1Z0-1127-24 VCE FORMAT, LATEST 1Z0-1127-24 EXAM TESTKING

1z0-1127-24 Vce Format, Latest 1z0-1127-24 Exam Testking

1z0-1127-24 Vce Format, Latest 1z0-1127-24 Exam Testking

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q22-Q27):

NEW QUESTION # 22
What is the purpose of Retrievers in LangChain?

  • A. To break down complex tasks into smaller steps
  • B. To train Large Language Models
  • C. To retrieve relevant information from knowledge bases
  • D. To combine multiple components into a single pipeline

Answer: C

Explanation:
Retrievers in LangChain serve the primary function of fetching relevant data from an external knowledge base or database to enhance the performance of Large Language Models (LLMs).
How Retrievers Work:
They retrieve documents, embeddings, or structured data that might be relevant to a given query.
Used in Retrieval-Augmented Generation (RAG) models to fetch real-time data.
Improves model responses by providing accurate and up-to-date knowledge.
Use Cases of Retrievers:
Chatbots: Enhancing responses with real-world or proprietary knowledge.
Question Answering Systems: Providing factual accuracy by referencing stored knowledge.
Enterprise AI Solutions: Connecting with databases, vector stores, and APIs to fetch data.
Why Other Options Are Incorrect:
(A) is incorrect because breaking tasks into smaller steps is handled by agents or chains.
(C) is incorrect because retrievers do not train LLMs; they enhance query responses.
(D) is incorrect because pipelines integrate components, whereas retrievers fetch external data.
???? Oracle Generative AI Reference:
Oracle AI integrates retrieval mechanisms in enterprise AI solutions, improving data-driven AI responses.


NEW QUESTION # 23
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

  • A. To retrieve text from an external source and present it without any modifications
  • B. To generate text based only on the model's internal knowledge without external data
  • C. To generate text using extra information obtained from an external data source
  • D. To store text in an external database without using it for generation

Answer: C

Explanation:
Retrieval-Augmented Generation (RAG) combines retrieval mechanisms with text generation, allowing models to pull external knowledge before generating responses.
How RAG Works:
The model retrieves relevant documents from an external database.
Uses this retrieved information to generate factually grounded responses.
Reduces hallucinations, improving accuracy and context relevance.
Why Other Options Are Incorrect:
(A) is incorrect because RAG modifies the retrieved text by integrating it into a generated response.
(B) is incorrect because RAG retrieves and uses data, not just stores it.
(C) is incorrect because RAG relies on external knowledge, whereas LLMs alone use internal pre-trained knowledge.
???? Oracle Generative AI Reference:
Oracle AI applies RAG techniques to improve enterprise AI applications, enhancing fact-based text generation.


NEW QUESTION # 24
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

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

Answer: B


NEW QUESTION # 25
What do prompt templates use for templating in language model applications?

  • A. Python's lambda functions
  • B. Python's class and object structures
  • C. Python's str.format syntax
  • D. Python's list comprehension syntax

Answer: C

Explanation:
Prompt templates are structured text-based input patterns that include placeholders for dynamic variable substitution. These templates help generate prompts for LLMs (Large Language Models) in a systematic and reusable way.
Prompt Template Example using str.format():
template = "What is the capital of {country}?"
formatted_prompt = template.format(country="France")
print(formatted_prompt) # Output: "What is the capital of France?"
Why str.format() is Used:
It allows dynamic insertion of variables.
It is flexible and widely supported in Python-based AI frameworks.
Used in LangChain, OpenAI API, and Oracle AI applications.
Why Other Options Are Incorrect:
(A) Lambda functions are used for anonymous function execution, not string templating.
(C) List comprehensions are used for iterating over lists, not text formatting.
(D) Class and object structures define OOP models, not LLM prompt templates.
???? Oracle Generative AI Reference:
Oracle AI frameworks use Python's str.format() and f-strings for LLM prompt engineering and AI-driven workflow automation.


NEW QUESTION # 26
How does a presence penalty function in language model generation?

  • A. It applies a penalty only if the token has appeared more than twice.
  • B. It penalizes all tokens equally, regardless of how often they have appeared.
  • C. It penalizes a token each time it appears after the first occurrence.
  • D. It penalizes only tokens that have never appeared in the text before.

Answer: C

Explanation:
A presence penalty is a mechanism used in language model generation to discourage repetition of words or phrases in generated text. This is crucial for improving diversity in AI-generated responses.
How It Works:
The presence penalty increases the loss associated with words that have already appeared in the output.
The model is less likely to generate the same word multiple times, leading to more diverse responses.
Unlike frequency penalties, which increase with repeated occurrences, presence penalties apply as soon as a word appears.
Key Use Cases:
Avoiding redundant phrases in AI-generated text.
Enhancing creative writing applications where repetitive wording is undesirable.
Making chatbot conversations more engaging and natural.
???? Oracle Generative AI Reference:
Oracle's generative AI models implement presence and frequency penalties as part of their fine-tuning and model inference processes to balance text coherence and diversity.


NEW QUESTION # 27
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