AI0-001 AI Models and Data Engineering Practice Question
A data engineer needs to design a data pipeline for a real-time fraud detection system. The system requires low-latency processing of streaming transactions. Which architecture is most appropriate?
⚠ Common exam trap
CompTIA often tests the distinction between true stream processing (e.g., Flink, Kafka Streams) and micro-batch or near-real-time processing (e.g., Spark Streaming), where candidates mistakenly assume that any 'streaming' API (like Spark Streaming) is equivalent to low-latency stream processing.
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
✓
Stream processing with Apache Kafka and Flink
Apache Kafka provides a distributed, fault-tolerant event streaming platform that ingests high-throughput transaction data with low latency, while Apache Flink offers true stream processing with exactly-once semantics and sub-second event-time processing. Together, they enable real-time fraud detection by analyzing transactions as they arrive, without the delays inherent in batch or micro-batch approaches.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Stream processing with Apache Kafka and Flink
Why this is correct
Kafka ingests high-throughput transaction streams durably, while Flink performs stateful, event-time windowed computation with millisecond latency, satisfying the low-latency streaming requirement. Batch architectures such as Hadoop or Spark microbatching introduce latency unsuited to real-time fraud detection.
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Data lake with Apache Spark
Why it's wrong here
A data lake with Spark runs micro-batch or batch jobs over stored files, so events are processed after landing rather than per-record as they arrive. It suits large-scale analytical and machine-learning workloads over historical data, not continuous low-latency scoring of each streaming transaction.
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Batch processing with Apache Hadoop
Why it's wrong here
Hadoop MapReduce processes bounded input in scheduled jobs, so fraud decisions arrive after the batch window closes, far too late for in-flight transactions. Batch processing is correct for periodic aggregation and reporting over accumulated data, not for per-event latency requirements.
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Microservices architecture with REST APIs
Why it's wrong here
REST APIs are request-response and synchronous, so each transaction waits for a service reply, adding per-call latency and no stream windowing. Microservices suit decomposing business capabilities into independently deployable services, not sub-second event processing across continuous transaction streams.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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