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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 (Q15-Q20):

NEW QUESTION # 15
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

A. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
B. By optimizing GPIJ memory utilization for each model's unique para
C. By allocating separate GPUS for each model instance
D. By loading the entire model into G PU memory for efficient processing

Answer: A

NEW QUESTION # 16
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

A. Serves as a designated point for user requests and model responses
B. Hosts the training data for fine-tuning custom model
C. Evaluates the performance metrics of the custom model
D. Updates the weights of the base model during the fine-tuning process

Answer: B

NEW QUESTION # 17
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

A. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
C. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
D. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.

Answer: C

Explanation:
In Retrieval-Augmented Generation (RAG), two key evaluation metrics are Groundedness and Answer Relevance, each addressing different aspects of AI-generated responses.
Groundedness (Factual Correctness):
Determines whether the AI-generated answer is factually based on the retrieved data.
Ensures that AI responses are verifiable from the reference documents.
Prevents hallucination, where the model generates plausible but incorrect answers.
Answer Relevance (Query Alignment):
Evaluates how well the response aligns with the user's original query.
A response may be factually correct but irrelevant to the actual question.
Ensures that AI responses remain contextually appropriate.
Key Differences:
Groundedness ensures that responses are based on correct data.
Answer Relevance checks whether the response actually answers the user's query.
🔹 Oracle Generative AI Reference:
Oracle AI ensures fact-checking and contextual accuracy in its retrieval-augmented models, reducing AI hallucination risks.

NEW QUESTION # 18
Which statement best describes the role of encoder and decoder models in natural language processing?

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

Answer: C

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 # 19
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

A. Step-Bock Prompting
B. Chain-of-Through
C. In context Learning
D. Least to most Prompting

Answer: B

NEW QUESTION # 20
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