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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	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 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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It requires a large temperature setting to ensure diverse word selection.
B. It selects words bated on a flattened distribution over the vocabulary.
C. It chooses words randomly from the set of less probable candidates.
D. It picks the most likely word email at each step of decoding.

Answer: D

Explanation:
Greedy decoding in the context of language model word prediction refers to a decoding strategy where, at each step, the model selects the word with the highest probability (the most likely word). This approach is simple and straightforward but can sometimes lead to less diverse or creative outputs because it always opts for the most likely option without considering alternative sequences that might result in better overall sentences.
Reference
Research papers on decoding strategies in language models
Technical documentation on language model inference methods

NEW QUESTION # 31
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. Continuously throughout the entire chain execution process
B. Only after the output has been generated
C. After user input but before chain execution, and again after core logic but before output
D. Before user input and after chain execution

Answer: C

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 # 32
Which statement best describes the role of encoder and decoder models in natural language processing?

A. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
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 convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.
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: 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 # 33
How does a presence penalty function in language model generation?

A. It applies a penalty only if the token has appeared more than twice.
B. It penalizes a token each time it appears after the first occurrence.
C. It penalizes only tokens that have never appeared in the text before.
D. It penalizes all tokens equally, regardless of how often they have appeared.

Answer: B

Explanation:
A presence penalty is a mechanism used in language model generation to discourage repetition of words or phrases in generated text. This is crucial for improving diversity in AI-generated responses.
How It Works:
The presence penalty increases the loss associated with words that have already appeared in the output.
The model is less likely to generate the same word multiple times, leading to more diverse responses.
Unlike frequency penalties, which increase with repeated occurrences, presence penalties apply as soon as a word appears.
Key Use Cases:
Avoiding redundant phrases in AI-generated text.
Enhancing creative writing applications where repetitive wording is undesirable.
Making chatbot conversations more engaging and natural.
🔹 Oracle Generative AI Reference:
Oracle's generative AI models implement presence and frequency penalties as part of their fine-tuning and model inference processes to balance text coherence and diversity.

NEW QUESTION # 34
What do prompt templates use for templating in language model applications?

A. Python's list comprehension syntax
B. Python's lambda functions
C. Python's class and object structures
D. Python's str.format syntax

Answer: D

Explanation:
Prompt templates are structured text-based input patterns that include placeholders for dynamic variable substitution. These templates help generate prompts for LLMs (Large Language Models) in a systematic and reusable way.
Prompt Template Example using str.format():
template = "What is the capital of {country}?"
formatted_prompt = template.format(country="France")
print(formatted_prompt) # Output: "What is the capital of France?"
Why str.format() is Used:
It allows dynamic insertion of variables.
It is flexible and widely supported in Python-based AI frameworks.
Used in LangChain, OpenAI API, and Oracle AI applications.
Why Other Options Are Incorrect:
(A) Lambda functions are used for anonymous function execution, not string templating.
(C) List comprehensions are used for iterating over lists, not text formatting.
(D) Class and object structures define OOP models, not LLM prompt templates.
🔹 Oracle Generative AI Reference:
Oracle AI frameworks use Python's str.format() and f-strings for LLM prompt engineering and AI-driven workflow automation.

NEW QUESTION # 35
......

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Oracle 1z0-1127-24 Exam Syllabus Topics:

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q30-Q35):

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