- A
Decrease the Lambda function's batch size to 10.
Why wrong: Decreasing the batch size increases the number of Lambda invocations per shard per second (from 5 to 50), raising concurrency to ~100, which could still be within reserved concurrency but does nothing to fix DynamoDB write throttling.
- B
Increase the DynamoDB write capacity units to 1000.
Correct. The DynamoDB table has insufficient write capacity (100 WCU) for the incoming load (10,000 WCU needed). Increasing to 1,000 WCU provides a 10x improvement, reducing throttling and retries.
- C
Increase the number of shards in the Kinesis stream to 100.
Why wrong: Increasing shards to 100 would increase the maximum concurrency but also increase the total write load on DynamoDB, worsening the bottleneck.
- D
Increase the Lambda function's reserved concurrency to 1000.
Why wrong: Increasing reserved concurrency to 1,000 has no effect because the current concurrency is only ~10, well within the existing 100 limit. The issue is DynamoDB, not Lambda concurrency.
MLS-C01 Kinesis Shard-Level Parallelism Practice Question
This MLS-C01 practice question tests your understanding of data engineering. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. A key principle to apply: kinesis Shard-Level Parallelism. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data engineering team is building a real-time fraud detection pipeline. The pipeline ingests transaction data from an Amazon Kinesis Data Stream with 10 shards. Each shard produces about 500 records per second, each record is 2 KB. The data is processed by a Lambda function that runs for about 200 ms and then writes results to an Amazon DynamoDB table. The team notices that the Lambda function is experiencing a high number of throttles, and there are increasing numbers of records being retried. The Lambda function's reserved concurrency is set to 100. The DynamoDB table has 100 read capacity units and 100 write capacity units. Which change would most effectively reduce throttling and improve processing throughput?
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 DynamoDB write capacity units to 1000.
The observed throttling is due to insufficient Lambda concurrency. With 10 shards each producing 500 records per second and a batch size of 100, each shard generates 5 batches per second. Given a 200 ms processing time, each shard requires multiple concurrent invocations to avoid backlog, but the current reserved concurrency of 100 may limit the number of simultaneous executions. Increasing reserved concurrency to 1000 ensures the function can process all batches in parallel without throttling (e.g., via ParallelizationFactor >1 if configured), reducing retries and improving throughput. DynamoDB write capacity may also be a factor, but the most direct remedy for Lambda throttling is raising the concurrency limit.
Key principle: Kinesis Shard-Level Parallelism
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the Lambda function's batch size to 10.
Why it's wrong here
Decreasing the batch size increases the number of Lambda invocations per shard per second (from 5 to 50), raising concurrency to ~100, which could still be within reserved concurrency but does nothing to fix DynamoDB write throttling.
- ✓
Increase the DynamoDB write capacity units to 1000.
Why this is correct
Correct. The DynamoDB table has insufficient write capacity (100 WCU) for the incoming load (10,000 WCU needed). Increasing to 1,000 WCU provides a 10x improvement, reducing throttling and retries.
Related concept
Kinesis Shard-Level Parallelism
- ✗
Increase the number of shards in the Kinesis stream to 100.
Why it's wrong here
Increasing shards to 100 would increase the maximum concurrency but also increase the total write load on DynamoDB, worsening the bottleneck.
- ✗
Increase the Lambda function's reserved concurrency to 1000.
Why it's wrong here
Increasing reserved concurrency to 1,000 has no effect because the current concurrency is only ~10, well within the existing 100 limit. The issue is DynamoDB, not Lambda concurrency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may mistakenly think Lambda concurrency is the problem because they see throttles and retries, but fail to check the downstream DynamoDB capacity.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Kinesis Shard-Level Parallelism
- DynamoDB Write Capacity Units (WCU)
- Lambda Reserved Concurrency
- Batch Size in Kinesis Event Source
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Kinesis Shard-Level Parallelism
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
Got this wrong? Here's your next step.
Review kinesis Shard-Level Parallelism, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Kinesis Shard-Level Parallelism.
What is the correct answer to this question?
The correct answer is: Increase the DynamoDB write capacity units to 1000. — The observed throttling is due to insufficient Lambda concurrency. With 10 shards each producing 500 records per second and a batch size of 100, each shard generates 5 batches per second. Given a 200 ms processing time, each shard requires multiple concurrent invocations to avoid backlog, but the current reserved concurrency of 100 may limit the number of simultaneous executions. Increasing reserved concurrency to 1000 ensures the function can process all batches in parallel without throttling (e.g., via ParallelizationFactor >1 if configured), reducing retries and improving throughput. DynamoDB write capacity may also be a factor, but the most direct remedy for Lambda throttling is raising the concurrency limit.
What should I do if I get this MLS-C01 question wrong?
Review kinesis Shard-Level Parallelism, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Kinesis Shard-Level Parallelism
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Last reviewed: Jun 20, 2026
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