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Vce 1z0-1127-24 Format - 1z0-1127-24 Reliable Dumps Ebook

For Oracle professionals, passing the Oracle Cloud Infrastructure 2024 Generative AI Professional exams such as the 1z0-1127-24 Exam is essential to achieve their dream professional life. However, passing the Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) Exam is not an easy task, especially for those with busy schedules who need time to prepare well for the 1z0-1127-24 Exam. To ensure success on the 1z0-1127-24 Exam, you need Oracle 1z0-1127-24 Exam Questions that contain all the relevant information about the exam.

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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which is NOT a built-in memory type in LangChain?

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

Answer: C

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 # 37
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

A. Increasing the temperature removes the impact of the most likely word.
B. Increasing the temperature flattens the distribution, allowing for more varied word choices.
C. Temperature has no effect on probability distribution; it only changes the speed of decoding.
D. Decreasing the temperature broadens the distribution, making less likely words more probable.

Answer: B

Explanation:
Temperature is a parameter in LLM decoding algorithms that controls randomness in text generation.
Effects of Temperature on Text Generation:
Higher Temperature (>1.0):
Flattens the probability distribution, making lower-probability words more likely.
Increases randomness, resulting in more creative and diverse outputs.
Lower Temperature (<1.0):
Sharpening effect, making high-probability words more dominant.
Produces more predictable and deterministic responses.
Why Other Options Are Incorrect:
(B) is incorrect because temperature does not remove the impact of likely words; it reduces or increases randomness.
(C) is incorrect because temperature affects probability, not speed.
(D) is incorrect because decreasing the temperature narrows the distribution, making text more deterministic.
🔹 Oracle Generative AI Reference:
Oracle AI models allow dynamic temperature control to balance coherence and creativity in text generation.

NEW QUESTION # 38
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. RetrievalQA
B. Chain Deployment
C. Text Leader
D. GenerativeAI

Answer: D

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 # 39
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

Answer: C

NEW QUESTION # 40
Given the following code: chain = prompt |11m

A. Which statement is true about LangChain Expression language (ICED?
B. LCEL is a programming language used to write documentation for LangChain.
C. LCEL is a declarative and preferred way to compose chains together.
D. LCEL is a legacy method for creating chains in LangChain

Answer: C

Explanation:
LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.
Reference
LangChain documentation on LCEL
Examples and tutorials on using LangChain Expression Language

NEW QUESTION # 41
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