AI Associate Data for AI Practice Question
For a real-time AI application that requires low-latency access to customer interaction data, which storage solution is most appropriate?
⚠ Common exam trap
Salesforce often tests the misconception that relational databases are always the best for structured data, but the trap here is that candidates overlook the strict latency requirement and choose a relational database (Option C) without considering that complex joins and disk-based storage make it too slow for real-time AI workloads.
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
✓
In-memory data store.
In-memory data stores (e.g., Redis, Memcached) store data in RAM rather than on disk, providing sub-millisecond read/write latencies essential for real-time AI applications that need immediate access to customer interaction data. This eliminates disk I/O bottlenecks and enables high-throughput, low-latency data retrieval for time-sensitive inference or decision-making.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Flat files on a network drive.
Why it's wrong here
Network drives have higher latency and lack concurrency support.
- ✓
In-memory data store.
Why this is correct
In-memory storage offers microsecond latency, ideal for real-time AI.
- ✗
Relational database with complex joins.
Why it's wrong here
Complex joins add overhead and are not optimal for low latency.
- ✗
Data lake with batch processing.
Why it's wrong here
Batch processing introduces latency and is not suitable for real-time.
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This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.