SAP-C02 Kinesis Shards Practice Question
A company is designing a serverless data processing pipeline using AWS Lambda. The pipeline processes data from an Amazon Kinesis Data Stream. The Lambda function has a memory limit of 512 MB and a timeout of 5 minutes. The data volume is expected to increase significantly. Which TWO strategies should the company implement to improve throughput and reduce processing latency? (Choose TWO.)
⚠ Common exam trap
Test-takers frequently think increasing reserved concurrency (Option E) is the key to scaling, but without increasing shards, Lambda cannot process more data in parallel because each shard is processed by only one concurrent Lambda instance at a time.
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
✓
Increase the number of shards in the Kinesis data stream
Increasing the number of shards in the Kinesis data stream (Option A) increases parallelism because each shard can be processed by one concurrent Lambda instance, allowing the pipeline to handle higher data volumes. Increasing the batch size in the event source mapping (Option B) allows each Lambda invocation to process more records at once, reducing the number of invocations and lowering per-record overhead. Together, these two strategies directly improve throughput and reduce latency. Option D (increasing memory) can improve performance for compute-bound functions but does not directly address parallelism or batching, and is not one of the two best choices for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the number of shards in the Kinesis data stream
Why this is correct
More shards allow more Lambda executions in parallel, improving throughput.
- ✓
Increase the batch size in the event source mapping
Why this is correct
Larger batch size means each invocation processes more records, reducing the number of invocations and overhead.
- ✗
Change the data source from Kinesis to an Amazon SQS queue
Why it's wrong here
Changing the source may not be necessary and could introduce other limitations.
- ✗
Increase the Lambda function memory to 1024 MB
Why it's wrong here
Increasing Lambda memory may reduce execution time for compute-intensive functions, but it does not increase parallelism or batch processing efficiency. While it could help in some cases, it is not one of the two primary strategies to improve throughput and reduce latency in a Kinesis-triggered pipeline.
- ✗
Increase the Lambda function's reserved concurrency to a higher value
Why it's wrong here
Reserved concurrency ensures capacity but does not inherently increase throughput if shards are the bottleneck.
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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