The answer is to configure DynamoDB to use on-demand capacity mode and increase the Lambda event source mapping batch size. Increasing the batch size allows each Lambda invocation to process more records from the Kinesis stream, which directly reduces the number of concurrent invocations and alleviates Lambda throttling. Switching DynamoDB to on-demand mode eliminates write capacity limits, preventing throttling on the database side that causes Lambda retries and backpressure in the pipeline. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how Kinesis-to-DynamoDB pipelines can suffer from throttling at two distinct layers: the compute layer (Lambda concurrency) and the storage layer (DynamoDB write capacity). A common trap is to focus only on increasing Lambda concurrency limits, but the real bottleneck often lies in DynamoDB’s provisioned capacity causing failed writes and retries. Remember the mnemonic “Batch and On-Demand” to recall that you fix Lambda throttling by batching more records per invocation and fix DynamoDB throttling by removing capacity limits.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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 ingests IoT sensor data into Kinesis Data Streams. The data is then processed by a Lambda function that aggregates readings and writes to DynamoDB. The Lambda function is experiencing high error rates due to throttling. Which TWO actions would reduce throttling?
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 Lambda event source mapping.
Option B is correct because increasing the batch size in the Lambda event source mapping allows each invocation to process more records from the Kinesis stream, reducing the number of concurrent Lambda invocations and thus lowering the risk of throttling. Option D is correct because switching DynamoDB to on-demand capacity mode eliminates write capacity limits, preventing throttling on the DynamoDB side that can cause Lambda retries and backpressure.
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.
✗
Increase the number of shards in the Kinesis stream.
Why it's wrong here
More shards increase parallelism but do not directly reduce Lambda throttling.
✓
Increase the batch size in the Lambda event source mapping.
Why this is correct
Larger batches mean fewer invocations, reducing throttling.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the batch window in the Lambda event source mapping.
Why it's wrong here
Shorter windows increase invocations, worsening throttling.
✓
Configure DynamoDB to use on-demand capacity mode.
Why this is correct
On-demand mode eliminates write throttling from DynamoDB.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the Lambda reserved concurrency to 1000.
Why it's wrong here
Reserved concurrency sets a limit but does not prevent throttling if the account limit is exceeded.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing shards (Option A) always improves throughput, but in a Lambda-integrated Kinesis stream, more shards mean more concurrent invocations, which can actually increase throttling risk.
Detailed technical explanation
How to think about this question
Lambda processes Kinesis records in batches per shard, and the batch size controls how many records are sent in a single invocation. By default, Lambda uses a batch size of 100; increasing it to, say, 10,000 can drastically reduce invocation frequency. On the DynamoDB side, on-demand mode automatically scales write capacity up to previous traffic peaks, but it has a per-account throughput limit (e.g., 40,000 write units per table) that can still throttle if exceeded—though for most IoT workloads, this is far less restrictive than provisioned capacity.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the batch size in the Lambda event source mapping. — Option B is correct because increasing the batch size in the Lambda event source mapping allows each invocation to process more records from the Kinesis stream, reducing the number of concurrent Lambda invocations and thus lowering the risk of throttling. Option D is correct because switching DynamoDB to on-demand capacity mode eliminates write capacity limits, preventing throttling on the DynamoDB side that can cause Lambda retries and backpressure.
What should I do if I get this DEA-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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Question Discussion
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