1Z0-1127-24 QUESTION EXPLANATIONS & 2025 ORACLE REALISTIC ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL QUESTION EXPLANATIONS

1z0-1127-24 Question Explanations & 2025 Oracle Realistic Oracle Cloud Infrastructure 2024 Generative AI Professional Question Explanations

1z0-1127-24 Question Explanations & 2025 Oracle Realistic Oracle Cloud Infrastructure 2024 Generative AI Professional Question Explanations

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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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

  • A. Evaluates the performance metrics of the custom model
  • B. Hosts the training data for fine-tuning custom model
  • C. Serves as a designated point for user requests and model responses
  • D. Updates the weights of the base model during the fine-tuning process

Answer: B


NEW QUESTION # 41
Which statement is NOT true about StreamlitChatMessageHistory?

  • A. A given StreamlitChatMessageHistory will not be shared across user sessions.
  • B. A given StreamlitChatMessageHistory will NOT be persisted.
  • C. StreamlitChatMessageHistory can be used in any type of LLM application.
  • D. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.

Answer: C

Explanation:
StreamlitChatMessageHistory is a chat message storage tool in Streamlit, used to manage message history within LLM-powered applications.
Key Features of StreamlitChatMessageHistory:
Stores chat messages within Streamlit's session state.
Not persistent across sessions; resets when the session is closed.
Specific to Streamlit applications, not designed for all LLM applications.
Why Option (D) is Incorrect:
StreamlitChatMessageHistory is designed for Streamlit-based apps.
It is not suitable for all LLM applications, particularly those requiring persistent storage.
Why Other Options Are Correct:
(A) True: Each session has its own instance of StreamlitChatMessageHistory.
(B) True: It is not persisted across sessions.
(C) True: It stores messages in the Streamlit session state.
???? Oracle Generative AI Reference:
While Oracle AI supports various LLM applications, StreamlitChatMessageHistory is limited to Streamlit-based chat interfaces.


NEW QUESTION # 42
Which statement best describes the role of encoder and decoder models in natural language processing?

  • A. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
  • B. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
  • C. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
  • D. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.

Answer: D

Explanation:
In natural language processing (NLP), encoder and decoder models play distinct but complementary roles:
Encoder Models: These models convert a sequence of words into a vector representation. They capture the semantic meaning of the input text and encode it into a fixed-size vector.
Decoder Models: These models take the vector representation generated by the encoder and convert it back into a sequence of words. This process allows for generating new text based on the encoded information, such as in translation or text generation tasks.
Reference
Research articles on encoder-decoder architectures in NLP
Technical guides on the use of encoder and decoder models in machine translation and text generation


NEW QUESTION # 43
When should you use the T-Few fine-tuning method for training a model?

  • A. For complicated semantical undemanding improvement
  • B. For data sets with a few thousand samples or less
  • C. For models that require their own hosting dedicated Al duster
  • D. For data sets with hundreds of thousands to millions of samples

Answer: B

Explanation:
The T-Few fine-tuning method is particularly suitable for data sets with a few thousand samples or less. This method is designed to be efficient and effective with limited data, making it ideal for scenarios where collecting large amounts of training data is impractical. T-Few fine-tuning allows for meaningful adjustments to the model even with smaller data sets, providing good performance improvements without requiring extensive data.
Reference
Articles on fine-tuning techniques for small data sets
Technical documentation on T-Few fine-tuning in machine learning models


NEW QUESTION # 44
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

  • A. T- Few fine-tuning involves updating the weights of all layers in the model.
  • B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
  • C. T-Few fine-tuning requires manual annotation of input-output pain.
  • D. T-Few fine-tuning relies on unsupervised learning techniques for annotation.

Answer: B

Explanation:
T-Few fine-tuning is a technique that uses annotated data to adjust only a fraction of the model's weights. This method aims to efficiently fine-tune the model with a limited amount of data and computational resources. By updating only a small subset of the parameters, T-Few fine-tuning can achieve significant performance improvements without the need for extensive training data or computational power.
Reference
Research papers on parameter-efficient fine-tuning techniques
Technical guides on T-Few fine-tuning methodology


NEW QUESTION # 45
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