- A
Use Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling.
Firehose buffers data, retries on failures, and decouples the producer from DynamoDB writes.
- B
Increase the Lambda function's reserved concurrency and provision more DynamoDB write capacity.
Why wrong: Increasing capacity may help temporarily but does not decouple the system; throttling can still occur.
- C
Place the Lambda function's output into an Amazon SQS queue, and have a second Lambda function write to DynamoDB.
Why wrong: SQS is not directly integrated with Kinesis; additional components increase complexity.
- D
Use Kinesis Data Analytics to process the stream and write results directly to DynamoDB.
Why wrong: Kinesis Data Analytics does not have a built-in DynamoDB sink; it can output to Firehose or Lambda.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 Kinesis Data Streams to ingest real-time sensor data. The data is consumed by a Lambda function that writes to DynamoDB. During peak hours, the Lambda function throws ProvisionedThroughputExceededException. The team wants to decouple the write operation and improve resilience. What should they do?
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
Use Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling.
Option A is correct because Kinesis Firehose can consume data from a Kinesis Data Stream and invoke a Lambda function for transformation before delivering to destinations like DynamoDB. By using Firehose with error handling, the team decouples the write operation from the Lambda consumer, allowing Firehose to buffer data and retry failed writes, which improves resilience against ProvisionedThroughputExceededException without losing data.
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.
- ✓
Use Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling.
Why this is correct
Firehose buffers data, retries on failures, and decouples the producer from DynamoDB writes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the Lambda function's reserved concurrency and provision more DynamoDB write capacity.
Why it's wrong here
Increasing capacity may help temporarily but does not decouple the system; throttling can still occur.
- ✗
Place the Lambda function's output into an Amazon SQS queue, and have a second Lambda function write to DynamoDB.
Why it's wrong here
SQS is not directly integrated with Kinesis; additional components increase complexity.
- ✗
Use Kinesis Data Analytics to process the stream and write results directly to DynamoDB.
Why it's wrong here
Kinesis Data Analytics does not have a built-in DynamoDB sink; it can output to Firehose or Lambda.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume adding a queue (SQS) is the standard decoupling pattern, but in this context, Kinesis Firehose is purpose-built for stream ingestion with built-in error handling and Lambda integration, making it a more direct and efficient solution than introducing an additional queue layer.
Trap categories for this question
Command / output trap
Kinesis Data Analytics does not have a built-in DynamoDB sink; it can output to Firehose or Lambda.
Detailed technical explanation
How to think about this question
Kinesis Firehose buffers incoming data from the stream and can invoke a Lambda function for transformation, then deliver to DynamoDB via a custom destination or AWS Lambda integration. Under the hood, Firehose handles retries and error logging to Amazon S3 or CloudWatch, ensuring data durability even when DynamoDB throttles writes. In real-world scenarios, this pattern is used to smooth out traffic spikes and avoid backpressure on the stream consumer, as Firehose can batch records and apply exponential backoff.
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 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.
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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: Use Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling. — Option A is correct because Kinesis Firehose can consume data from a Kinesis Data Stream and invoke a Lambda function for transformation before delivering to destinations like DynamoDB. By using Firehose with error handling, the team decouples the write operation from the Lambda consumer, allowing Firehose to buffer data and retry failed writes, which improves resilience against ProvisionedThroughputExceededException without losing data.
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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