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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	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	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 (Q55-Q60):

NEW QUESTION # 55
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Specifies a string that tells the model to stop generating more content
B. Assigns a penalty to tokens that have already appeared in the preceding text
C. Controls the randomness of the model's output, affecting its creativity
D. Determines the maximum number of tokens the model can generate per response

Answer: C

NEW QUESTION # 56
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
L Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

A. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
B. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most
C. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
D. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back

Answer: C

NEW QUESTION # 57
What is the purpose of Retrievers in LangChain?

A. To combine multiple components into a single pipeline
B. To train Large Language Models
C. To retrieve relevant information from knowledge bases
D. To break down complex tasks into smaller steps

Answer: C

Explanation:
Retrievers in LangChain serve the primary function of fetching relevant data from an external knowledge base or database to enhance the performance of Large Language Models (LLMs).
How Retrievers Work:
They retrieve documents, embeddings, or structured data that might be relevant to a given query.
Used in Retrieval-Augmented Generation (RAG) models to fetch real-time data.
Improves model responses by providing accurate and up-to-date knowledge.
Use Cases of Retrievers:
Chatbots: Enhancing responses with real-world or proprietary knowledge.
Question Answering Systems: Providing factual accuracy by referencing stored knowledge.
Enterprise AI Solutions: Connecting with databases, vector stores, and APIs to fetch data.
Why Other Options Are Incorrect:
(A) is incorrect because breaking tasks into smaller steps is handled by agents or chains.
(C) is incorrect because retrievers do not train LLMs; they enhance query responses.
(D) is incorrect because pipelines integrate components, whereas retrievers fetch external data.
🔹 Oracle Generative AI Reference:
Oracle AI integrates retrieval mechanisms in enterprise AI solutions, improving data-driven AI responses.

NEW QUESTION # 58
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 # 59
In which scenario is soft prompting appropriate compared to other training styles?

A. When there is a significant amount of labeled, task-specific data available
B. When the model needs to be adapted to perform well in a domain on which it was not originally trained
C. When the model requires continued pretraining on unlabeled data
D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training

Answer: D

Explanation:
Soft prompting is an efficient method for modifying LLM behavior without full retraining. Unlike fine-tuning, soft prompting adds learnable embeddings (soft prompts) to guide the model.
When Soft Prompting is Useful:
Enhances model behavior without full retraining.
Uses small trainable prompt tokens, avoiding large parameter updates.
Works well when labeled, task-specific data is unavailable.
Why Other Options Are Incorrect:
(A) is incorrect because continued pretraining involves modifying core model weights.
(C) is incorrect because adapting a model to a new domain is better suited to fine-tuning or full retraining.
(D) is incorrect because soft prompting is designed for low-data scenarios, while full fine-tuning requires labeled datasets.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient adaptation methods, including soft prompting and LoRA, to improve LLM flexibility.

NEW QUESTION # 60
......

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Oracle Updated 1z0-1127-24 Test Cram: Oracle Cloud Infrastructure 2024 Generative AI Professional - Real4Prep Help you Pass

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Oracle 1z0-1127-24 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q55-Q60):

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