SAP-C02 Design for New Solutions Practice Question
A company is migrating a monolithic application to a serverless architecture using AWS Lambda. The application reads and writes to an Amazon RDS for PostgreSQL database. The database connection pool is exhausted during peak traffic. Which design change should a solutions architect recommend to avoid connection exhaustion?
⚠ Common exam trap
Candidates often think increasing max_connections or using a queue (SQS) is sufficient, but they overlook that Lambda's concurrent execution model requires connection pooling at the database layer, which RDS Proxy uniquely provides.
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
✓
Use Amazon RDS Proxy to pool and share database connections.
Amazon RDS Proxy sits between the Lambda function and the RDS database, managing a pool of database connections and reusing them across multiple invocations. This prevents the Lambda function from exhausting the database connection pool during traffic spikes, as each Lambda instance does not need to open its own connection. RDS Proxy also handles connection multiplexing and reduces the overhead of establishing new connections.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon SQS to buffer write requests to the database.
Why it's wrong here
SQS decouples and buffers write volume, but consumers still open a connection per invocation, so read traffic and concurrent pollers keep exhausting the pool. It is tempting because queueing smooths spikes, and it would be correct for absorbing bursty writes or retrying failed ones, not for capping database connections.
- ✗
Use DynamoDB Accelerator (DAX) as a caching layer.
Why it's wrong here
DAX caches DynamoDB items and cannot front Amazon RDS for PostgreSQL, so it does nothing for the exhausted connection pool. It is tempting because caching offloads read traffic from a database, which would help here; but DAX only serves DynamoDB, so the correct fix is Amazon RDS Proxy.
- ✓
Use Amazon RDS Proxy to pool and share database connections.
Why this is correct
Lambda's per-invocation concurrency opens many direct PostgreSQL connections, exhausting the pool. RDS Proxy maintains a shared pool and multiplexes Lambda connections onto it, so the constraint of connection exhaustion at peak traffic is removed without changing application logic.
- ✗
Increase the max_connections parameter in the RDS parameter group.
Why it's wrong here
Raising max_connections only lifts the server-side ceiling; each Lambda invocation still opens its own connection, so concurrency above that limit exhausts it again and inflates RDS memory. It is tempting because it is a quick parameter-group edit, and it suits steady EC2 workloads with fixed pools, not bursty per-invocation Lambda connections.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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