Oracle Exam 1Z0-1122-25 Topic 2 Question 3 Discussion

Actual exam question for Oracle's 1Z0-1122-25 exam

Question #: 3
Topic #: 2

[All 1Z0-1122-25 Questions]

How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

AThey prioritize larger model sizes to achieve better performance.

BThey focus on increasing the number of tokens while keeping the model size constant.

CThey disregard model size and prioritize high-quality data only.

DThey ensure that the model size, training time, and data size are balanced for optimal results.

Show Suggested Answer

Suggested Answer: D

Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.

by Nguyet at Mar 22, 2025, 06:43 PM

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Brandon

12 days ago

Haha, I can just imagine the engineers behind LLMs just throwing more and more data at the models until they break. 'Bigger is better' is not always the way to go!

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Melita

14 days ago

I feel like they should prioritize high-quality data over model size.

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Nickie

15 days ago

I disagree, I believe they focus on balancing model size, training time, and data size for optimal results.

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Corrina

15 days ago

I'm curious to see what the 'right' answer is, but I have to say, option A sounds like a recipe for an inefficient and bloated model. Who needs a giant model if the data quality is poor?

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Lindsey

16 days ago

I'm going with B on this one. Increasing the number of tokens while keeping the model size constant seems like a good way to improve performance without getting bogged down by huge model sizes.

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Edda

17 days ago

I think Large Language Models prioritize larger model sizes for better performance.

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Marg

1 months ago

Definitely D. LLMs have to carefully manage all of those factors to deliver the most accurate and efficient results. It's not as simple as just going for the biggest model possible.

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Silva

1 months ago

I think the answer is D. From what I've read, the best LLMs achieve a balance between model size, data quality, and data size to optimize performance. Focusing solely on larger models or more data doesn't always lead to the best results.

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Derick

5 days ago

Yeah, prioritizing all aspects like model size, data quality, and training time is crucial for performance.

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Deeanna

10 days ago

I think D makes sense, balancing all those factors is key.

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Clarinda

26 days ago

I agree, it's important to find the right balance for optimal results.

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