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Latest updated Oracle 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional Exam Study Solutions - Reliable TestValid Latest 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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q19-Q24):

NEW QUESTION # 19
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

A. PEFT involves only a few or new parameters and uses labeled, task-specific data.
B. PEFT parameters and b typically used when no training data exists.
C. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
D. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies

Answer: A

NEW QUESTION # 20
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship.
What is the nature of these relationships, and why are they crucial for language models?

A. Semantic relationships; crucial for understanding context and generating precise language
B. Hierarchical relationships; important for structuring database queries
C. Linear relationships; they simplify the modeling process
D. Temporal relationships; necessary for predicting future linguistic trends

Answer: A

Explanation:
Vector databases store word, sentence, or document embeddings that preserve semantic meaning. These embeddings capture relationships between concepts in a multi-dimensional space, improving LLM performance.
Why Semantic Relationships Are Crucial:
Enhance NLP Models: Ensure that words with similar meanings are closely placed in vector space.
Improve Search and Retrieval: Allow LLMs to retrieve conceptually relevant documents even if exact keywords do not match.
Enable Context-Aware Responses: Helps LLMs generate cohesive and meaningful text.
Why Other Options Are Incorrect:
(A) Hierarchical relationships help in database indexing, but they do not drive semantic understanding.
(B) Linear relationships are too simplistic for complex semantic modeling.
(D) Temporal relationships matter for time-based predictions, not semantic retrieval.
🔹 Oracle Generative AI Reference:
Oracle AI integrates vector databases to enhance LLM retrieval accuracy and semantic search capabilities.

NEW QUESTION # 21
Which statement best describes the role of encoder and decoder models in natural language processing?

A. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.
B. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
C. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
D. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.

Answer: A

Explanation:
In natural language processing (NLP), encoder and decoder models play distinct but complementary roles:
Encoder Models: These models convert a sequence of words into a vector representation. They capture the semantic meaning of the input text and encode it into a fixed-size vector.
Decoder Models: These models take the vector representation generated by the encoder and convert it back into a sequence of words. This process allows for generating new text based on the encoded information, such as in translation or text generation tasks.
Reference
Research articles on encoder-decoder architectures in NLP
Technical guides on the use of encoder and decoder models in machine translation and text generation

NEW QUESTION # 22
When should you use the T-Few fine-tuning method for training a model?

A. For data sets with hundreds of thousands to millions of samples
B. For models that require their own hosting dedicated Al duster
C. For complicated semantical undemanding improvement
D. For data sets with a few thousand samples or less

Answer: D

Explanation:
The T-Few fine-tuning method is particularly suitable for data sets with a few thousand samples or less. This method is designed to be efficient and effective with limited data, making it ideal for scenarios where collecting large amounts of training data is impractical. T-Few fine-tuning allows for meaningful adjustments to the model even with smaller data sets, providing good performance improvements without requiring extensive data.
Reference
Articles on fine-tuning techniques for small data sets
Technical documentation on T-Few fine-tuning in machine learning models

NEW QUESTION # 23
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. Text Leader
B. RetrievalQA
C. Chain Deployment
D. GenerativeAI

Answer: D

NEW QUESTION # 24
......

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