2025 EXCELLENT 100% FREE 1Z0-1127-24–100% FREE NEW TEST CRAM | EXAM 1Z0-1127-24 DUMPS

2025 Excellent 100% Free 1z0-1127-24–100% Free New Test Cram | Exam 1z0-1127-24 Dumps

2025 Excellent 100% Free 1z0-1127-24–100% Free New Test Cram | Exam 1z0-1127-24 Dumps

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q43-Q48):

NEW QUESTION # 43
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

  • A. Stored in Object Storage encrypted by default
  • B. Stored in Key Management service
  • C. Shared among multiple customers for efficiency
  • D. Stored in an unencrypted form in Object Storage

Answer: A

Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services


NEW QUESTION # 44
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 assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
  • D. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

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 # 45
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

  • A. A language model that operates on a token-by-token output basis
  • B. A Retrieval Augmented Generation (RAG) model that uses text as input and output
  • C. A Large Language Model based agent that focuses on generating textual responses
  • D. A diffusion model that specializes in producing complex outputs.

Answer: B


NEW QUESTION # 46
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

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

Answer: A

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 # 47
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

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

Answer: C

Explanation:
The utilization of T-Few transformer layers contributes to the efficiency of the fine-tuning process by restricting updates to only a specific group of transformer layers. This selective updating approach allows the model to adapt to new data without the need to retrain all layers, thus saving computational resources and time. By focusing on the most relevant parts of the model, T-Few fine-tuning achieves efficient and effective performance improvements.
Reference
Research papers on T-Few fine-tuning techniques
Technical guides on optimizing transformer models


NEW QUESTION # 48
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