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.
Refer to the exhibit. A data engineer is using a Kinesis Data Stream with 2 shards. The producer uses a partition key that is the user ID (a UUID). The consumer is falling behind. Which change would improve throughput?
Switch to Kinesis Data Firehose
Why wrong: Firehose would add buffering latency, not improve consumer throughput.
Increase the number of shards
More shards increase the read capacity for consumers.
Increase the retention period
Why wrong: Retention period does not affect ingestion or consumption throughput.
Change the partition key to a constant value
Why wrong: A constant partition key would send all data to one shard, reducing throughput.
Answer choices
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
The consumer is falling behind because the total throughput of the stream (1 MB/s or 1,000 records/s per shard for writes, and 2 MB/s per shard for reads) is insufficient for the incoming data volume. Increasing the number of shards scales both the write and read capacity linearly, allowing the consumer to process records faster and catch up. Changing the partition key or retention period does not increase throughput, and switching to Firehose changes the delivery model but does not inherently solve the consumer lag.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Switch to Kinesis Data Firehose
Why it's wrong here
Firehose would add buffering latency, not improve consumer throughput.
Increase the number of shards
Why this is correct
More shards increase the read capacity for consumers.
Related concept
Read the scenario before looking for a memorised answer.
Increase the retention period
Why it's wrong here
Retention period does not affect ingestion or consumption throughput.
Change the partition key to a constant value
Why it's wrong here
A constant partition key would send all data to one shard, reducing throughput.
Common exam traps
The trap here is that candidates may think changing the partition key to a constant value would simplify processing, but it actually destroys parallelism and reduces throughput to a single shard, making the lag worse.
Detailed technical explanation
Each shard in Kinesis Data Streams supports up to 1 MB/s or 1,000 records/s for writes and 2 MB/s for reads (with a maximum of 5 transactions per second per shard for GetRecords). When a consumer falls behind, it is often due to hitting the read throughput limit; adding shards increases the total read capacity proportionally. The partition key (UUID) already provides good distribution across shards, so the bottleneck is capacity, not key design.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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FAQ
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the number of shards — The consumer is falling behind because the total throughput of the stream (1 MB/s or 1,000 records/s per shard for writes, and 2 MB/s per shard for reads) is insufficient for the incoming data volume. Increasing the number of shards scales both the write and read capacity linearly, allowing the consumer to process records faster and catch up. Changing the partition key or retention period does not increase throughput, and switching to Firehose changes the delivery model but does not inherently solve the consumer lag.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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