1Z0-1127-24 TEST QUESTIONS ANSWERS & 1Z0-1127-24 EXAM ACTUAL TESTS

1z0-1127-24 Test Questions Answers & 1z0-1127-24 Exam Actual Tests

1z0-1127-24 Test Questions Answers & 1z0-1127-24 Exam Actual Tests

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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 (Q19-Q24):

NEW QUESTION # 19
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

  • A. Encoder-decoder
  • B. Generator
  • C. Retriever
  • 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 # 20
Which is NOT a built-in memory type in LangChain?

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

Answer: B

Explanation:
In LangChain, "Conversation Image Memory" is not a built-in memory type. The built-in memory types in LangChain include:
Conversation Token Buffer Memory: This memory type stores a buffer of tokens from the conversation history.
Conversation Buffer Memory: This memory type retains a buffer of conversation history, typically in the form of text.
Conversation Summary Memory: This memory type summarizes the conversation history to keep track of key points and information.
These memory types help manage and utilize conversation history in various ways to enhance the performance of conversational models.
Reference
LangChain documentation on memory types
Technical guides on implementing memory in conversational AI systems


NEW QUESTION # 21
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

  • A. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
  • B. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
  • C. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
  • D. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

Answer: C

Explanation:
Dot Product and Cosine Distance are both metrics used to compare text embeddings, but they operate differently:
Dot Product: Measures the magnitude and direction of the vectors. It takes into account both the size (magnitude) and the angle (direction) between the vectors. This can result in higher similarity scores for longer vectors, even if they point in similar directions.
Cosine Distance: Focuses on the orientation of the vectors regardless of their magnitude. It measures the cosine of the angle between two vectors, which normalizes the vectors to unit length. This makes it a measure of the angle (or orientation) between the vectors, providing a similarity score that is independent of the vector lengths.
Reference
Research papers on text embedding comparison metrics
Technical documentation on vector similarity measures


NEW QUESTION # 22
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

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

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 # 23
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

  • A. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
  • B. It enables them to bypass the need for pretraining on large text corpora.
  • C. It transforms their architecture from a neural network to a traditional database system.
  • D. It limits their ability to understand and generate natural language.

Answer: A

Explanation:
The integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alters their responses by shifting the basis from pretrained internal knowledge to real-time data retrieval. This means that instead of relying solely on the knowledge encoded in the model during training, the LLM can retrieve and incorporate up-to-date and relevant information from an external database in real time. This enhances the model's ability to generate accurate and contextually relevant responses.
Reference
Research papers on Retrieval-Augmented Generation (RAG) techniques
Technical documentation on integrating vector databases with LLMs


NEW QUESTION # 24
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