Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
Exhibit
log_entry: 2023-10-27 10:00:00 [ERROR] Model inference failed: Connection to Vector Store timed out. Retrying in 5s... 2023-10-27 10:00:05 [ERROR] Max retries exceeded.
Refer to the exhibit. What is the most likely cause for this error in a production RAG application?
⚠ Common exam trap
Candidates often blame the model or the code logic itself. They fail to recognize that RAG-specific errors are usually infrastructure-related, specifically network timeouts when the model attempts to query a vector store.
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
✓
Network connectivity between Model Serving and the vector store is misconfigured.
The error indicates a network timeout when connecting to the vector store. This is a common connectivity issue between the serving endpoint and the data source. Investigating network security groups, firewall rules, or DNS resolution issues within the Databricks environment is necessary to resolve it. Ensuring robust, low-latency connectivity to the retrieval source is fundamental for reliable RAG performance and minimizing application downtime in production.
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 is too large for the GPU memory.
Why it's wrong here
A memory issue would manifest as an OOM (Out of Memory) error, not a network timeout related to a connection to an external vector store. This error clearly identifies a communication failure between the model service and the database, which is a network or configuration layer issue.
- ✓
Network connectivity between Model Serving and the vector store is misconfigured.
Why this is correct
The timeout error explicitly points to a failure in establishing a connection to the vector store. This suggests that the network routing, VPC peering, or security group rules are blocking the communication path, which is a common deployment issue that must be addressed to restore RAG service functionality.
- ✗
The model weights are corrupted.
Why it's wrong here
Corrupted weights would cause errors during the model loading phase or during the inference computation itself, such as segmentation faults or arithmetic errors. It would not typically present as a connection timeout to an external dependency, as the model service would have already initialized before attempting a query.
- ✗
The input prompt is too long for the model.
Why it's wrong here
Prompt length limits result in model-specific errors, typically token limits or context window errors. They do not trigger network timeout errors regarding external service connections. This error is specific to the infrastructure communication path, not the content of the request being processed by the generative model.
Visual reference
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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.