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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 (Q55-Q60):

NEW QUESTION # 55
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?

A. Top k and Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
B. Top k selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the Top token.
C. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
D. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.

Answer: C

NEW QUESTION # 56
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

Answer: D

NEW QUESTION # 57
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

A. It increases the training time as compared to Vanilla fine-tuning.
B. It selectively updates only a fraction of the model's weights.
C. It updates all the weights of the model uniformly.
D. It does not update any weights but restructures the model architecture.

Answer: B

Explanation:
T-Few (Task-Specific Fine-tuning with Few-Shot Learning) is a fine-tuning approach designed to efficiently adapt Large Language Models (LLMs) to new tasks with minimal training data while using a small subset of model weights.
Characteristics of T-Few Fine-Tuning:
Selective Weight Updating: It does not update all model weights but focuses on a small fraction.
Few-Shot Learning Efficiency: Reduces the amount of labeled data required for fine-tuning.
Computational Cost Reduction: Requires significantly less compute than full model fine-tuning.
Better Transferability: Preserves the general knowledge of the base model while adapting to specific tasks.
Why Other Options Are Incorrect:
(B) is incorrect because T-Few updates weights rather than restructuring the model.
(C) is incorrect because not all weights are updated-only a small fraction.
(D) is incorrect because T-Few is optimized for efficiency and does not significantly increase training time.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient fine-tuning techniques like T-Few and LoRA (Low-Rank Adaptation) to enhance task-specific performance while reducing computational overhead.

NEW QUESTION # 58
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 convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.
C. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
D. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

Answer: B

NEW QUESTION # 59
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 convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.
C. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
D. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

Answer: B

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 # 60
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