Databricks-GenAI-Assoc Design Applications Practice Question
Exhibit
ERROR: 'Invalid signature for model logging. Expected: [input_tensor, output_tensor]'
Refer to the exhibit. What is the cause of this error when logging a RAG chain to MLflow?
⚠ Common exam trap
Candidates often assume the error is related to model size or environment libraries. They overlook the critical requirement that MLflow must have a defined input/output schema for inference.
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 model was logged without a signature defining input and output types.
This error occurs because the model signature is missing or incorrectly defined, which is required for MLflow to perform input validation and inference. When serving a RAG chain, MLflow needs to know exactly what the input schema looks like so it can properly route requests and manage data types. Providing a valid signature ensures the model is deployable and functional.
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 vector index is offline.
Why it's wrong here
The model signature error is related to the code or the model registration process, not the status of the vector index. While an offline index would cause retrieval errors at runtime, it would not trigger a model signature validation error during the logging or registration process.
- ✓
The model was logged without a signature defining input and output types.
Why this is correct
MLflow Models require a signature to define the expected input and output structure. This signature is critical for serving, as it allows the platform to validate incoming requests. Without it, the model cannot be registered or deployed to a serving endpoint, resulting in the error shown during the logging process.
- ✗
The user has insufficient Unity Catalog permissions.
Why it's wrong here
Insufficient permissions would typically result in an 'Access Denied' or '403 Forbidden' error when trying to write to the MLflow Registry or workspace. A signature error specifically points to a structural mismatch in the model object being logged, not to environmental access issues.
- ✗
The LLM endpoint is overloaded.
Why it's wrong here
Overload would manifest as a timeout or a '503 Service Unavailable' error during runtime. Signature validation happens at the development/deployment phase, before the application is even serving requests. It is a metadata definition issue, not a runtime capacity issue.
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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-GenAI-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-GenAI-Assoc exam.