Model Development

Exam objectives are the official skill domains Databricks Certified Machine Learning Associate tests. Below are the official DATABRICKS docs our questions on this objective cite — review them, then practise.

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Official references for this objective

Databricks — Building a Foundation Model for Energy on Databricks with Applied Computing | Databricks

an AI model that is both physics-informed and domain specific
Databricks — Accelerating AI From Model to Impact at Workday With Databricks Apps | Databricks

Workday compressed delivery to weeks, launched three production AI applications in a single year, and scaled its flagship Sales Companion to 4,500+ active users
Databricks — Dr. Jekyll and Mr. H-AI-de: Using MLflow and AI Judges to measure model alignment and safety | Databricks

Emergent misalignment sent shockwaves through the AI community in 2025. We discovered that a model fine-tuned on narrow tasks could suddenly pivot from a "helpful
Databricks — A Simple, Managed Platform - Modern Feature Engineering on Databricks | Databricks

Define what a feature is (source, entity, aggregation, time window) and let the platform compute it correctly in every context.
Databricks — From Training to Production: MLOps for Deep Learning on Databricks | Databricks

distributed training with MLflow tracking, model registry workflows for large models, GPU-aware serving with traffic splitting and autoscaling
Databricks — High-Throughput, Low-Latency: The Databricks Playbook for Production Model Serving | Databricks

autoscaling strategies, and optimizations across GPU/CPU utilization, request routing, and caching that enable sustained high throughput without sacrificing latency or cost
Databricks — How Databricks Apps Facilitated Model Retraining | Databricks

in 3 weeks, a fully-fledged computer vision labeling application could be built using apps, saving our retread SMEs months of manual effort