Databricks-ML-Assoc Model Deployment Practice Question
Which strategy is most effective for managing model drift in a production Databricks environment?
⚠ Common exam trap
Candidates often select real-time model re-training or offline batch metrics aggregation, missing that logging inference data to Delta tables is the core requirement for drift analysis.
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
✓
Log inference data to Delta tables for analysis
Managing model drift requires active monitoring of input data and output predictions. Setting up Databricks Model Serving with feature tables allows for logging of inference requests and responses to a Delta table. By comparing these production distributions against the training data using tools like Great Expectations or custom SQL queries, teams can detect shifts in data patterns that necessitate retraining or model adjustments to maintain performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually check the model accuracy once a year
Why it's wrong here
Annual checks are insufficient for modern data environments where data distribution shifts can occur in minutes or hours. Drift management requires continuous or automated evaluation. Without frequent monitoring, models can degrade silently, leading to incorrect business decisions and a loss of trust in the machine learning system.
- ✗
Re-deploy the model with new data every hour
Why it's wrong here
Continuous re-deployment without evaluating for drift is inefficient and risky. It can lead to overfitting on noisy data or instability in production. A controlled approach involving validation and drift detection is much better than blindly cycling models. Automated retraining should only trigger based on performance thresholds or drift metrics.
- ✓
Log inference data to Delta tables for analysis
Why this is correct
Logging inference inputs and predictions to Delta tables creates an audit trail that enables monitoring. This data can be analyzed to measure drift in input features or model outputs. This is the industry-standard approach in Databricks for building a feedback loop that informs when a model needs to be updated.
- ✗
Use a static model that never changes
Why it's wrong here
Static models are highly prone to performance decay in non-stationary environments. As data patterns change over time, the model's relevance decreases. A robust MLOps strategy must account for the reality of data evolution, making static models a poor choice for any application where input data is subject to change.
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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-ML-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-ML-Assoc exam.