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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	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	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 (Q15-Q20):

NEW QUESTION # 15
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token is less likely to follow the current token.
B. The token is more likely to follow the current token.
C. The token will be the only one considered in the next generation step.
D. The token is unrelated to the current token and will not be used.

Answer: B

NEW QUESTION # 16
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

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

Answer: D

Explanation:
The utilization of T-Few transformer layers contributes to the efficiency of the fine-tuning process by restricting updates to only a specific group of transformer layers. This selective updating approach allows the model to adapt to new data without the need to retrain all layers, thus saving computational resources and time. By focusing on the most relevant parts of the model, T-Few fine-tuning achieves efficient and effective performance improvements.
Reference
Research papers on T-Few fine-tuning techniques
Technical guides on optimizing transformer models

NEW QUESTION # 17
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

A. GenerativeAI
B. Text Leader
C. Chain Deployment
D. RetrievalQA

Answer: A

NEW QUESTION # 18
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

A. 15 unit hours
B. 30 unit hours
C. 40 unit hours
D. 10 unit hours

Answer: D

NEW QUESTION # 19
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

A. The improvement in accuracy achieved by the model during training on the user-uploaded data set
B. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
C. The level of incorrectness in the models predictions, with lower values indicating better performance
D. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

Answer: C

Explanation:
In the evaluation of OCI Generative AI fine-tuned models, "Loss" measures the level of incorrectness in the model's predictions. It quantifies how far the model's predictions are from the actual values. Lower loss values indicate better performance, as they reflect a smaller discrepancy between the predicted and true values. The goal during training is to minimize the loss, thereby improving the model's accuracy and reliability.
Reference
Articles on loss functions in machine learning
OCI Generative AI service documentation on model evaluation metrics

NEW QUESTION # 20
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