RAG and LLM Architecture: Graded Quiz 2

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Which of this is always true about query transformation in RAG?
*

10 points

Transforms user queries into JSON objects.

It's not required as LLMs are great at processing text directly

Transforms a user's input query into a compatible vector representation.

Requires manual query optimization for effective retrieval.

Which feature is NOT stated as an advantage of using RAG for enterprise-grade LLM applications?
*

10 points

Minimized hallucination

Regulatory adaptability

Automated summarization of all data sources

Real-Time, Human-Like Learning for Trusted and Relevant Information

If you are dealing with a real-time stream of data, is a separate real-time processing framework necessary for RAG?
*

10 points

Yes, always

No, Pathway’s LLM App for RAG supports real-time streaming

Depends on the data size

Only if you are using a legacy LLM

Which of these is a possible advantage of RAG over fine-tuning in Large Language Models?
*

10 points

Increased token limit

Data freshness

Easier implementation

Improved code generation for specific domain of tasks

In the context of LLMs, what does fine-tuning mean?
*

10 points

Adjusting the model's hyperparameters

Improving the model's energy efficiency

Training the model on a specialized dataset after pre-training

Reducing the size of the model

Which component of the LLM Architecture would most likely use a technology like Streamlit?
*

10 points

Dynamic Vector Indexing

Query Transformation

User Interface

Data Sources

What is the primary challenge in using Prompt Engineering as opposed to RAG for data retrieval in LLMs? *

10 points

Costlier to implement

It doesn’t work with unstructured data

Token limit constraints

It's more time-consuming

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In which of these can RAG contribute to robust data governance in LLMs?
*

10 points

By increasing the token limit

By introducing role-based access controls

By improving the model's humor detection

By simplifying user interfaces

Which of the following is NOT a mandatory step in the RAG?
*

10 points

Connecting the static or live data sources

Augmentation of user prompt

Using a vector database for managing vector indexes

Sourcing most-relevant information from data corpus

Which of this is true about the Token Limit Constraints in RAG? *

10 points

Token limitations are eliminated in RAG.

Token limitations are higher in RAG compared to LLMs.

Quick information retrieval in RAG makes token limitations less restrictive.

Token limitations don't exist in any LLM or RAG.

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