Databricks-GenAI-Assoc Application Development Practice Question
A team is deploying a GenAI application using Mosaic AI Model Serving. They want to ensure that the endpoint can handle sudden spikes in traffic without dropping requests. Which feature should they configure?
⚠ Common exam trap
Many candidates confuse provisioned throughput with autoscaling; provisioned throughput is fixed and does not automatically scale during unexpected spikes.
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
✓
Autoscaling
Autoscaling is designed to automatically adjust the serving endpoint's capacity based on incoming traffic. It ensures that the endpoint can handle sudden spikes by adding more resources and scales down when traffic decreases. This maintains availability and performance without manual intervention, making it the correct choice for handling variable loads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Request batching
Why it's wrong here
Request batching groups multiple requests into a single batch to improve throughput, but it can increase latency and does not dynamically scale resources. It may help with efficiency but does not prevent dropped requests during sudden spikes without autoscaling.
- ✓
Autoscaling
Why this is correct
Autoscaling dynamically adjusts the number of concurrent requests the endpoint can handle based on traffic. It scales resources up during spikes and down during lulls, ensuring requests are not dropped. This is the correct feature to handle sudden traffic increases while optimizing cost.
- ✗
Model versioning
Why it's wrong here
Model versioning allows deploying multiple versions of a model and routing traffic between them, but it does not affect the endpoint's ability to scale with traffic. It is used for A/B testing or gradual rollouts, not for handling load spikes.
- ✗
Provisioned throughput
Why it's wrong here
Provisioned throughput reserves a fixed capacity for the endpoint, which can handle expected traffic but may not scale automatically during sudden spikes. It is suitable for predictable workloads but lacks the elasticity to handle unexpected increases without manual intervention.
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.