Databricks-ML-Assoc ML Workflows Practice Question
Exhibit
{
"mlflow_config": {
"run_id": "xyz123",
"artifact_uri": "s3://my-bucket/mlflow/xyz123",
"model_flavor": "sklearn"
},
"error": "Permission denied: Unable to write to artifact path"
}Refer to the exhibit. A data scientist is attempting to log a model to an S3 bucket via MLflow, but they receive the error shown. What is the most likely root cause?
⚠ Common exam trap
Candidates often blame incorrect MLflow syntax for bucket write failures, overlooking cloud-level permissions like the compute cluster's instance profile configuration.
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 compute cluster's instance profile lacks write access to the S3 bucket.
The 'Permission denied' error indicates that the identity running the Databricks notebook or job lacks the necessary IAM permissions to write to the specified S3 bucket. In Databricks, interacting with external storage requires proper instance profile or service principal configuration. This error underscores the importance of cloud identity and access management (IAM) in ML workflows, as secure, authorized access to storage is foundational for maintaining the integrity and security of model artifacts during the development lifecycle.
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 S3 bucket is full and cannot accept new model artifacts.
Why it's wrong here
A full bucket would return an 'Insufficient Space' or 'Quota Exceeded' error, not a 'Permission denied' error. Permission errors are exclusively related to the authentication and authorization policies (IAM) assigned to the compute identity, not the physical capacity or resource availability of the target storage container.
- ✗
The MLflow model flavor is not compatible with the S3 storage protocol.
Why it's wrong here
The model flavor (sklearn) is independent of the underlying storage system (S3). MLflow's abstraction layer handles the writing of various flavors to different storage backends, provided the environment has the correct permissions. The error message explicitly points to a permissions issue, not a compatibility or library support failure.
- ✓
The compute cluster's instance profile lacks write access to the S3 bucket.
Why this is correct
This is the most common cause for permission errors when interacting with S3 from Databricks. The instance profile or service principal attached to the cluster must have explicit IAM permissions granted to write to the specific bucket path. Without these permissions, all write operations will be blocked by the cloud provider.
- ✗
The run_id 'xyz123' already exists and is locked by another user.
Why it's wrong here
MLflow allows creating multiple runs, and run IDs are generated to be unique. Even if a run existed, an attempt to log to it would not cause a 'Permission denied' error at the storage level. Such an issue would be handled by the MLflow server's application logic, not the storage backend.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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-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.