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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 (Q41-Q46):

NEW QUESTION # 41
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 already understands the topics necessary for text generation
D. When the LLM requires access to the latest data for generating outputs

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 # 42
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

A. By restricting updates to only a specific croup of transformer Layers
B. By excluding transformer layers from the fine-tuning process entirely
C. By allowing updates across all layers of the model
D. By incorporating additional layers to the base model

Answer: A

NEW QUESTION # 43
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Underfitting
B. Model Drift
C. Data Leakage
D. Overfilling

Answer: D

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

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

Answer: A

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
Given the following code: chain = prompt |11m

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

Answer: C

NEW QUESTION # 46
......

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