MLS-C01 Data Engineering Practice Question
A company runs a real-time recommendation system that uses Amazon SageMaker endpoints for inference. The system ingests user activity data from a mobile app via Amazon API Gateway and AWS Lambda, which writes events to an Amazon Kinesis Data Stream. A second Lambda function consumes the stream, calls a SageMaker endpoint to generate recommendations, and stores the results in Amazon DynamoDB. The system has been working well, but recently the team noticed an increase in latency from the time a user action occurs to when the recommendation is stored. The SageMaker endpoint shows increased invocation latency but no throttling. CloudWatch metrics show that the Kinesis stream's IteratorAgeMilliseconds is increasing, indicating the consumer is falling behind. The Lambda consumer's duration is within limits, but the number of invocations is lower than expected. The team suspects the issue is with the event source mapping. Which course of action should the team take to reduce the latency?
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 batch size in the event source mapping to process more records per invocation.
The increasing IteratorAgeMilliseconds indicates the consumer is falling behind. The Lambda consumer's duration is within limits but the number of invocations is lower than expected, suggesting that the event source mapping is not invoking the function often enough. Increasing the batch size allows each invocation to process more records per invocation, effectively increasing throughput without requiring more invocations. This directly addresses the lag. Option B (increase shards) could help if the consumer had sufficient concurrency, but the root cause is low invocations per shard, not lack of shards. Option C (decrease reserved concurrency) would worsen the problem. Option D (Firehose) does not solve the consumer lag and changes the architecture unnecessarily.
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 batch size in the event source mapping to process more records per invocation.
Why this is correct
Larger batches improve throughput by reducing overhead per invocation.
- ✗
Increase the number of shards in the Kinesis data stream to increase parallelism.
Why it's wrong here
The consumer is already falling behind; more shards would require more Lambda concurrency but the event source mapping may be limiting.
- ✗
Decrease the Lambda function's reserved concurrency to force it to scale down.
Why it's wrong here
Reducing concurrency would increase lag.
- ✗
Replace the Lambda consumer with an Amazon Kinesis Data Firehose delivery stream.
Why it's wrong here
Firehose is for near-real-time delivery, not for calling SageMaker endpoints.
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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