Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A team deploys a RAG agent to a Mosaic AI Model Serving endpoint. During load testing, requests intermittently return HTTP 429 responses even though the endpoint shows healthy replicas. The agent calls an external vector search index and a foundation model endpoint. Which action most directly addresses the 429 responses?
⚠ Common exam trap
The trap here is reaching for client-side retries as the fix for 429s, when a sustained 429 pattern points to insufficient endpoint capacity rather than transient failure.
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
✓
Increase the endpoint's provisioned concurrency and configure appropriate rate limits so the agent can handle the incoming request rate.
A 429 from a Mosaic AI Model Serving endpoint signals that inbound request rate exceeds the endpoint's provisioned capacity or configured rate limits. Raising provisioned concurrency and setting realistic rate limits aligns capacity with the load-test traffic and directly removes the rate-limiting responses. Client retries, smaller prompts, and index relocation do not increase the endpoint's ability to accept concurrent requests, so they leave the root cause unaddressed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the endpoint's provisioned concurrency and configure appropriate rate limits so the agent can handle the incoming request rate.
Why this is correct
HTTP 429 indicates the endpoint is rate-limiting requests because the incoming rate exceeds provisioned concurrency or configured limits. Raising provisioned concurrency and tuning rate limits lets the agent absorb the load, which directly addresses the 429s rather than masking them. This aligns capacity with the observed traffic during load testing.
- ✗
Move the vector search index into the same Unity Catalog schema as the agent model to reduce cross-service latency.
Why it's wrong here
Co-locating the index in the same schema improves organization and governance but does not increase the serving endpoint's request capacity. The 429 responses come from the endpoint rate-limiting inbound requests, not from index placement or network latency. This change would not reduce the rate-limit errors observed during load testing.
- ✗
Add retry logic with exponential backoff and jitter to the client calling the endpoint.
Why it's wrong here
Retries help with transient failures but do not fix an endpoint that is consistently rate-limiting because of insufficient capacity. Under sustained load, retries can amplify traffic and worsen the 429 rate. While backoff is a good client-side resilience practice, it treats the symptom rather than the capacity shortfall causing the responses.
- ✗
Switch the agent's foundation model calls to a smaller context window to reduce token usage per request.
Why it's wrong here
Reducing context window size may lower token cost but does not change the endpoint's request-rate capacity, which is what drives 429 responses. The agent could still exceed provisioned concurrency even with smaller prompts. This optimization is useful for cost and latency, not for resolving rate-limit errors under load.
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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.