DA0-002 Data Concepts and Environments Practice Question
An e-commerce company wants to provide real-time personalized product recommendations based on customer browsing behavior. Currently, they have a traditional data warehouse that processes batch updates every night. The marketing team complains that recommendations are outdated within hours because customers see yesterday's data. The data engineer needs to modify the architecture to support near-real-time analytics. The budget is limited, and the existing warehouse infrastructure must be reused as much as possible. Which architectural change would best meet the requirement?
⚠ Common exam trap
It's easy for candidates to assume increasing batch frequency (Option D) is sufficient for near-real-time needs, but the Data+ exam tests the understanding that 'near-real-time' typically requires sub-minute latency, which batch processing cannot achieve due to scheduling overhead and resource contention.
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
✓
Implement a streaming data pipeline (e.g., Apache Kafka) that feeds a real-time recommendation engine.
Implementing a streaming data pipeline like Apache Kafka enables the ingestion and processing of customer browsing events in near real-time, feeding a dedicated recommendation engine that can update recommendations within seconds or minutes. This approach reuses the existing data warehouse for historical analytics and batch reporting while adding a lightweight streaming layer for low-latency recommendations, aligning with the limited budget and reuse requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the warehouse with an in-memory database for real-time processing.
Why it's wrong here
Incorrect: Expensive and may not reuse existing infrastructure.
- ✗
Add more nodes to the warehouse cluster to speed up batch processing.
Why it's wrong here
Incorrect: Batch processing inherently has latency; scaling doesn't achieve real-time.
- ✓
Implement a streaming data pipeline (e.g., Apache Kafka) that feeds a real-time recommendation engine.
Why this is correct
Correct: Streaming enables real-time analytics without replacing the warehouse.
- ✗
Increase the frequency of batch load from nightly to every hour.
Why it's wrong here
Incorrect: Hourly updates still have up to 60-minute latency, not real-time.
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