AI0-001 AI Models and Data Engineering Practice Question
A company streams sensor data from IoT devices. The data arrives as JSON messages at high velocity. Which data pipeline architecture is BEST suited to handle this streaming data for near-real-time analytics?
⚠ Common exam trap
CompTIA often tests the distinction between batch and stream processing by presenting batch options that seem 'reliable' or 'traditional,' trapping candidates who overlook the explicit 'near-real-time' requirement in the question.
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 using Apache Kafka and Spark Streaming.
Apache Kafka acts as a distributed, fault-tolerant ingestion layer that can handle high-velocity JSON messages, while Spark Streaming processes the data in micro-batches for near-real-time analytics. This combination provides the low-latency, scalable pipeline required for streaming IoT sensor data, unlike batch or single-node 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.
- ✗
Batch processing using Hadoop MapReduce every 24 hours.
Why it's wrong here
Batch processing introduces high latency, not suitable for near-real-time.
- ✗
Batch processing using nightly ETL jobs.
Why it's wrong here
Nightly ETL adds at least 24 hours delay.
- ✗
Single-node database with periodic inserts.
Why it's wrong here
Cannot handle high velocity and lacks scalability.
- ✓
Stream processing using Apache Kafka and Spark Streaming.
Why this is correct
Kafka ingests streaming data, Spark Streaming processes it with low latency.
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