DUMPS ORACLE 1Z0-1127-24 VCE, EXAM 1Z0-1127-24 SUCCESS

Dumps Oracle 1z0-1127-24 Vce, Exam 1z0-1127-24 Success

Dumps Oracle 1z0-1127-24 Vce, Exam 1z0-1127-24 Success

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
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
  • 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 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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q21-Q26):

NEW QUESTION # 21
What is the primary purpose of LangSmith Tracing?

  • A. To monitor the performance of language models
  • B. To debug issues in language model outputs
  • C. To analyze the reasoning process of language
  • D. To generate test cases for language models

Answer: C


NEW QUESTION # 22
In LangChain, which retriever search type is used to balance between relevancy and diversity?

  • A. similarity
  • B. top k
  • C. similarity_score_threshold
  • D. mmr

Answer: A


NEW QUESTION # 23
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

  • A. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
  • B. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.
  • C. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
  • D. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content

Answer: D


NEW QUESTION # 24
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

  • A. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
  • B. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
  • C. PEFT involves only a few or new parameters and uses labeled, task-specific data.
  • D. PEFT parameters and b typically used when no training data exists.

Answer: C


NEW QUESTION # 25
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

  • A. Top p assigns penalties to frequently occurring tokens.
  • B. Top p determines the maximum number of tokens per response.
  • C. Top p limits token selection based on the sum of their probabilities.
  • D. Top p selects tokens from the "Top k' tokens sorted by probability.

Answer: C


NEW QUESTION # 26
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