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Pass Guaranteed Quiz 1z0-1127-24 - Oracle Cloud Infrastructure 2024 Generative AI Professional–High Pass-Rate Exam Demo

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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 (Q63-Q68):

NEW QUESTION # 63
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

A. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
B. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
C. The level of incorrectness in the models predictions, with lower values indicating better performance
D. The improvement in accuracy achieved by the model during training on the user-uploaded data set

Answer: C

NEW QUESTION # 64
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in Object Storage encrypted by default
B. Shared among multiple customers for efficiency
C. Stored in an unencrypted form in Object Storage
D. Stored in Key Management service

Answer: A

Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services

NEW QUESTION # 65
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

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

Answer: A

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 # 66
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. Before user input and after chain execution
B. Only after the output has been generated
C. Continuously throughout the entire chain execution process
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 # 67
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Retriever
B. Generator
C. Encoder-decoder
D. Ranker

Answer: D

NEW QUESTION # 68
......

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David Ross David Ross

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