Databricks-ML-Assoc Model Development Practice Question
A data scientist is using MLflow to track experiments. They notice that all runs from a particular notebook are being logged to the default experiment instead of the experiment they intended to use. They have already called mlflow.start_run() without specifying an experiment ID. What is the most likely cause?
⚠ Common exam trap
The trap here is assuming that start_run automatically uses the experiment associated with the notebook or script; it requires explicit setting.
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
✓
The active MLflow experiment was not set using mlflow.set_experiment before starting the run.
MLflow logs runs to the active experiment, which is set via mlflow.set_experiment. If not set, runs go to the default experiment (ID 0). The start_run function does not automatically infer the intended experiment from the notebook context. To fix this, the data scientist should call mlflow.set_experiment with the desired experiment name or pass experiment_id to start_run.
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 MLflow tracking server is misconfigured, causing runs to be redirected to the default experiment.
Why it's wrong here
A misconfigured tracking server would typically result in connection errors or runs not being logged at all, rather than being silently redirected to the default experiment. The tracking server URI determines where logs are stored but does not override the experiment selection logic. The issue is more likely due to not setting the experiment.
- ✗
The MLflow client library version is outdated and does not support custom experiments.
Why it's wrong here
MLflow has supported custom experiments for many versions. An outdated client might lack newer features, but the basic functionality of setting an experiment and logging runs has been stable. The described behavior is not a known symptom of an outdated client; it is a common user error when the experiment is not set.
- ✓
The active MLflow experiment was not set using mlflow.set_experiment before starting the run.
Why this is correct
MLflow uses the active experiment to determine where runs are logged. If mlflow.set_experiment is not called, MLflow defaults to the experiment with ID 0, often named 'Default'. The start_run function does not automatically associate runs with a specific experiment unless the experiment is set beforehand or specified via the experiment_id argument.
- ✗
The run was started with nested=True, which forces logging to the default experiment.
Why it's wrong here
The nested=True argument in start_run creates a nested run under the active run, but it does not change the experiment. Nested runs inherit the experiment of the parent run. If no experiment is set, they still log to the default. The nested parameter is unrelated to experiment selection.
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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.