1Z0-1127-24 LATEST TEST DUMPS - ORACLE LATEST 1Z0-1127-24 EXAM EXPERIENCE: ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL PASS CERTIFY

1z0-1127-24 Latest Test Dumps - Oracle Latest 1z0-1127-24 Exam Experience: Oracle Cloud Infrastructure 2024 Generative AI Professional Pass Certify

1z0-1127-24 Latest Test Dumps - Oracle Latest 1z0-1127-24 Exam Experience: Oracle Cloud Infrastructure 2024 Generative AI Professional Pass Certify

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

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

Answer: A

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 # 13
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

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

Answer: B

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 # 14
Which is NOT a built-in memory type in LangChain?

  • A. Conversation Buffer Memory
  • B. Conversation Token Buffer Memory
  • C. Conversation ImgeMemory
  • D. Conversation Summary Memory

Answer: C


NEW QUESTION # 15
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

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

Answer: B


NEW QUESTION # 16
What does a cosine distance of 0 indicate about the relationship between two embeddings?

  • A. They are completely dissimilar
  • B. They have the same magnitude
  • C. They are unrelated
  • D. They are similar in direction

Answer: D

Explanation:
Cosine distance (or cosine similarity) is a metric used to measure the angular similarity between two vectors in high-dimensional space.
Cosine Distance Calculation:
Cosine similarity formula:

The value ranges from -1 to 1:
1 → Vectors are identical.
0 → Vectors are orthogonal (unrelated).
-1 → Vectors are completely opposite.
Why a Cosine Distance of 0 Means Similar Direction:
A cosine similarity of 1 means vectors point in the same direction.
A cosine distance of 0 means maximum similarity (no angular difference).
Why Other Options Are Incorrect:
(A) is incorrect because a cosine distance of 0 implies similarity, not dissimilarity.
(B) is incorrect because unrelated vectors have a cosine similarity close to 0, not exactly 0.
(C) is incorrect because cosine similarity does not measure vector magnitude, only direction.
???? Oracle Generative AI Reference:
Oracle's vector search and embedding-based AI models rely on cosine similarity for semantic search, recommendation systems, and NLP tasks.


NEW QUESTION # 17
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