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1z0-1127-24 Exam Labs | New 1z0-1127-24 Exam Review

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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

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 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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q65-Q70):

NEW QUESTION # 65
Which is NOT a typical use case for LangSmith Evaluators?

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

Answer: A

NEW QUESTION # 66
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. RetrievalQA
D. Chain Deployment

Answer: A

Explanation:
The Oracle Accelerated Data Science (ADS) class that can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment is GenerativeAI. This class provides the necessary tools and functions to work with generative AI models, including deployment, fine-tuning, and inference capabilities. It integrates with OCI Data Science to streamline the process of deploying and managing LLM applications.
Reference
Oracle ADS documentation
Guides on deploying AI models using Oracle Data Science services

NEW QUESTION # 67
In the simplified workflow for managing and querying vector data, what is the role of indexing?

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

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 # 68
Which is NOT a built-in memory type in LangChain?

A. Conversation Buffer Memory
B. Conversation Token Buffer Memory
C. Conversation Summary Memory
D. Conversation ImgeMemory

Answer: D

Explanation:
In LangChain, "Conversation Image Memory" is not a built-in memory type. The built-in memory types in LangChain include:
Conversation Token Buffer Memory: This memory type stores a buffer of tokens from the conversation history.
Conversation Buffer Memory: This memory type retains a buffer of conversation history, typically in the form of text.
Conversation Summary Memory: This memory type summarizes the conversation history to keep track of key points and information.
These memory types help manage and utilize conversation history in various ways to enhance the performance of conversational models.
Reference
LangChain documentation on memory types
Technical guides on implementing memory in conversational AI systems

NEW QUESTION # 69
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

A. It does not update any weights but restructures the model architecture.
B. It selectively updates only a fraction of the model's weights.
C. It increases the training time as compared to Vanilla fine-tuning.
D. It updates all the weights of the model uniformly.

Answer: B

Explanation:
T-Few (Task-Specific Fine-tuning with Few-Shot Learning) is a fine-tuning approach designed to efficiently adapt Large Language Models (LLMs) to new tasks with minimal training data while using a small subset of model weights.
Characteristics of T-Few Fine-Tuning:
Selective Weight Updating: It does not update all model weights but focuses on a small fraction.
Few-Shot Learning Efficiency: Reduces the amount of labeled data required for fine-tuning.
Computational Cost Reduction: Requires significantly less compute than full model fine-tuning.
Better Transferability: Preserves the general knowledge of the base model while adapting to specific tasks.
Why Other Options Are Incorrect:
(B) is incorrect because T-Few updates weights rather than restructuring the model.
(C) is incorrect because not all weights are updated-only a small fraction.
(D) is incorrect because T-Few is optimized for efficiency and does not significantly increase training time.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient fine-tuning techniques like T-Few and LoRA (Low-Rank Adaptation) to enhance task-specific performance while reducing computational overhead.

NEW QUESTION # 70
......

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