Introduction to Large Language
Models Exam 2025 Questions and
Answers 100% Pass
large language models - ✔✔large, general-purpose language models can be pre-
trained (basic common language problems: text classification, question answering,
document summarization, text generation) and then fine-tuned (specific problems in
different fields) specific purposes
3 major features of large language models - ✔✔1. large:
-large training dataset
- a large number of parameters: memory and knowledge machine-learned from
training, define the skills of the model
2. general purpose: model sufficient ability to solve a common problem
-commonality of human languages
-resource restriction
3. pre-trained and fine-tuned
benefits of using large language models - ✔✔1. a single code can be used for
different tasks
COPYRIGHT © 2025 BY EMILLY CHARLOTTE, ALL RIGHTS RESERVED 1
, 2. the fine-tuning process requires minimal field data
-obtain decent performance even with minimal training
-can be used for few-shot or even zero-shot scenarios
3. the performance of LLM is continuously growing when you add more data and
parameters
few-shot - ✔✔in machine learning, it refers to training a model with minimal data
zero-shot - ✔✔in Machine learning, implies that a model can recognize things that
have not explicitly been taught in the training before
Pathways Language Model (PaLM) - ✔✔-has 540 billion parameters
-leverages the new Pathway system: efficiently train a single model across multiple
TPU v4 Pods
-orchestrate distributed computation for accelerators
*will handle many tasks at once, learn new tasks quickly, and reflect a better
understanding of the world
Transformer model - ✔✔1. encoding component
-input
2. decoding component
-output
LLM Development v. Traditional Development - ✔✔LLM development
-no ML expertise needed
COPYRIGHT © 2025 BY EMILLY CHARLOTTE, ALL RIGHTS RESERVED 2
Models Exam 2025 Questions and
Answers 100% Pass
large language models - ✔✔large, general-purpose language models can be pre-
trained (basic common language problems: text classification, question answering,
document summarization, text generation) and then fine-tuned (specific problems in
different fields) specific purposes
3 major features of large language models - ✔✔1. large:
-large training dataset
- a large number of parameters: memory and knowledge machine-learned from
training, define the skills of the model
2. general purpose: model sufficient ability to solve a common problem
-commonality of human languages
-resource restriction
3. pre-trained and fine-tuned
benefits of using large language models - ✔✔1. a single code can be used for
different tasks
COPYRIGHT © 2025 BY EMILLY CHARLOTTE, ALL RIGHTS RESERVED 1
, 2. the fine-tuning process requires minimal field data
-obtain decent performance even with minimal training
-can be used for few-shot or even zero-shot scenarios
3. the performance of LLM is continuously growing when you add more data and
parameters
few-shot - ✔✔in machine learning, it refers to training a model with minimal data
zero-shot - ✔✔in Machine learning, implies that a model can recognize things that
have not explicitly been taught in the training before
Pathways Language Model (PaLM) - ✔✔-has 540 billion parameters
-leverages the new Pathway system: efficiently train a single model across multiple
TPU v4 Pods
-orchestrate distributed computation for accelerators
*will handle many tasks at once, learn new tasks quickly, and reflect a better
understanding of the world
Transformer model - ✔✔1. encoding component
-input
2. decoding component
-output
LLM Development v. Traditional Development - ✔✔LLM development
-no ML expertise needed
COPYRIGHT © 2025 BY EMILLY CHARLOTTE, ALL RIGHTS RESERVED 2
