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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?

A. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
B. Top k selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the Top token.
C. Top k and Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
D. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.

Answer: D

NEW QUESTION # 29
Which is NOT a typical use case for LangSmith Evaluators?

A. Detecting bias or toxicity
B. Measuring coherence of generated text
C. Aliening code readability
D. Evaluating factual accuracy of outputs

Answer: C

NEW QUESTION # 30
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. 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.
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. 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.
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: C

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 # 31
When should you use the T-Few fine-tuning method for training a model?

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

Answer: A

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 # 32
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A. It com rob the randomness of the model* output, affecting its creativity.
B. It specifies a string that tells the model to stop generating more content
C. It determines the maximum number of tokens the model can generate per response.
D. It assigns a penalty to frequently occurring tokens to reduce repetitive text.

Answer: B

NEW QUESTION # 33
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