The Best Periodic Clock App | The Ultimate Chemistry Clock

Scott Kelly Scott Kelly

0 Course Enrolled • 0 Course Completed

Biography

최신업데이트된1z0-1127-24최고덤프데모시험덤프

저희가 알아본 데 의하면 많은it인사들이Oracle인증1z0-1127-24시험을 위하여 많은 시간을 투자하고 잇다고 합니다.하지만 특별한 학습 반 혹은 인터넷강이 같은건 선택하지 않으셨습니다.때문에 패스는 아주 어렵습니다.보통은 한번에 패스하시는 분들이 적습니다.우리 ITDumpsKR에서는 아주 믿을만한 학습가이드를 제공합니다.우리 ITDumpsKR에는Oracle인증1z0-1127-24테스트버전과Oracle인증1z0-1127-24문제와 답 두 가지 버전이 있습니다.우리는 여러분의Oracle인증1z0-1127-24시험을 위한 최고의 문제와 답 제공은 물론 여러분이 원하는 모든 it인증시험자료들을 선사할 수 있습니다.

Oracle 1z0-1127-24 시험요강:

주제
소개

주제 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.

주제 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.

주제 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.

>> 1z0-1127-24최고덤프데모 <<

최신버전 1z0-1127-24최고덤프데모 완벽한 시험 최신 기출문제

ITDumpsKR의 완벽한 Oracle인증 1z0-1127-24덤프는 고객님이Oracle인증 1z0-1127-24시험을 패스하는 지름길입니다. 시간과 돈을 적게 들이는 반면 효과는 십점만점에 십점입니다. ITDumpsKR의 Oracle인증 1z0-1127-24덤프를 선택하시면 고객님께서 원하시는 시험점수를 받아 자격증을 쉽게 취득할수 있습니다.

최신 Oracle Cloud Infrastructure 1z0-1127-24 무료샘플문제 (Q49-Q54):

질문 # 49
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

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

정답：C

질문 # 50
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
B. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
C. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
D. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.

정답：B

설명：
Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) are two techniques used for adapting pre-trained LLMs for specific tasks.
Fine-tuning:
Modifies all model parameters, requiring significant computing power.
Can lead to catastrophic forgetting, where the model loses prior general knowledge.
Example: Training GPT on medical texts to improve healthcare-specific knowledge.
Parameter-Efficient Fine-Tuning (PEFT):
Only a subset of model parameters is updated, making it computationally cheaper.
Uses techniques like LoRA (Low-Rank Adaptation) and Adapters to modify small parts of the model.
Avoids retraining the full model, maintaining general-purpose knowledge while adding task-specific expertise.
Why Other Options Are Incorrect:
(A) is incorrect because fine-tuning does not train from scratch, but modifies an existing model.
(B) is incorrect because both techniques involve model modifications.
(D) is incorrect because PEFT does not replace the model architecture.
🔹 Oracle Generative AI Reference:
Oracle AI supports both full fine-tuning and PEFT methods, optimizing AI models for cost efficiency and scalability.

질문 # 51
What does a cosine distance of 0 indicate about the relationship between two embeddings?

A. They are completely dissimilar
B. They are unrelated
C. They have the same magnitude
D. They are similar in direction

정답：D

설명：
Cosine distance (or cosine similarity) is a metric used to measure the angular similarity between two vectors in high-dimensional space.
Cosine Distance Calculation:
Cosine similarity formula:

The value ranges from -1 to 1:
1 → Vectors are identical.
0 → Vectors are orthogonal (unrelated).
-1 → Vectors are completely opposite.
Why a Cosine Distance of 0 Means Similar Direction:
A cosine similarity of 1 means vectors point in the same direction.
A cosine distance of 0 means maximum similarity (no angular difference).
Why Other Options Are Incorrect:
(A) is incorrect because a cosine distance of 0 implies similarity, not dissimilarity.
(B) is incorrect because unrelated vectors have a cosine similarity close to 0, not exactly 0.
(C) is incorrect because cosine similarity does not measure vector magnitude, only direction.
🔹 Oracle Generative AI Reference:
Oracle's vector search and embedding-based AI models rely on cosine similarity for semantic search, recommendation systems, and NLP tasks.

질문 # 52
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

정답：A

설명：
The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:
Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.
High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.
Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.
Reference
Research articles on the role of temperature in generative models
Technical guides for tuning generative AI models in OCI

질문 # 53
Which is NOT a built-in memory type in LangChain?

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

정답：A

질문 # 54
......

최근 Oracle인증 1z0-1127-24시험이 IT업계에서 제일 높은 인지도를 가지고 있습니다.바라만 보지 마시고Oracle인증 1z0-1127-24시험에 도전해보세요. ITDumpsKR 의 Oracle인증 1z0-1127-24덤프로 시험준비공부를 하시면 한방에 시험패스 가능합니다. Oracle인증 1z0-1127-24덤프로 자격증취득에 가까워지고 나아가서는 IT업계에서 인정을 받는 열쇠를 소유한것과 같다고 할수 있습니다.

1z0-1127-24 100％시험패스 자료: https://www.itdumpskr.com/1z0-1127-24-exam.html