- A
Amazon Kinesis Data Analytics for Apache Flink.
Kinesis Data Analytics with Flink provides low-latency, stateful stream processing at scale.
- B
AWS Lambda functions triggered by Kinesis Data Streams.
Why wrong: Lambda has concurrency limits and may throttle at high throughput; also, it is not stateful for aggregations.
- C
Amazon EMR with Spark Streaming.
Why wrong: EMR requires provisioning and tuning; it is not fully managed without additional effort.
- D
Amazon Redshift with materialized views refreshed frequently.
Why wrong: Redshift is optimized for batch analytics; sub-second latency is not feasible with frequent refreshes.
Question 577 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 company is building a near-real-time dashboard using data from multiple sources. They need to aggregate millions of events per second with sub-second latency. The architecture must be fully managed and minimize operational overhead. Which service should they use for the aggregation layer?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 Analytics for Apache Flink is the correct choice because it provides a fully managed, serverless runtime for Apache Flink, which is designed for stateful stream processing at scale. It can aggregate millions of events per second with sub-second latency using exactly-once semantics and built-in checkpointing, meeting the near-real-time dashboard requirements without any infrastructure management.
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.
- ✓
Amazon Kinesis Data Analytics for Apache Flink.
Why this is correct
Kinesis Data Analytics with Flink provides low-latency, stateful stream processing at scale.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS Lambda functions triggered by Kinesis Data Streams.
Why it's wrong here
Lambda has concurrency limits and may throttle at high throughput; also, it is not stateful for aggregations.
- ✗
Amazon EMR with Spark Streaming.
Why it's wrong here
EMR requires provisioning and tuning; it is not fully managed without additional effort.
- ✗
Amazon Redshift with materialized views refreshed frequently.
Why it's wrong here
Redshift is optimized for batch analytics; sub-second latency is not feasible with frequent refreshes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse AWS Lambda's event-driven nature with true stream processing, overlooking its concurrency and latency limitations for high-throughput aggregation, or they assume Spark Streaming is always the best choice for real-time without considering Flink's superior sub-second latency and fully managed nature on Kinesis Data Analytics.
Detailed technical explanation
How to think about this question
Under the hood, Apache Flink uses a pipelined, dataflow-based execution model with asynchronous checkpointing to achieve low-latency, stateful processing. In a real-world scenario, a financial trading dashboard ingesting market data from multiple Kinesis streams can use Flink's event-time processing and watermarks to handle out-of-order events while maintaining sub-second aggregation windows, which is critical for accurate near-real-time metrics.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — 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 Analytics for Apache Flink is the correct choice because it provides a fully managed, serverless runtime for Apache Flink, which is designed for stateful stream processing at scale. It can aggregate millions of events per second with sub-second latency using exactly-once semantics and built-in checkpointing, meeting the near-real-time dashboard requirements without any infrastructure management.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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