- A
Ignore the throttling errors and let Lambda retry.
Why wrong: Retries don't solve the root cause and may lead to data loss if retention exceeded.
- B
Increase the number of shards in the Kinesis stream.
Why wrong: More shards increase parallelism, potentially more writes to DynamoDB.
- C
Use an Amazon SQS queue as a buffer between Kinesis and Lambda.
Why wrong: SQS adds complexity and delay; not necessary.
- D
Decrease the batch size in the Lambda event source mapping.
Smaller batches reduce the write rate, avoiding throttling.
Question 732 of 1,755
Resolve DynamoDB Write Throttling in Lambda from Kinesis
This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 company uses Amazon Kinesis Data Streams to collect clickstream data. The data is consumed by a Lambda function that writes to DynamoDB. Occasionally, the Lambda function fails due to throttling from DynamoDB. How can the company resolve this issue without losing data?
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
✓
Decrease the batch size in the Lambda event source mapping.
Option D is correct because decreasing the batch size in the Lambda event source mapping reduces the number of records sent to each Lambda invocation. This lowers the write throughput demand on DynamoDB per invocation, mitigating throttling while still allowing Lambda to retry failed records individually. The Kinesis stream retains data for up to 365 days, so no data is lost as long as the Lambda function eventually processes all records.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ignore the throttling errors and let Lambda retry.
Why it's wrong here
Retries don't solve the root cause and may lead to data loss if retention exceeded.
- ✗
Increase the number of shards in the Kinesis stream.
Why it's wrong here
More shards increase parallelism, potentially more writes to DynamoDB.
- ✗
Use an Amazon SQS queue as a buffer between Kinesis and Lambda.
Why it's wrong here
SQS adds complexity and delay; not necessary.
- ✓
Decrease the batch size in the Lambda event source mapping.
Why this is correct
Smaller batches reduce the write rate, avoiding throttling.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing shards or adding a buffer will solve throttling, but the real issue is the downstream write volume per invocation, which is directly controlled by the batch size in the event source mapping.
Detailed technical explanation
How to think about this question
Under the hood, Lambda's event source mapping for Kinesis uses a batch window and batch size to control how many records are passed per invocation. By default, the batch size is 100 records. Reducing it to a smaller value (e.g., 10) decreases the number of concurrent writes to DynamoDB per Lambda execution, allowing each write to stay within the provisioned capacity. Additionally, Lambda's built-in retry behavior for Kinesis processes failed records individually, and the stream's retention period ensures no data is lost even if retries span hours.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Visual reference
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
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Decrease the batch size in the Lambda event source mapping. — Option D is correct because decreasing the batch size in the Lambda event source mapping reduces the number of records sent to each Lambda invocation. This lowers the write throughput demand on DynamoDB per invocation, mitigating throttling while still allowing Lambda to retry failed records individually. The Kinesis stream retains data for up to 365 days, so no data is lost as long as the Lambda function eventually processes all records.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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