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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.

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

NEW QUESTION # 22
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. 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.
B. 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.
C. 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.
D. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.

Answer: A

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 # 23
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Controls the randomness of the model's output, affecting its creativity
B. Determines the maximum number of tokens the model can generate per response
C. Assigns a penalty to tokens that have already appeared in the preceding text
D. Specifies a string that tells the model to stop generating more content

Answer: A

NEW QUESTION # 24
What is the primary purpose of LangSmith Tracing?

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

Answer: A

NEW QUESTION # 25
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in an unencrypted form in Object Storage
B. Stored in Object Storage encrypted by default
C. Shared among multiple customers for efficiency
D. Stored in Key Management service

Answer: B

Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services

NEW QUESTION # 26
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
L Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

A. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
B. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most
C. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back
D. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back

Answer: A

NEW QUESTION # 27
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1z0-1127-24 Practice Engine, Oracle Latest 1z0-1127-24 Test Sample: Oracle Cloud Infrastructure 2024 Generative AI Professional Pass Success

Oracle 1z0-1127-24 Exam Syllabus Topics:

Free PDF 2025 Oracle 1z0-1127-24 –High Pass-Rate Practice Engine

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

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