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Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 Practice Test

500 questions with instant explanations, domain breakdown, and wrong-answer analysis. Built for the real exam.

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Domain score breakdown
Real exam: 90 min
Pass mark: 65%

Sample questions with explanations

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Q1Fundamentals of Large Language Modelsmedium
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A company is deploying a large language model for a customer service chatbot. The model needs to understand industry-specific jargon and maintain low latency. Which approach best balances these requirements?

AEmploy retrieval-augmented generation (RAG) with a general model
BRely solely on prompt engineering with a general model
CUse a large general-purpose LLM with zero-shot prompting
Fine-tune a small open-source LLM on domain-specific dataCorrect

Fine-tuning a small open-source LLM on domain-specific data is the best approach because it adapts the model to understand industry-specific jargon while keeping the model small enough to maintain low latency. Unlike larger models, a fine-tuned small model can run efficiently on …Read full explanation

Q2Fundamentals of Large Language Modelshard
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A data scientist observes that their fine-tuned LLM performs well on training data but generates repetitive and dull responses in production. What is the most likely cause and best solution?

AThe model is overfitted; apply stronger regularization
The temperature is set too low; increase temperature during inferenceCorrect
CThe training data lacks diversity; add more varied examples
DThe model has too many layers; reduce model size

The model's repetitive and dull responses indicate that the temperature parameter is too low, causing the model to always select the most probable tokens, leading to deterministic and monotonous outputs. Increasing temperature during inference introduces randomness into token sam…Read full explanation

Q3Fundamentals of Large Language Modelseasy
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An organization wants to use an LLM to summarize legal documents. Which consideration is most important for ensuring accurate summaries?

Fine-tune the model on a curated legal corpusCorrect
BUse the largest available general-purpose model
CRely on zero-shot summarization with careful prompting
DPre-train a new model from scratch on legal texts

Legal documents require precise understanding, so fine-tuning on legal data is critical. Option B is wrong because larger models don't guarantee domain accuracy. Option C is wrong because pre-training from scratch is expensive and unnecessary. Option D is wrong because zero-shot …Read full explanation

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