Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
Which THREE of the following are common challenges when monitoring LLM applications in production that differ significantly from traditional ML monitoring? (Choose three)
⚠ Common exam trap
Candidates often apply traditional ML monitoring assumptions, failing to account for LLM non-determinism, subjective evaluation, and the absence of a single ground truth.
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
✓
Non-deterministic output behavior.
LLMs introduce unique challenges because their outputs are non-deterministic, high-dimensional, and often lack a single 'correct' answer. Traditional monitoring focuses on numerical drift and binary classification metrics like precision/recall. LLM evaluation requires semantic understanding, nuance assessment, and the ability to handle unstructured text, necessitating specialized tools like LLM-as-a-judge or human-in-the-loop validation to manage the subjectivity inherent in generative AI outputs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Non-deterministic output behavior.
Why this is correct
LLMs can produce different outputs for the same prompt due to temperature settings or inherent stochasticity. This makes simple equality checks useless for evaluation. Monitoring must account for this variance, requiring probabilistic or semantic similarity metrics rather than static, exact-match validation common in traditional classification tasks.
- ✓
Difficulty in defining a single 'ground truth' for responses.
Why this is correct
In generative tasks, there are often many valid ways to phrase a correct answer. Unlike a classification model where a label is either right or wrong, LLM outputs require qualitative judgment, which is inherently more subjective and difficult to automate without robust evaluation frameworks.
- ✗
Lack of high-throughput API endpoints.
Why it's wrong here
LLM services on Databricks are designed for high-throughput and low-latency, mirroring traditional API capabilities. The challenge in LLM monitoring is the nature of the data and the assessment of quality, not the ability of the underlying infrastructure to handle high request volumes and concurrent connections.
- ✓
High cost of manual evaluation at scale.
Why this is correct
Human evaluation is the gold standard but is prohibitively expensive and slow at production scale. Monitoring strategies must therefore balance automated proxy metrics (like model-based evaluation) with periodic human spot-checks to ensure the automated systems remain aligned with human-perceived quality over the long term.
- ✗
The model's inability to connect to internet data.
Why it's wrong here
While some models are restricted, this is a model capability feature rather than a specific monitoring challenge. Most enterprise RAG architectures explicitly use internal, private data. The monitoring challenge remains the quality of the generation, regardless of whether the data source is the internet or internal.
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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
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