untouched. Auto Scaling is correct for CPU-bound or connection-bound web tiers, not for reducing data-store read latency.
- ✓
Enable DynamoDB Accelerator (DAX) for the session table.
Why this is correct
DAX provides an in-memory write-through cache for DynamoDB, serving repeated session reads in microseconds and cutting latency for read-heavy workloads. It satisfies the performance constraint directly, unlike increasing capacity or adding read replicas, which address throughput rather than microsecond read latency.
- ✗
Increase the size of the EC2 instances to handle more concurrent users.
Why it's wrong here
Scaling EC2 instance size adds compute capacity but leaves DynamoDB read latency untouched, since the bottleneck is the database layer, not the application tier. It is tempting because vertical scaling genuinely helps CPU-bound workloads, and it would be the right choice if the stem described application processing limits rather than read-heavy DynamoDB access.
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