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The 1z0-1127-24 PDF is the collection of real, valid, and updated Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) practice questions. The Oracle 1z0-1127-24 PDF dumps file works with all smart devices. You can use the 1z0-1127-24 PDF questions on your tablet, smartphone, or laptop and start 1z0-1127-24 Exam Preparation anytime and anywhere. The 1z0-1127-24 dumps PDF provides you with everything that you must need in 1z0-1127-24 exam preparation and enable you to crack the final 1z0-1127-24 exam quickly.

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

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 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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

A. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
B. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
C. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
D. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

Answer: C

NEW QUESTION # 47
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

A. To generate text based only on the model's internal knowledge without external data
B. To retrieve text from an external source and present it without any modifications
C. To store text in an external database without using it for generation
D. To generate text using extra information obtained from an external data source

Answer: D

Explanation:
Retrieval-Augmented Generation (RAG) combines retrieval mechanisms with text generation, allowing models to pull external knowledge before generating responses.
How RAG Works:
The model retrieves relevant documents from an external database.
Uses this retrieved information to generate factually grounded responses.
Reduces hallucinations, improving accuracy and context relevance.
Why Other Options Are Incorrect:
(A) is incorrect because RAG modifies the retrieved text by integrating it into a generated response.
(B) is incorrect because RAG retrieves and uses data, not just stores it.
(C) is incorrect because RAG relies on external knowledge, whereas LLMs alone use internal pre-trained knowledge.
🔹 Oracle Generative AI Reference:
Oracle AI applies RAG techniques to improve enterprise AI applications, enhancing fact-based text generation.

NEW QUESTION # 48
Given the following code: chain = prompt |11m

A. LCEL is a declarative and preferred way to compose chains together.
B. LCEL is a programming language used to write documentation for LangChain.
C. Which statement is true about LangChain Expression language (ICED?
D. LCEL is a legacy method for creating chains in LangChain

Answer: B

NEW QUESTION # 49
How does a presence penalty function in language model generation?

A. It penalizes only tokens that have never appeared in the text before.
B. It penalizes a token each time it appears after the first occurrence.
C. It applies a penalty only if the token has appeared more than twice.
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 # 50
What does accuracy measure in the context of fine-tuning results for a generative model?

A. The proportion of incorrect predictions made by the model during an evaluation
B. The depth of the neural network layers used in the model
C. The number of predictions a model makes, regardless of whether they are correct or incorrect
D. How many predictions the model made correctly out of all the predictions in an evaluation

Answer: D

Explanation:
Accuracy in machine learning measures the proportion of correct predictions made by a model relative to the total predictions during an evaluation.
How Accuracy is Calculated:

A higher accuracy indicates better model performance.
Used primarily in classification tasks, but it can also assess LLM fine-tuning results.
Why Other Options Are Incorrect:
(A) is incorrect because the number of neural network layers does not define accuracy.
(B) is incorrect because accuracy considers correctness, not just total predictions.
(D) is incorrect because accuracy measures correct predictions, not just incorrect ones.
🔹 Oracle Generative AI Reference:
Oracle AI assesses model fine-tuning performance using accuracy, loss, and perplexity to improve LLM capabilities.

NEW QUESTION # 51
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