DUMPS ORACLE 1Z0-1127-24 DISCOUNT, EXAM 1Z0-1127-24 EXPERIENCE

Dumps Oracle 1z0-1127-24 Discount, Exam 1z0-1127-24 Experience

Dumps Oracle 1z0-1127-24 Discount, Exam 1z0-1127-24 Experience

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You will have prior experience in answering questions with adjustable time. With these features, you will improve your Oracle Cloud Infrastructure 2024 Generative AI Professional 1z0-1127-24 exam confidence and time management skills. Many candidates prefer to prepare for the Oracle Cloud Infrastructure 2024 Generative AI Professional 1z0-1127-24 Exam Dumps using different formats. The Oracle Cloud Infrastructure 2024 Generative AI Professional 1z0-1127-24 exam questions were designed in different formats so that every candidate could select what suited them best.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q39-Q44):

NEW QUESTION # 39
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

  • A. Explicitly providing k examples of the intended task in the prompt to guide the models output
  • B. Limiting the model to only k possible outcomes or answers for a given task
  • C. Providing the exact k words in the prompt to guide the model's response
  • D. The process of training the model on k different tasks simultaneously to improve its versatility

Answer: A

Explanation:
K-shot prompting refers to providing the language model with k examples of the task at hand within the prompt. These examples help guide the model's understanding and output by demonstrating the desired format and approach. This technique is used to improve the model's performance on specific tasks by showing it how to handle similar situations.
Reference
Research papers on few-shot learning and prompting techniques
Technical documentation on using examples in prompts for large language models


NEW QUESTION # 40
Given the following code: chain = prompt |11m

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

Answer: A

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 # 41
What is the primary purpose of LangSmith Tracing?

  • A. To monitor the performance of language models
  • B. To debug issues in language model outputs
  • C. To analyze the reasoning process of language
  • D. To generate test cases for language models

Answer: B

Explanation:
The primary purpose of LangSmith Tracing is to debug issues in language model outputs. LangSmith Tracing allows developers to trace and analyze the sequence of operations and decisions made by the model during the generation process. This helps identify and resolve problems, ensuring the model's outputs are accurate and reliable.
Reference
LangSmith documentation on tracing and debugging
Tutorials on using tracing tools for language model development


NEW QUESTION # 42
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

  • A. Emphasis on syntactic clustering of word embedding's
  • B. Capacity to translate text in over u languages
  • C. Support for tokenizing longer sentences
  • D. Improved retrievals for Retrieval Augmented Generation (RAG) systems

Answer: D

Explanation:
The Cohere Embed v3 model distinguishes itself from its predecessor in the OCI Generative AI service primarily through improved retrievals for Retrieval Augmented Generation (RAG) systems. This enhancement means that the new version of the model is better at retrieving relevant documents or passages that can be used to augment the generation of responses. The improvements likely include better embedding quality, which allows the model to find more relevant and contextually appropriate information during the retrieval phase.
Reference
Cohere model documentation and release notes
Technical discussions on improvements in RAG systems


NEW QUESTION # 43
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. Overfilling
  • C. Underfitting
  • D. Data Leakage

Answer: B


NEW QUESTION # 44
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