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1z0-1127-24 Learning Materials: Oracle Cloud Infrastructure 2024 Generative AI Professional & 1z0-1127-24 Questions and Answers
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Oracle 1z0-1127-24 Exam Syllabus Topics:
Topic
Details
Topic 1
- Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.
Topic 2
- Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.
Topic 3
- Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.
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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q39-Q44):
NEW QUESTION # 39
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?
- A. Summarization models
- B. Embedding models
- C. Generation models
- D. Translation models
Answer: D
NEW QUESTION # 40
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
- A. Retriever
- B. Encoder-decoder
- C. Generator
- D. Ranker
Answer: D
Explanation:
In Retrieval-Augmented Generation (RAG), the component responsible for evaluating and prioritizing the information retrieved by the retrieval system is the Ranker. After the Retriever fetches relevant documents or passages, the Ranker assesses these retrieved items based on their relevance to the query. It then prioritizes them, typically scoring and ordering the documents so that the most pertinent information is considered first in the generation process. This ensures that the generated response is based on the most relevant and useful content available.
Reference
Research papers on RAG (Retrieval-Augmented Generation)
Technical documentation on the architecture of RAG models
NEW QUESTION # 41
Given the following code: chain = prompt |11m
- A. LCEL is a legacy method for creating chains in LangChain
- B. LCEL is a declarative and preferred way to compose chains together.
- C. LCEL is a programming language used to write documentation for LangChain.
- D. Which statement is true about LangChain Expression language (ICED?
Answer: C
NEW QUESTION # 42
Which is NOT a typical use case for LangSmith Evaluators?
- A. Evaluating factual accuracy of outputs
- B. Aliening code readability
- C. Detecting bias or toxicity
- D. Measuring coherence of generated text
Answer: B
NEW QUESTION # 43
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?
- A. Specifies a string that tells the model to stop generating more content
- B. Assigns a penalty to tokens that have already appeared in the preceding text
- C. Controls the randomness of the model's output, affecting its creativity
- D. Determines the maximum number of tokens the model can generate per response
Answer: C
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
NEW QUESTION # 44
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