LATEST 1Z0-1127-24 DUMPS, 1Z0-1127-24 VALID TEST PATTERN

Latest 1z0-1127-24 Dumps, 1z0-1127-24 Valid Test Pattern

Latest 1z0-1127-24 Dumps, 1z0-1127-24 Valid Test Pattern

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 (Q58-Q63):

NEW QUESTION # 58
What is the primary purpose of LangSmith Tracing?

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

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


NEW QUESTION # 59
Which is NOT a built-in memory type in LangChain?

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

Answer: C

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 # 60
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

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

Answer: B

Explanation:
The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.
Reference
Research papers on nucleus sampling and token selection methods
OCI Generative AI model documentation


NEW QUESTION # 61
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship.
What is the nature of these relationships, and why are they crucial for language models?

  • A. Hierarchical relationships; important for structuring database queries
  • B. Temporal relationships; necessary for predicting future linguistic trends
  • C. Semantic relationships; crucial for understanding context and generating precise language
  • D. Linear relationships; they simplify the modeling process

Answer: C

Explanation:
Vector databases store word, sentence, or document embeddings that preserve semantic meaning. These embeddings capture relationships between concepts in a multi-dimensional space, improving LLM performance.
Why Semantic Relationships Are Crucial:
Enhance NLP Models: Ensure that words with similar meanings are closely placed in vector space.
Improve Search and Retrieval: Allow LLMs to retrieve conceptually relevant documents even if exact keywords do not match.
Enable Context-Aware Responses: Helps LLMs generate cohesive and meaningful text.
Why Other Options Are Incorrect:
(A) Hierarchical relationships help in database indexing, but they do not drive semantic understanding.
(B) Linear relationships are too simplistic for complex semantic modeling.
(D) Temporal relationships matter for time-based predictions, not semantic retrieval.
???? Oracle Generative AI Reference:
Oracle AI integrates vector databases to enhance LLM retrieval accuracy and semantic search capabilities.


NEW QUESTION # 62
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

  • A. Providing the exact k words in the prompt to guide the model's response
  • B. Limiting the model to only k possible outcomes or answers for a given task
  • C. The process of training the model on k different tasks simultaneously to improve its versatility
  • D. Explicitly providing k examples of the intended task in the prompt to guide the models output

Answer: D

Explanation:
K-shot prompting refers to providing the language model with k examples of the task at hand within the prompt. These examples help guide the model's understanding and output by demonstrating the desired format and approach. This technique is used to improve the model's performance on specific tasks by showing it how to handle similar situations.
Reference
Research papers on few-shot learning and prompting techniques
Technical documentation on using examples in prompts for large language models


NEW QUESTION # 63
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