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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q14-Q19):

NEW QUESTION # 14
Given the following code: chain = prompt |11m

A. LCEL is a programming language used to write documentation for LangChain.
B. LCEL is a declarative and preferred way to compose chains together.
C. LCEL is a legacy method for creating chains in LangChain
D. Which statement is true about LangChain Expression language (ICED?

Answer: A

NEW QUESTION # 15
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

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

Answer: D

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 # 16
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

A. When you want to optimize the model without any instructions
B. When the LLM does not perform well on a task and the data for prompt engineering is too large
C. When the LLM requires access to the latest data for generating outputs
D. When the LLM already understands the topics necessary for text generation

Answer: B

Explanation:
Fine-tuning is a technique used to customize an existing Large Language Model (LLM) by training it on domain-specific or task-specific data. Fine-tuning is necessary when:
The LLM's General Knowledge is Insufficient - If the model struggles with a specialized domain (e.g., medical, legal, finance), fine-tuning helps by exposing it to relevant domain-specific data.
Prompt Engineering is Ineffective Due to Large Data Requirements - When a task requires significant custom instructions or examples, fine-tuning is a better approach than prompt engineering, which may have length and complexity limitations.
Improved Accuracy is Required - Fine-tuning helps tailor the model to perform specific tasks more accurately, as it learns from additional training data.
Adapting to a Changing Knowledge Base - Fine-tuning can help update the model with recent trends or company-specific data that were not available during its initial training.
🔹 Oracle Generative AI Reference:
Oracle supports LLM fine-tuning within its AI ecosystem, allowing enterprises to optimize pre-trained AI models for industry-specific applications.

NEW QUESTION # 17
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
B. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
C. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
D. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.

Answer: B

Explanation:
Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) are two techniques used for adapting pre-trained LLMs for specific tasks.
Fine-tuning:
Modifies all model parameters, requiring significant computing power.
Can lead to catastrophic forgetting, where the model loses prior general knowledge.
Example: Training GPT on medical texts to improve healthcare-specific knowledge.
Parameter-Efficient Fine-Tuning (PEFT):
Only a subset of model parameters is updated, making it computationally cheaper.
Uses techniques like LoRA (Low-Rank Adaptation) and Adapters to modify small parts of the model.
Avoids retraining the full model, maintaining general-purpose knowledge while adding task-specific expertise.
Why Other Options Are Incorrect:
(A) is incorrect because fine-tuning does not train from scratch, but modifies an existing model.
(B) is incorrect because both techniques involve model modifications.
(D) is incorrect because PEFT does not replace the model architecture.
🔹 Oracle Generative AI Reference:
Oracle AI supports both full fine-tuning and PEFT methods, optimizing AI models for cost efficiency and scalability.

NEW QUESTION # 18
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

A. Summarization models
B. Embedding models
C. Translation models
D. Generation models

Answer: C

NEW QUESTION # 19
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Valid 1z0-1127-24 Mock Exam, 1z0-1127-24 Practice Braindumps

1z0-1127-24 Practice Braindumps | 1z0-1127-24 Pdf Version

Oracle 1z0-1127-24 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q14-Q19):

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