DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data ingestion pipeline for real-time clickstream data using Amazon Kinesis Data Streams. The data must be transformed using AWS Lambda and then stored in Amazon S3 in Parquet format. Which Kinesis client library configuration should be used to minimize the number of Lambda invocations while ensuring data is processed within 60 seconds?
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
✓
Set batch size to 10000 records and batch window to 60 seconds
A batch size of 10,000 records combined with a batch window of 60 seconds maximizes the number of records per Lambda invocation while respecting the 60-second processing requirement, thereby minimizing the total number of invocations. Option A (batch size 100, no batch window) leads to many small invocations. Option C (batch window 0 seconds) triggers immediate processing, increasing invocation frequency. Option D (batch window 5 seconds) forces more frequent invocations than necessary, even with a large batch size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set batch size to 100 records and disable batch window
Why it's wrong here
Set batch size to 100 records and disable batch window leads to many small invocations, increasing the number of Lambda function calls.
- ✓
Set batch size to 10000 records and batch window to 60 seconds
Why this is correct
Set batch size to 10000 records and batch window to 60 seconds maximizes records per invocation while respecting the 60-second requirement, minimizing invocations.
- ✗
Set batch size to 100 records and batch window to 0 seconds
Why it's wrong here
Set batch size to 100 records and batch window to 0 seconds triggers immediate processing, increasing invocation frequency.
- ✗
Set batch size to 10000 records and batch window to 5 seconds
Why it's wrong here
Sets a batch window of 5 seconds, which forces Lambda to invoke every 5 seconds even if the batch size is not reached. This increases the number of invocations compared to a 60-second window, and does not minimize invocations.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Variation 1. A data engineer is designing a data ingestion pipeline that uses AWS Lambda to process records from a Kinesis Data Stream and write to DynamoDB. Which TWO strategies can help handle increased throughput and prevent data loss? (Choose TWO.)
easy- ✓ A.Configure the Lambda event source mapping with a batch window and set the number of concurrent batches per shard
- B.Use synchronous invocation of Lambda from the producer
- ✓ C.Increase the number of shards in the Kinesis data stream
- D.Configure a dead-letter queue (DLQ) for the Lambda function
- E.Increase the Lambda function timeout
Why A: Configuring a batch window allows Lambda to accumulate records from the Kinesis stream for up to 300 seconds before invoking the function, which helps smooth out traffic spikes and reduces the number of invocations. Setting the number of concurrent batches per shard (via the ParallelizationFactor, up to 10) enables Lambda to process multiple batches from the same shard in parallel, increasing throughput without data loss. This combination ensures that records are processed efficiently even under high load, as Lambda can handle more concurrent executions per shard while batching reduces the risk of throttling.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.