Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
Which of the following describes the 'drift' phenomenon in the context of LLM monitoring?
⚠ Common exam trap
Candidates often confuse LLM drift with traditional feature drift, missing that LLM drift specifically refers to evolving user query patterns and intents over time.
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
✓
A shift in user query patterns over time.
LLM drift occurs when the input data distribution or the nature of user queries changes over time, causing the model to perform worse than during validation. Because user expectations and language usage evolve, a model that performed well at launch may become less accurate as queries drift into new domains or change in tone. Monitoring this drift is essential for proactive maintenance and model updating.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model's weights change during inference.
Why it's wrong here
Model weights are static during inference in a production serving endpoint. Drift refers to the shift in input data or the changing quality of outputs relative to the environment, not a physical change in the model's neural network parameters or architecture during the serving phase.
- ✓
A shift in user query patterns over time.
Why this is correct
Drift in generative AI often manifests as a divergence in user query patterns, where the model encounters prompts that are significantly different from its training or fine-tuning data. This shift leads to degradation in performance, as the model was not optimized for these new interaction patterns or domains.
- ✗
The model generating the same answer too often.
Why it's wrong here
This describes a specific failure mode often called 'repetitiveness' or 'low diversity.' While this is a quality problem, it is not what is defined as 'drift' in machine learning monitoring. Drift refers to a statistical shift in the underlying data distribution, not the specific diversity of outputs.
- ✗
Increased latency in the serving endpoint.
Why it's wrong here
Latency is a performance metric related to infrastructure throughput and compute efficiency. It is not drift. Drift specifically refers to a degradation in the quality or predictive accuracy of the model's output resulting from a change in the input data distribution, not the processing speed of the service.
About these practice questions
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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-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.