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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 2
  • Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
Topic 3
  • Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain
  • similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.
Topic 4
  • Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.

Reference: https://www.databricks.com/learn/certification/genai-engineer-associate

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Databricks Certified Generative AI Engineer Associate Sample Questions:

A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.
Which of the following components will NOT be useful in building such a chatbot?

  • A. Embedding model
  • B. Vector database
  • C. Response-generating LLM
  • D. Invite users to submit long, rather than concise, questions
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).

A Generative AI Engineer is developing an agent system using a popular agent-authoring library. The agent comprises multiple parallel and sequential chains. The engineer encounters challenges as the agent fails at one of the steps, making it difficult to debug the root cause. They need to find an appropriate approach to research this issue and discover the cause of failure. Which approach do they choose?

  • A. Run MLflow.evaluate to determine root cause of failed step.
  • B. Implement structured logging within the agent ' s code to capture detailed execution information.
  • C. Enable MLflow tracing to gain visibility into each agent ' s behavior and execution step.
  • D. Deconstruct the agent into independent steps to simplify debugging.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).

A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?

  • A. Llama2-70B
  • B. Dolly 1.5B
  • C. OpenAI GPT-4
  • D. BGE-large
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).

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)
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).

A Generative Al Engineer is building a production-ready LLM system which replies directly to customers.
The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.
Which approach will do this?

  • A. Host Llama Guard on Foundation Model API and use it to detect unsafe responses
  • B. Add some LLM calls to their chain to detect unsafe content before returning text
  • C. Add a regex expression on inputs and outputs to detect unsafe responses.
  • D. Ask users to report unsafe responses
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).

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