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Oracle 1z0-1127-24 Exam Syllabus Topics:
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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q29-Q34):
NEW QUESTION # 29
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?
Answer: B
Explanation:
In the "Show Likelihoods" feature of language model token generation, a higher number assigned to a token indicates that the token is more likely to follow the current token. This likelihood is based on the model's probability distribution, where tokens with higher probabilities are considered more likely to be the next in the sequence. This feature helps in understanding the model's decision-making process and the relative probabilities of different tokens.
Reference
Technical documentation on language model token generation
Research articles on probability distributions in generative models
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NEW QUESTION # 30
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?
Answer: B
Explanation:
Retrieval-Augmented Generation (RAG) combines retrieval mechanisms with text generation, allowing models to pull external knowledge before generating responses.
How RAG Works:
The model retrieves relevant documents from an external database.
Uses this retrieved information to generate factually grounded responses.
Reduces hallucinations, improving accuracy and context relevance.
Why Other Options Are Incorrect:
(A) is incorrect because RAG modifies the retrieved text by integrating it into a generated response.
(B) is incorrect because RAG retrieves and uses data, not just stores it.
(C) is incorrect because RAG relies on external knowledge, whereas LLMs alone use internal pre-trained knowledge.
๐น Oracle Generative AI Reference:
Oracle AI applies RAG techniques to improve enterprise AI applications, enhancing fact-based text generation.
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NEW QUESTION # 31
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?
Answer: B
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NEW QUESTION # 32
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?
Answer: B
Explanation:
The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:
Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.
High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.
Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.
Reference
Research articles on the role of temperature in generative models
Technical guides for tuning generative AI models in OCI
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NEW QUESTION # 33
What is the primary purpose of LangSmith Tracing?
Answer: A
Explanation:
The primary purpose of LangSmith Tracing is to debug issues in language model outputs. LangSmith Tracing allows developers to trace and analyze the sequence of operations and decisions made by the model during the generation process. This helps identify and resolve problems, ensuring the model's outputs are accurate and reliable.
Reference
LangSmith documentation on tracing and debugging
Tutorials on using tracing tools for language model development
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NEW QUESTION # 34
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