NEW 1Z0-1127-24 DUMPS DISCOUNT | VALID 1Z0-1127-24 TORRENT: ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL

New 1z0-1127-24 Dumps Discount | Valid 1z0-1127-24 Torrent: Oracle Cloud Infrastructure 2024 Generative AI Professional

New 1z0-1127-24 Dumps Discount | Valid 1z0-1127-24 Torrent: Oracle Cloud Infrastructure 2024 Generative AI Professional

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q54-Q59):

NEW QUESTION # 54
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

Answer: D

Explanation:
The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:
Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.
High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.
Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.
Reference
Research articles on the role of temperature in generative models
Technical guides for tuning generative AI models in OCI


NEW QUESTION # 55
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

  • A. 10 unit hours
  • B. 30 unit hours
  • C. 15 unit hours
  • D. 40 unit hours

Answer: A

Explanation:
When you create a fine-tuning dedicated AI cluster and it is active for 10 hours, the number of unit hours required for fine-tuning is equal to the duration for which the cluster is active. Therefore, if the cluster is active for 10 hours, it requires 10 unit hours. This calculation assumes that the unit hour measurement directly corresponds to the active time of the cluster.
Reference
OCI documentation on unit hours and fine-tuning processes
Usage guidelines for dedicated AI clusters in OCI


NEW QUESTION # 56
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

  • A. GenerativeAI
  • B. Text Leader
  • C. RetrievalQA
  • D. Chain Deployment

Answer: A


NEW QUESTION # 57
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

Answer: D


NEW QUESTION # 58
Given the following code: chain = prompt |11m

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

Answer: C

Explanation:
LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.
Reference
LangChain documentation on LCEL
Examples and tutorials on using LangChain Expression Language


NEW QUESTION # 59
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