- A
AWS Glue streaming ETL
Why wrong: Glue streaming ETL has higher latency than Flink.
- B
Amazon Kinesis Data Firehose
Why wrong: Firehose has buffering latency, typically 60 seconds.
- C
Amazon SQS
Why wrong: SQS is a message queue, not designed for high-throughput streaming with sub-second latency.
- D
Amazon Kinesis Data Analytics for Apache Flink
Flink can process streaming data with sub-second latency.
- E
Amazon Kinesis Data Streams
Kinesis Data Streams can ingest 100,000 events/sec with low latency.
Question 1,121 of 1,786
High Throughput Streaming with Kinesis Data Streams and Analytics
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 data engineering team is designing a data ingestion pipeline for a social media analytics platform. The pipeline must handle up to 100,000 events per second with less than 1 second processing latency. Which TWO services should be used together to meet these requirements?
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
✓
Amazon Kinesis Data Analytics for Apache Flink
Amazon Kinesis Data Streams (Option E) is designed for real-time data ingestion at scale, supporting up to 1,000 records per second per shard with sub-second latency, making it suitable for 100,000 events per second when provisioned with sufficient shards. Amazon Kinesis Data Analytics for Apache Flink (Option D) can consume data directly from Kinesis Data Streams and perform low-latency stream processing (e.g., aggregations, filtering) with exactly-once semantics, meeting the <1 second processing latency requirement. Together, they form a fully managed, scalable pipeline for high-throughput, low-latency streaming analytics.
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.
- ✗
AWS Glue streaming ETL
Why it's wrong here
Glue streaming ETL has higher latency than Flink.
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose has buffering latency, typically 60 seconds.
- ✗
Amazon SQS
Why it's wrong here
SQS is a message queue, not designed for high-throughput streaming with sub-second latency.
- ✓
Amazon Kinesis Data Analytics for Apache Flink
Why this is correct
Flink can process streaming data with sub-second latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Kinesis Data Streams can ingest 100,000 events/sec with low latency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Kinesis Data Firehose (near-real-time, buffered delivery) with Kinesis Data Streams (real-time, unbuffered ingestion), or assume AWS Glue streaming ETL can achieve sub-second latency when it is actually optimized for micro-batch processing with higher overhead.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the unit of throughput; each shard supports 1 MB/s write and 2 MB/s read, so 100,000 events per second (assuming ~1 KB per event) requires approximately 100 shards. Kinesis Data Analytics for Apache Flink runs on a Flink runtime that leverages checkpointing and state backends (e.g., RocksDB) for fault-tolerant, low-latency stream processing, enabling complex event processing (CEP) and windowed aggregations within milliseconds. In real-world scenarios, this combination is used for real-time fraud detection or social media sentiment analysis where sub-second reaction to trending topics is critical.
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
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.
What to study next
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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: Amazon Kinesis Data Analytics for Apache Flink — Amazon Kinesis Data Streams (Option E) is designed for real-time data ingestion at scale, supporting up to 1,000 records per second per shard with sub-second latency, making it suitable for 100,000 events per second when provisioned with sufficient shards. Amazon Kinesis Data Analytics for Apache Flink (Option D) can consume data directly from Kinesis Data Streams and perform low-latency stream processing (e.g., aggregations, filtering) with exactly-once semantics, meeting the <1 second processing latency requirement. Together, they form a fully managed, scalable pipeline for high-throughput, low-latency streaming analytics.
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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