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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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 3

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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
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

Explanation:
LangSmith Evaluators are not typically used for aligning code readability. Instead, they are used for tasks such as measuring the coherence of generated text, evaluating the factual accuracy of outputs, and detecting bias or toxicity. Evaluators help ensure the quality and reliability of the outputs generated by language models.
Reference
LangSmith documentation on evaluators
Research articles on evaluation metrics for language models

NEW QUESTION # 39
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

A. In context Learning
B. Least to most Prompting
C. Chain-of-Through
D. Step-Bock Prompting

Answer: C

Explanation:
Chain-of-Thought prompting involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response. This technique helps the model articulate its thought process and reasoning, leading to more transparent and understandable outputs. By breaking down the problem into smaller, logical steps, the model can provide more accurate and detailed responses.
Reference
Research articles on Chain-of-Thought prompting
Technical guides on enhancing model transparency and reasoning with intermediate steps

NEW QUESTION # 40
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

A. LangChain Application
B. Document Loaders
C. Vector Stores
D. LLMs

Answer: D

Explanation:
LangChain is an open-source framework that helps integrate Large Language Models (LLMs) into applications. In a chatbot system, the LLM (Large Language Model) component is responsible for generating linguistic output, as it processes user inputs and generates human-like responses.
Key components of LangChain include:
Document Loaders - Responsible for extracting and processing external data sources before passing them to the LLM.
Vector Stores - Used for storing and retrieving vector embeddings of documents for semantic search and similarity retrieval.
LLMs (Large Language Models) - This is the core component responsible for understanding prompts and generating text-based outputs in a chatbot.
LangChain Applications - The overall framework that connects all components but does not directly generate text.
🔹 Oracle Generative AI Reference:
Oracle supports LLM-driven chatbots and enterprise AI solutions, utilizing frameworks like LangChain to enhance AI capabilities.

NEW QUESTION # 41
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token is more likely to follow the current token.
B. The token is less likely to follow the current token.
C. The token is unrelated to the current token and will not be used.
D. The token will be the only one considered in the next generation step.

Answer: A

NEW QUESTION # 42
Which is NOT a built-in memory type in LangChain?

A. Conversation ImgeMemory
B. Conversation Buffer Memory
C. Conversation Summary Memory
D. Conversation Token Buffer Memory

Answer: A

NEW QUESTION # 43
......

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