Databricks-ML-Assoc Databricks Machine Learning Practice Question
Exhibit
2023-10-27 10:00:00 [ERROR] Model inference failed: Connection refused to serving-endpoint-x 2023-10-27 10:00:05 [INFO] Retrying connection to endpoint... 2023-10-27 10:00:10 [ERROR] Model inference failed: Request timeout
Refer to the exhibit. The logs indicate a persistent connection failure for a Databricks Model Serving endpoint. What is the most likely cause?
⚠ Common exam trap
Candidates often attribute connection failures to code syntax bugs rather than investigating infrastructure limits, endpoint overload, or misconfigured scaling resources.
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 endpoint is overloaded or incorrectly configured.
A 'Connection refused' error coupled with a request timeout typically indicates that the serving endpoint is either overloaded, configured incorrectly with insufficient resources, or facing network connectivity issues. Since the error persists despite retries, it points to a failure in the endpoint infrastructure or scaling capacity, which requires an investigation of the endpoint's resource allocation and the network configuration within the Databricks workspace to restore service reliability.
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 artifacts are missing.
Why it's wrong here
If artifacts were missing, the endpoint would likely fail during the initialization phase rather than returning 'connection refused' or 'timeout' errors during runtime. These errors indicate that the infrastructure is up but cannot handle the incoming traffic, pointing toward resource contention or configuration issues rather than missing files.
- ✓
The endpoint is overloaded or incorrectly configured.
Why this is correct
Connection refused and timeouts are classic symptoms of an endpoint being overwhelmed or improperly configured to accept incoming traffic. This suggests that the current resources assigned to the serving endpoint are insufficient to manage the request load, requiring an increase in instances or a change to the scaling configuration.
- ✗
The input data format is incorrect.
Why it's wrong here
Input format errors typically result in a 400 Bad Request response rather than connection failures. A connection error implies that the request never even reached the model processing logic, but was blocked at the network or endpoint infrastructure level, making data format mismatches an unlikely source of this error.
- ✗
The model version is archived.
Why it's wrong here
Archiving a model version generally prevents it from being used for new deployments but doesn't necessarily cause connection timeouts for already deployed endpoints. The issue described is a runtime network/load error, which is unrelated to the archival status of the specific model version currently deployed to the endpoint.
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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.