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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 (Q54-Q59):

NEW QUESTION # 54
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Model Drift
B. Underfitting
C. Overfilling
D. Data Leakage

Answer: B

Explanation:
Using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service might result in underfitting. Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, leading to poor performance on both training and validation data. This is particularly problematic with small data sets because there may not be enough information for the model to learn the necessary patterns and relationships.
Reference
Articles on machine learning challenges with small data sets
Technical documentation on fine-tuning models in OCI

NEW QUESTION # 55
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. Top p selects tokens from the "Top k' tokens sorted by probability.
B. Top p limits token selection based on the sum of their probabilities.
C. Top p assigns penalties to frequently occurring tokens.
D. Top p determines the maximum number of tokens per response.

Answer: B

Explanation:
The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.
Reference
Research papers on nucleus sampling and token selection methods
OCI Generative AI model documentation

NEW QUESTION # 56
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

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

Answer: D

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 # 57
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

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

Answer: A

Explanation:
Dot Product and Cosine Distance are both metrics used to compare text embeddings, but they operate differently:
Dot Product: Measures the magnitude and direction of the vectors. It takes into account both the size (magnitude) and the angle (direction) between the vectors. This can result in higher similarity scores for longer vectors, even if they point in similar directions.
Cosine Distance: Focuses on the orientation of the vectors regardless of their magnitude. It measures the cosine of the angle between two vectors, which normalizes the vectors to unit length. This makes it a measure of the angle (or orientation) between the vectors, providing a similarity score that is independent of the vector lengths.
Reference
Research papers on text embedding comparison metrics
Technical documentation on vector similarity measures

NEW QUESTION # 58
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

A. By excluding transformer layers from the fine-tuning process entirely
B. By restricting updates to only a specific croup of transformer Layers
C. By allowing updates across all layers of the model
D. By incorporating additional layers to the base model

Answer: B

NEW QUESTION # 59
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