DEA-C01 Data Operations and Support Practice Question
A data pipeline using Amazon Kinesis Data Streams is experiencing high consumer lag. The stream has 10 shards. The consumer is an AWS Lambda function that processes each record and writes to Amazon DynamoDB. What is the MOST likely cause of the lag?
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
✓
The Lambda function's reserved concurrency is set too low
The most likely cause of high consumer lag is that the Lambda function's reserved concurrency is set too low (Option A). Each Kinesis shard is processed by a single Lambda invocation, and if the function's concurrency limit is less than the number of shards (10), some shards will not be processed in parallel, leading to lag. Option B (DynamoDB write capacity throttling) could cause lag but is less common if the table is properly provisioned. Option C (insufficient shards) is unlikely because 10 shards already provide parallelism; increasing shards would improve throughput only if Lambda concurrency is not the bottleneck. Option D (authorization) would cause errors, not just lag.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The Lambda function's reserved concurrency is set too low
Why this is correct
Low concurrency limits parallel processing of shards.
- ✗
The DynamoDB table's write capacity is throttling writes
Why it's wrong here
Could cause lag but Lambda would retry; concurrency is more direct.
- ✗
The number of shards is insufficient for the data volume
Why it's wrong here
Could be a cause, but not the most likely given existing setup.
- ✗
The Lambda function is not authorized to read from Kinesis
Why it's wrong here
Would cause errors, not lag.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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