The Databricks-Generative-AI-Engineer-Associate exam has a reputation for tripping up even experienced professionals, and the Databricks Certified Generative AI Engineer Associate blueprint keeps getting broader. Rather than guessing what matters, work through the 75 practice questions 2Pass4sure has built around the current exam and walk in knowing the format cold.

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Answers to the Most Common Questions About Databricks Certified Generative AI Engineer Associate

Databricks Certified Generative AI Engineer Associate Sample Questions:

Question 1

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

Question 2

A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.
Which approach will NOT improve the LLM's response to achieve the desired response?

A. Include few-shot examples in the prompt to the LLM
B. Fine-tune the LLM on a dataset of desired tone and style
C. Use a neutralizer to normalize the tone and style of the underlying documents
D. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style

Question 3

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

Question 4

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. (Incorrect structure)
C. 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"}])
D. (Incorrect structure)

Question 5

A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

A. vsc.get_index()
B. vsc.similarity_search()
C. vsc.create_direct_access_index()
D. vsc.create_delta_sync_index()

Solutions: