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Question 429 of 1,786

Data Ingestion and Transformation →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to increase the Lambda timeout to 60 seconds and memory to 1024 MB, set the batch size to 100 records, and enable a parallelization factor of 10. This works because the parallelization factor allows each Kinesis shard to process up to 10 concurrent batches, so with a batch size of 100, a single shard can handle 1,000 records per second—matching the stream’s ingestion rate—while the increased timeout and memory prevent processing failures. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Lambda-Kinesis integration tuning, specifically how batch size and parallelization factor interact to scale throughput without exceeding concurrency limits. A common trap is thinking only memory or timeout needs adjustment, but the real bottleneck is often the sequential per-shard processing. Remember the mnemonic “10x100 = 1000” to recall that a parallelization factor of 10 with a batch size of 100 yields 1,000 records per second per shard.

DEA-C01 Data Ingestion and Transformation Practice Question

This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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 financial services company ingests real-time stock trade data from multiple exchanges into Amazon Kinesis Data Streams. Each trade record is a JSON object containing fields: trade_id, symbol, price, quantity, and timestamp. The data is consumed by an AWS Lambda function that performs data validation and enrichment, then writes the processed records to an Amazon DynamoDB table for low-latency querying. Recently, the Lambda function has been timing out and failing to process all records. The Lambda function is configured with a 5-second timeout and 128 MB memory. The average record size is 2 KB, and the stream receives about 1000 records per second. The Lambda function's concurrency limit is 1000. Which set of actions should the data engineer take to resolve the issue without losing data?

Question 1hardmultiple choice

Full question →

A
Increase the Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records and enable parallelization factor of 10.
This combination increases processing capacity and prevents timeouts.
B
Increase the number of shards in the Kinesis data stream to 20 and keep the Lambda configuration unchanged.
Why wrong: More shards increase parallelism but Lambda may still timeout with current settings.
C
Replace the Lambda function with a Kinesis Data Firehose delivery stream that writes directly to DynamoDB using a Lambda transformation.
Why wrong: Firehose does not support DynamoDB as a direct destination; it uses Lambda for transformation, which still has the same issue.
D
Increase the Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records.
Why wrong: This alone does not increase concurrency; may still fall behind.

Full breakdown with real-world context →

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 Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records and enable parallelization factor of 10.

Option A is correct because increasing the Lambda timeout and memory addresses the processing bottleneck, while setting the batch size to 100 and enabling a parallelization factor of 10 allows each shard to process up to 10 concurrent batches, dramatically increasing throughput to handle 1000 records/sec (each shard can process 10 batches of 100 records concurrently, yielding 1000 records/sec per shard if the stream has at least 1 shard). This combination ensures no data loss by keeping up with the ingestion rate without exceeding the Lambda concurrency limit of 1000.

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 Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records and enable parallelization factor of 10.
Why this is correct
This combination increases processing capacity and prevents timeouts.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of shards in the Kinesis data stream to 20 and keep the Lambda configuration unchanged.
Why it's wrong here
More shards increase parallelism but Lambda may still timeout with current settings.
✗
Replace the Lambda function with a Kinesis Data Firehose delivery stream that writes directly to DynamoDB using a Lambda transformation.
Why it's wrong here
Firehose does not support DynamoDB as a direct destination; it uses Lambda for transformation, which still has the same issue.
✗
Increase the Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records.
Why it's wrong here
This alone does not increase concurrency; may still fall behind.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often overlook the parallelization factor setting, assuming that increasing batch size and Lambda resources alone will suffice, but without parallelization, each shard can only process one batch at a time, creating a throughput bottleneck that leads to data loss.

Detailed technical explanation

How to think about this question

The parallelization factor in Lambda-Kinesis integration allows a single shard to process up to 10 concurrent batches, each with its own Lambda invocation, effectively multiplying throughput per shard. Under the hood, Lambda uses the Kinesis Client Library (KCL) to manage shard iterators and checkpointing; enabling parallelization factor creates multiple iterators per shard, each tracking its own sequence number, ensuring exactly-once processing semantics. In real-world scenarios, this is critical for high-throughput streams where a single Lambda invocation per shard would be throttled by the 6-minute Lambda timeout or memory limits.

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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

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.

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FAQ

Questions learners often ask

What does this DEA-C01 question test?

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 Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records and enable parallelization factor of 10. — Option A is correct because increasing the Lambda timeout and memory addresses the processing bottleneck, while setting the batch size to 100 and enabling a parallelization factor of 10 allows each shard to process up to 10 concurrent batches, dramatically increasing throughput to handle 1000 records/sec (each shard can process 10 batches of 100 records concurrently, yielding 1000 records/sec per shard if the stream has at least 1 shard). This combination ensures no data loss by keeping up with the ingestion rate without exceeding the Lambda concurrency limit of 1000.

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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