Oracle Updated 1z0-1127-24 Exam Questions and Answers by mylah

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.

References

Research articles on prompting techniques for language models

Documentation on effective use of prompting strategies

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.

References

Research papers on few-shot learning and prompting techniques

Technical documentation on using examples in prompts for large language models

Parameter-Efficient Fine-Tuning (PEFT) is a technique used in large language model training that focuses on adjusting only a subset of the model's parameters rather than all of them. This approach involves using labeled, task-specific data to fine-tune new or a limited number of parameters. PEFT is designed to be more efficient than classic fine-tuning, which typically adjusts all the parameters of the model. By only updating a small fraction of the model's parameters, PEFT reduces the computational resources and time required for fine-tuning while still achieving significant performance improvements on specific tasks.

References

Research papers on Parameter-Efficient Fine-Tuning (PEFT)

Technical documentation on fine-tuning techniques for large language models

Temperature is a parameter in LLM decoding algorithms that controls randomness in text generation.

Effects of Temperature on Text Generation:

Higher Temperature (>1.0):

Flattens the probability distribution, making lower-probability words more likely.

Increases randomness, resulting in more creative and diverse outputs.

Lower Temperature (<1.0):

Sharpening effect, making high-probability words more dominant.

Produces more predictable and deterministic responses.

Why Other Options Are Incorrect:

(B) is incorrect because temperature does not remove the impact of likely words; it reduces or increases randomness.

(C) is incorrect because temperature affects probability, not speed.

(D) is incorrect because decreasing the temperature narrows the distribution, making text more deterministic.

???? Oracle Generative AI Reference:Oracle AI models allow dynamic temperature control to balance coherence and creativity in text generation.