A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs.
Which action would be most effective in mitigating the problem of offensive text outputs?

• A.Increase the frequency of upstream data updates
• B.Inform the user of the expected RAG behavior
• C.Restrict access to the data sources to a limited number of users
• D.Curate upstream data properly that includes manual review before it is fed into the RAG system

Comment: *

Name: *

Email: *

Verification: *

A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.
What are the steps needed to build this RAG application and deploy it?

• A.Ingest documents from a source -> Index the documents and saves to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> Evaluate model -> LLM generates a response -> Deploy it using Model Serving
• B.Ingest documents from a source -> Index the documents and save to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
• C.Ingest documents from a source -> Index the documents and save to Vector Search -> Evaluate model -
  > Deploy it using Model Serving
• D.User submits queries against an LLM -> Ingest documents from a source -> Index the documents and save to Vector Search -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving

Comment: *

Name: *

Email: *

Verification: *

A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?

• A.context length 514; smallest model is 0.44GB and embedding dimension 768
• B.context length 2048: smallest model is 11GB and embedding dimension 2560
• C.context length 32768: smallest model is 14GB and embedding dimension 4096
• D.context length 512: smallest model is 0.13GB and embedding dimension 384

Comment: *

Name: *

Email: *

Verification: *

A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.
Which metric would help them increase user engagement and retention for their platform?

• A.Randomness
• B.Diversity of responses
• C.Lack of relevance
• D.Repetition of responses

Comment: *

Name: *

Email: *

Verification: *

A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?

• A.Number of customer inquiries processed per unit of time
• B.Energy usage per query
• C.Final perplexity scores for the training of the model
• D.HuggingFace Leaderboard values for the base LLM

Comment: *

Name: *

Email: *

Verification: *