Databricks-GenAI-Assoc Application Development Practice Question
An AI engineer is designing a scalable customer support application on Databricks that integrates custom vector search indexes with a fine-tuned LLM. Which TWO architectural components are essential for enabling efficient similarity search and low-latency retrieval within the Databricks ecosystem? (Choose TWO)
⚠ Common exam trap
Candidates often select generic options like 'Delta Tables' or 'MLflow' without identifying the specific services (Vector Search and Model Serving) required for low-latency RAG performance.
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
✓
Databricks Vector Search
Databricks Vector Search provides serverless vector database capabilities to store and query embeddings efficiently without managing external infrastructure. Combined with Databricks Model Serving for hosting the LLM, these native services ensure high throughput, enterprise-grade security, and low-latency inference required for modern retrieval-augmented generation applications built on the Databricks platform.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Databricks Vector Search
Why this is correct
Databricks Vector Search is a serverless vector database natively integrated into the Databricks platform. It automatically synchronizes with Delta tables, manages embedding indexes, and provides high-performance similarity search APIs essential for retrieving contextually relevant documents in RAG applications.
- ✗
An external third-party Hadoop cluster managed via SSH tunnels
Why it's wrong here
External Hadoop clusters managed via SSH tunnels introduce severe operational overhead, security risks, and network latency. Modern Databricks applications leverage native serverless and cloud-optimized services rather than legacy distributed computing architectures for real-time inference tasks.
- ✓
Databricks Model Serving
Why this is correct
Databricks Model Serving delivers highly available, low-latency, enterprise-grade endpoints for foundational models, custom LLMs, and embedding models. It automatically scales based on incoming traffic demands, making it a critical component for serving real-time chat and completion requests.
- ✗
A manual cron job running on a local developer laptop
Why it's wrong here
A cron job on a developer laptop provides no scalable, always-available retrieval path and cannot serve Databricks Vector Search indexes at low latency. It tempts for scheduled batch refresh of embeddings, but that scenario is offline index maintenance, not the online similarity-search architecture the application requires.
- ✗
A static CSV file stored on local driver disk storage
Why it's wrong here
Static CSV files stored on local driver storage cannot scale concurrently and lack transactional consistency, indexing capabilities, and high availability. Production RAG applications require distributed, ACID-compliant storage layers like Delta Lake alongside dedicated vector indexes.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
About these practice questions
Courseiva writes every Databricks-GenAI-Assoc question from scratch — 330 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-GenAI-Assoc exam.