ACCURATE 100% FREE 1Z0-1127-24–100% FREE EXAM QUESTIONS ANSWERS | FRENQUENT 1Z0-1127-24 UPDATE

Accurate 100% Free 1z0-1127-24–100% Free Exam Questions Answers | Frenquent 1z0-1127-24 Update

Accurate 100% Free 1z0-1127-24–100% Free Exam Questions Answers | Frenquent 1z0-1127-24 Update

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q14-Q19):

NEW QUESTION # 14
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
L Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

  • A. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
  • B. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back
  • C. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most
  • D. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most

Answer: B

Explanation:
Chain-of-Thought: The first prompt calculates the total number of wheels and then uses that information to determine how many sets of wheels can be bought. This sequential reasoning process aligns with the Chain-of-Thought technique.
Least-to-most: The second prompt solves a complex problem by first identifying the needed formula and then solving a simpler version before tackling the full question. This incremental approach matches the Least-to-most technique.
Step-Back: The third prompt starts by defining greenhouse gases and then explores their impact on climate change, taking a step back to establish foundational knowledge before addressing the main question.
Reference
Research articles on prompting techniques for language models
Documentation on effective use of prompting strategies


NEW QUESTION # 15
Given the following code:
Prompt Template
(input_variable[''rhuman_input",'city''], template-template)
Which statement is true about Promt Template in relation to input_variables?

  • A. PromptTemplate can support only a single variable M a time.
  • B. PromptTemplate is unable to use any variables.
  • C. PromptTemplate requires a minimum of two variables to function property.
  • D. PromptTemplate supports Any number of variable*, including the possibility of having none.

Answer: D

Explanation:
The PromptTemplate in relation to input_variables is designed to be flexible and can support any number of variables, including the possibility of having none. This means that users can define a template with multiple variables or none at all, depending on their specific needs. The PromptTemplate facilitates dynamic prompt creation by inserting variable values into predefined template slots.
Reference
LangChain documentation on PromptTemplate
Examples and tutorials on using PromptTemplate in generative AI applications


NEW QUESTION # 16
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

  • A. T- Few fine-tuning involves updating the weights of all layers in the model.
  • B. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
  • C. T-Few fine-tuning requires manual annotation of input-output pain.
  • D. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

Answer: D

Explanation:
T-Few fine-tuning is a technique that uses annotated data to adjust only a fraction of the model's weights. This method aims to efficiently fine-tune the model with a limited amount of data and computational resources. By updating only a small subset of the parameters, T-Few fine-tuning can achieve significant performance improvements without the need for extensive training data or computational power.
Reference
Research papers on parameter-efficient fine-tuning techniques
Technical guides on T-Few fine-tuning methodology


NEW QUESTION # 17
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

  • A. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content
  • B. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
  • C. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
  • D. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

Answer: D

Explanation:
The Retrieval-Augmented Generation (RAG) technique enhances the response generation process of language models by incorporating relevant external documents. RAG Token and RAG Sequence are two variations of this technique.
RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally. This means that during the response generation process, the model continuously retrieves and incorporates information from external documents as it generates each token (or part) of the response. This allows for more dynamic and contextually relevant answers, as the model can adjust its retrieval based on the evolving context of the response.
In contrast, RAG Sequence typically retrieves documents once at the beginning of the response generation and uses those documents to generate the entire response. This approach is less dynamic compared to RAG Token, as it does not adjust the retrieval process during the generation of the response.
Reference
Research articles on Retrieval-Augmented Generation (RAG) techniques
Documentation on advanced language model inference methods


NEW QUESTION # 18
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

  • A. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
  • B. T- Few fine-tuning involves updating the weights of all layers in the model.
  • C. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
  • D. T-Few fine-tuning requires manual annotation of input-output pain.

Answer: A


NEW QUESTION # 19
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