Why architecture choices affect scalability in Prompt Engineering / GenAI - Quick Recap

Recall & Review

beginner

What does scalability mean in machine learning architecture?

Scalability means how well a machine learning system can handle more data or more users without losing performance.

beginner

How can choosing a simple model architecture help scalability?

Simple models use less computing power and memory, so they can handle bigger data or more requests faster.

intermediate

Why can complex architectures limit scalability?

Complex architectures need more resources and time to train and predict, which can slow down the system when data or users grow.

intermediate

What role does parallel processing play in scalable architectures?

Parallel processing splits tasks to run at the same time, helping the system handle more data quickly and improving scalability.

intermediate

How does modular architecture improve scalability?

Modular architecture breaks the system into parts that can be updated or scaled independently, making it easier to grow without big changes.

What happens if a model architecture is too complex for the available resources?

A The system becomes simpler to maintain.

B The system will always run faster.

C The model will use less memory.

D The system may slow down or fail to handle more data.

Complex models require more resources, so if resources are limited, the system can slow down or fail.

Which architecture choice helps a system handle more users at the same time?

A Single-threaded processing

B Parallel processing

C Using a very deep neural network

D Ignoring data size

Parallel processing allows multiple tasks to run simultaneously, improving the ability to handle many users.

Why is modular architecture good for scalability?

A It reduces the number of components.

B It combines all parts into one big block.

C It allows parts to be scaled or changed independently.

D It makes the system slower.

Modular design lets you update or scale parts without affecting the whole system, making growth easier.

What is a downside of choosing a very simple model architecture?

A It may not capture complex patterns well.

B It cannot be trained.

C It always uses too much memory.

D It always requires parallel processing.

Simple models may not learn complex data patterns, which can reduce accuracy.

How do architecture choices affect training time?

A Simpler architectures usually train faster.

B More complex architectures usually train faster.

C Architecture does not affect training time.

D Training time depends only on data size.

Simpler architectures need fewer calculations, so they usually train faster.

Explain how architecture choices impact the ability of a machine learning system to grow with more data or users.

Think about how simple vs complex models use resources and how system parts can be designed to grow.

Describe why parallel processing and modular architecture are important for scalable machine learning systems.

Consider how tasks and system parts can be managed to handle more work efficiently.