DA0-002 Data Acquisition and Preparation Practice Question
A data architect is designing an ETL pipeline to ingest streaming data from IoT sensors. The data must be available for real-time analytics. Which acquisition method is best?
⚠ Common exam trap
Candidates often confuse 'real-time' with 'frequent batch' and choose hourly polling (Option B), not realizing that real-time analytics requires sub-second latency, not just periodic updates.
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
✓
Real-time streaming via API
Real-time streaming via API is the best method because IoT sensors generate continuous data that must be ingested with sub-second latency for real-time analytics. APIs (e.g., REST, WebSocket, or MQTT) enable event-driven ingestion, allowing the ETL pipeline to process each sensor reading as it arrives, which is essential for time-sensitive use cases like anomaly detection or live monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Real-time streaming via API
Why this is correct
Real-time streaming via API pushes each IoT sensor reading to the pipeline as it is produced, rather than buffering batches. This satisfies the requirement that data be available for real-time analytics, unlike batch acquisition which introduces latency before availability.
- ✗
Poll sensors every hour
Why it's wrong here
Hourly polling yields data at best 60 minutes old, failing the real-time analytics requirement. Polling suits slowly changing metrics where freshness is not critical, which makes it tempting for sensor data, but continuous streaming ingestion is needed when each reading must be processed on arrival.
- ✗
Manually upload sensor logs
Why it's wrong here
Manual uploads are human-triggered and cannot sustain continuous IoT ingestion or real-time analytics. This approach suits one-off migrations or ad hoc file transfers, which is why it appears in data-loading discussions, but automated streaming is required for sensor telemetry that must be analysed immediately.
- ✗
Batch load daily CSV files
Why it's wrong here
Daily CSV batch loads introduce latency of up to 24 hours, so real-time analytics on sensor readings is impossible. Batch ingestion suits periodic reporting and bulk historical loads, which is why it tempts architects, but streaming ingestion is required when data must be analysed as it arrives.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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