- A
Increase the Lambda function's memory allocation
Why wrong: More memory may speed up processing but the bottleneck is the shard throughput limit.
- B
Increase the number of shards to 20
More shards increase the total throughput of the stream, allowing Lambda to process more data in parallel.
- C
Enable enhanced fan-out for the Lambda consumer
Enhanced fan-out provides dedicated read throughput per consumer, eliminating throttling from other consumers.
- D
Switch to using Kinesis Client Library (KCL) instead of Lambda
Why wrong: Lambda already uses KCL under the hood; this would not solve the throughput issue.
- E
Decrease the batch size in the Lambda event source mapping
Why wrong: Smaller batch sizes increase the number of Lambda invocations, adding overhead.
Question 1,670 of 1,786
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 company runs a real-time analytics platform using Amazon Kinesis Data Streams. The data is consumed by multiple consumers: one for real-time dashboard (using Lambda) and one for long-term storage (using Firehose to S3). The Kinesis stream has 10 shards. Each record is 1 KB, and the total incoming data rate is 5 MB/s. The Lambda consumer is falling behind and processing latency exceeds 10 seconds. Which TWO actions should be taken to resolve the issue?
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 number of shards to 20
Option B is correct because increasing the number of shards from 10 to 20 doubles the stream's read capacity, allowing the Lambda consumer to poll more data per second and reduce backlog. Option C is correct because enabling enhanced fan-out provides each consumer with a dedicated 2 MB/s read throughput per shard, eliminating contention between the Lambda consumer and the Firehose consumer, which is critical when multiple consumers read from the same stream.
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's memory allocation
Why it's wrong here
More memory may speed up processing but the bottleneck is the shard throughput limit.
- ✓
Increase the number of shards to 20
Why this is correct
More shards increase the total throughput of the stream, allowing Lambda to process more data in parallel.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable enhanced fan-out for the Lambda consumer
Why this is correct
Enhanced fan-out provides dedicated read throughput per consumer, eliminating throttling from other consumers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to using Kinesis Client Library (KCL) instead of Lambda
Why it's wrong here
Lambda already uses KCL under the hood; this would not solve the throughput issue.
- ✗
Decrease the batch size in the Lambda event source mapping
Why it's wrong here
Smaller batch sizes increase the number of Lambda invocations, adding overhead.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing Lambda resources (memory) or reducing batch size will fix processing lag, when the root cause is a shared read throughput bottleneck between multiple consumers on the same Kinesis stream.
Detailed technical explanation
How to think about this question
Under the hood, each Kinesis shard provides a maximum of 2 MB/s read throughput (shared across all consumers using the standard 'polling' model) and 1 MB/s write throughput. With 10 shards, the total read capacity is 20 MB/s, but when two consumers (Lambda and Firehose) share this pool, each consumer's effective throughput is reduced. Enhanced fan-out (option C) allocates a dedicated 2 MB/s read pipe per shard per consumer, so the Lambda consumer gets its own 20 MB/s (10 shards × 2 MB/s) independent of Firehose, eliminating contention. In real-world scenarios, this is critical when one consumer is latency-sensitive (real-time dashboard) and another is throughput-oriented (Firehose to S3).
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.
Visual reference
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 |
What to study next
Got this wrong? Here's your next step.
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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 number of shards to 20 — Option B is correct because increasing the number of shards from 10 to 20 doubles the stream's read capacity, allowing the Lambda consumer to poll more data per second and reduce backlog. Option C is correct because enabling enhanced fan-out provides each consumer with a dedicated 2 MB/s read throughput per shard, eliminating contention between the Lambda consumer and the Firehose consumer, which is critical when multiple consumers read from the same stream.
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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Last reviewed: Jul 4, 2026
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