Databricks-ML-Pro Model Development Practice Question
A machine learning engineer is using MLflow to track experiments on Databricks. They notice that when they run `mlflow.log_artifact` with a local file path inside a notebook, the artifact is stored in the run's artifact location, but when they run the same code in a job cluster, the artifact is missing. The job cluster uses the same MLflow tracking server and experiment. What is the most likely reason for the missing artifact?
⚠ Common exam trap
The trap here is assuming that local filesystem paths behave identically in interactive notebooks and job clusters, overlooking that job clusters do not share local storage across nodes.
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 local file path used in the job cluster points to a location that is not accessible from the driver, such as a path on the local filesystem of a different node, so the file does not exist when `log_artifact` is called.
In a Databricks job cluster, code may execute on the driver, but local filesystem paths are not shared across nodes. If the artifact file is created on an executor or a different node, the driver cannot access it when `log_artifact` is called. To ensure artifacts are logged, write them to a distributed storage location such as DBFS or a Unity Catalog volume, then log from there.
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 rejects artifacts from job clusters because they lack an interactive user session, so the artifact is discarded.
Why it's wrong here
MLflow tracking servers do not discriminate based on interactive versus job clusters. Artifacts are logged with the run's credentials, and job clusters can log artifacts normally if the file path is valid. The rejection scenario is not a documented behavior. The issue is local file accessibility, not server-side filtering.
- ✓
The local file path used in the job cluster points to a location that is not accessible from the driver, such as a path on the local filesystem of a different node, so the file does not exist when `log_artifact` is called.
Why this is correct
In a job cluster, code may run on the driver, but if the file is created on an executor's local filesystem or a path that is not synchronized, `log_artifact` on the driver cannot find it. Unlike notebooks attached to an interactive cluster, job clusters do not share local storage across nodes. The file must be written to a distributed location like DBFS or Unity Catalog volume before logging.
- ✗
The artifact location is configured with a short-lived credential that expires before the job cluster completes, causing the artifact upload to fail silently.
Why it's wrong here
While credential expiry can cause upload failures, it would typically raise an error and affect all runs, not just job clusters. In this scenario, the same tracking server and experiment are used, so credentials are not the differentiating factor. The missing artifact is more directly explained by a local path that is not available on the driver node of the job cluster.
- ✗
The job cluster does not have the MLflow library installed, so `log_artifact` silently fails without logging an error.
Why it's wrong here
Databricks job clusters include MLflow by default when using Databricks Runtime for Machine Learning. Even if not, `log_artifact` would raise an exception rather than silently fail. The missing artifact is more likely due to a path issue. The library installation is not the root cause in this scenario.
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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-Pro 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-Pro exam.