Building LLM Applications with RAG and Vector Search →mediumMultiple ChoiceObjective-mapped

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Exhibit

Refer to the exhibit.

error log:
{
  "timestamp": "2025-03-15T10:30:00Z",
  "source": "oci-generative-ai-inference",
  "message": "CohereClientException: 429 Too Many Requests",
  "details": {
    "retryAfter": 60,
    "modelId": "cohere.command-r-plus-08-2024"
  }
}

A developer receives the above error when querying a RAG application. What is the most likely cause and recommended action?

⚠ Common exam trap

Oracle often tests the distinction between HTTP status codes (429 vs 400 vs 404 vs 410) to see if candidates can map the exact error to the correct cause rather than guessing based on general troubleshooting.

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 API rate limit has been exceeded; wait for the retry period and implement exponential backoff.

The 429 (Too Many Requests) error indicates the API rate limit has been exceeded. In OCI Generative AI services, rate limits are enforced per tenancy and per region; the recommended action is to wait for the retry-after period and implement exponential backoff to avoid overwhelming the service.

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 API rate limit has been exceeded; wait for the retry period and implement exponential backoff.
Why this is correct
429 means rate limit.
✗
The model is deprecated; update to the latest model.
Why it's wrong here
Error is about rate limit, not deprecation.
✗
The endpoint URL is incorrect; verify the OCI region endpoint.
Why it's wrong here
Error is from Cohere API, not endpoint.
✗
The request payload is malformed; check the input format.
Why it's wrong here
No syntax error indicated.

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1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Exhibit

Why each option matters

Option-by-option breakdown

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