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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 (Q59-Q64):

NEW QUESTION # 59
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

A. Continuously throughout the entire chain execution process
B. Before user input and after chain execution
C. Only after the output has been generated
D. After user input but before chain execution, and again after core logic but before output

Answer: D

Explanation:
In a Conversational Retrieval Chain using LangChain, the chain typically interacts with memory at two key points: after the user input but before the chain execution, and again after the core logic but before the output is generated. This approach allows the system to update the memory with relevant context before executing the chain's main logic and then update the memory again with any new information or context gained during the execution before producing the final output.
Reference
LangChain documentation on Conversational Retrieval Chains
Technical guides on managing memory in conversational AI models

NEW QUESTION # 60
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

A. It limits their ability to understand and generate natural language.
B. It enables them to bypass the need for pretraining on large text corpora.
C. It transforms their architecture from a neural network to a traditional database system.
D. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.

Answer: D

NEW QUESTION # 61
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

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

Answer: B

Explanation:
Using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service might result in underfitting. Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, leading to poor performance on both training and validation data. This is particularly problematic with small data sets because there may not be enough information for the model to learn the necessary patterns and relationships.
Reference
Articles on machine learning challenges with small data sets
Technical documentation on fine-tuning models in OCI

NEW QUESTION # 62
How are documents usually evaluated in the simplest form of keyword-based search?

A. Based on the number of images and videos contained in the documents
B. Based on the presence and frequency of the user-provided keywords
C. According to the length of the documents
D. By the complexity of language used 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 # 63
In the simplified workflow for managing and querying vector data, what is the role of indexing?

A. To convert vectors into a nonindexed format for easier retrieval
B. To map vectors to a data structure for faster searching, enabling efficient retrieval
C. To categorize vectors based on their originating data type (text, images, audio)
D. To compress vector data for minimized storage usage

Answer: B

Explanation:
Vector indexing plays a crucial role in vector search and retrieval systems, particularly in AI-driven databases. The key functions of vector indexing include:
Efficient Search and Retrieval - Vector indexing structures (such as HNSW, FAISS, or Annoy) help organize vector embeddings to enable fast retrieval of similar vectors.
Mapping to Searchable Data Structures - The process involves creating indexes that efficiently store and map vectors, reducing computational overhead when searching for similar embeddings.
Handling High-Dimensional Data - Since vector embeddings (used in NLP, image recognition, etc.) are often high-dimensional, indexing helps compress and cluster similar vectors, improving retrieval speed.
Used in Vector Databases - Many AI applications, including Oracle's AI-driven database solutions, use indexing techniques for faster similarity searches.
???? Oracle Generative AI Reference:
Oracle integrates vector search within its AI and database services, allowing enterprises to efficiently manage and retrieve vectorized data.

NEW QUESTION # 64
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PASS GUARANTEED 2025 ORACLE 1Z0-1127-24: ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL–VALID PASS GUARANTEED

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