Pass with professional Databricks-Generative-AI-Engineer-Associate actual quiz materials

Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?

• A. The latency of the response and the length of text generated
• B. The similarity to the previous language
• C. The accuracy and relevance of the responses
• D. The ability to generate responses in code

A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website. Given the large investment the company put into fine-tuning this model, and the proprietary nature of the tuning data, they are concerned about model inversion attacks. Which of the following Databricks AI Security Framework (DASF) risk mitigation strategies are most relevant to this use case?

• A. Implement AI guardrails to allow users to configure and enforce compliance
• B. Use secure model features with Databricks Feature Store
• C. Leverage Databricks access control lists (ACLs) to configure permissions for accessing models
• D. Apply attribute-based access controls (ABAC) to limit unauthorized access

A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error:
Python
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
prompt_template = " Tell me a {adjective} joke "
prompt = PromptTemplate(input_variables=[ " adjective " ], template=prompt_template)
# ... (Error-prone section)
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

• A. (Incorrect structure)
• B. prompt_template = " Tell me a {adjective} joke "
  prompt = PromptTemplate(input_variables=[ " adjective " ], template=prompt_template) llm = OpenAI() llm_chain = LLMChain(prompt=prompt, llm=llm) llm_chain.generate([{ " adjective " : " funny " }])
• C. (Incorrect structure)
• D. (Incorrect structure)

A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

• A. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container
• B. Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint
• C. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn
• D. Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint

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