Databricks-GenAI-Assoc Application Development Practice Question
A team is deploying a fine-tuned LLM using Mosaic AI Model Serving. To reduce the cost of serving the model while maintaining acceptable performance, which strategy should be prioritized?
⚠ Common exam trap
Candidates incorrectly suggest increasing GPU instance sizes to handle LLM costs, overlooking model optimization techniques that reduce hardware requirements altogether.
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 model quantization to reduce memory footprint and hardware requirements.
Using smaller, optimized model architectures or quantization techniques significantly reduces the compute and memory footprint required for serving. By shrinking the model size, you can effectively use smaller instance types for serving, leading to direct cost savings. Furthermore, optimizing the underlying serving configuration by choosing the right hardware type ensures you are not paying for expensive GPU capacity when CPU-based serving would suffice for your throughput needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the largest available instance type to ensure zero request failures.
Why it's wrong here
Over-provisioning hardware is a primary cause of high costs in AI deployments. Larger instances are often underutilized, leading to wasted budget. Performance should be managed through intelligent scaling and model optimization rather than simply throwing expensive hardware at the problem without verifying the actual compute requirements.
- ✓
Implement model quantization to reduce memory footprint and hardware requirements.
Why this is correct
Quantization reduces the precision of the model weights, which leads to smaller memory usage and faster inference times. This allows the model to run on smaller, cheaper instances without a significant drop in accuracy, providing a highly effective way to balance performance with operational costs in production environments.
- ✗
Switch to a Multi-Model Serving endpoint regardless of throughput.
Why it's wrong here
Multi-Model Serving is designed for sharing compute resources between models, but it can introduce latency overhead and complexity. It is not necessarily cheaper if the models have vastly different resource requirements. It should only be used if the workload profile justifies the shared infrastructure, not as a blanket cost-saving measure.
- ✗
Disable all logging to save on storage costs.
Why it's wrong here
Disabling logging is a major security and operational risk. The storage costs for inference logs are negligible compared to the cost of compute instances. Losing the ability to monitor model drift, performance, and audit requests makes the system unmaintainable and non-compliant, which is a poor trade-off for minor savings.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.