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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 (Q53-Q58):

NEW QUESTION # 53
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

A. Hosts the training data for fine-tuning custom model
B. Evaluates the performance metrics of the custom model
C. Updates the weights of the base model during the fine-tuning process
D. Serves as a designated point for user requests and model responses

Answer: A

NEW QUESTION # 54
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

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

Answer: A

Explanation:
In the OCI Generative AI service, the categories of pre-trained foundational models available include Summarization models, Generation models, and Embedding models. However, Translation models are not listed as a category of pre-trained foundational models available in OCI Generative AI service. The service focuses on providing models that support text generation, summarization, and embedding tasks.
Reference
OCI Generative AI service documentation
Listings and descriptions of pre-trained foundational models in OCI

NEW QUESTION # 55
How are documents usually evaluated in the simplest form of keyword-based search?

A. According to the length of the documents
B. Based on the presence and frequency of the user-provided keywords
C. By the complexity of language used in the documents
D. Based on the number of images and videos contained in the documents

Answer: B

Explanation:
In the simplest form of keyword-based search, documents are evaluated based on keyword matching and term frequency. This approach does not account for context, semantics, or the meaning behind the words, but rather focuses on:
Presence of Keywords - If a document contains the search term, it is considered relevant.
Term Frequency (TF) - The more a keyword appears in a document, the higher the ranking in basic search algorithms.
Inverse Document Frequency (IDF) - Words that are common across many documents (e.g., "the," "is") are given less weight, while rare words are prioritized.
Boolean Matching - Some basic search engines support logical operators like AND, OR, and NOT to refine keyword searches.
Exact Match vs. Partial Match - Some systems prioritize exact keyword matches, while others allow partial or fuzzy matches.
🔹 Oracle Generative AI Reference:
Oracle has implemented semantic search and advanced AI-driven document search techniques in its cloud solutions, but traditional keyword-based search still forms the foundation of many enterprise search mechanisms.

NEW QUESTION # 56
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

A. Evaluates the performance metrics of the custom model
B. Hosts the training data for fine-tuning custom model
C. Updates the weights of the base model during the fine-tuning process
D. Serves as a designated point for user requests and model responses

Answer: D

Explanation:
In the inference workflow of the OCI Generative AI service, a "model endpoint" is a critical component. It serves as a designated point for handling user requests and providing model responses. When users or applications send requests to the model endpoint, the endpoint processes these requests by passing them to the deployed model. The model then generates responses based on the input data, and these responses are returned to the user through the same endpoint. This setup facilitates efficient and scalable interaction with the AI model, ensuring that inference can be performed seamlessly and reliably.
Reference
Oracle Cloud Infrastructure (OCI) Generative AI service documentation
General principles of model deployment and inference in cloud services

NEW QUESTION # 57
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 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 relies on unsupervised learning techniques for annotation.

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 # 58
......

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