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Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Generative AI Engineer Associate
Exam Number:	Generative AI Engineer Associate
Available Languages:	Japanese, Korean, Portuguese (Brazil), English
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Exam Format:	Multiple Select, Multiple Choice
Passing Score:	Scaled Score (approx. 70%)
Related Certifications:	Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate
Exam Duration:	90 minutes
Real Exam Qty:	45
Sample Questions:	Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions
Exam Way:	Online (Proctored) or Test Center
Pre Condition:	None (Recommended: 6+ months hands-on experience)
Official Syllabus URL:	https://www.databricks.com/learn/certification/genai-engineer-associate

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

Topic	Details
Topic 1	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.
Topic 2	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 3	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.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q38-Q43):

NEW QUESTION # 38
A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?

A. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.
B. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
C. Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow's built in evaluation metrics to perform the evaluation on the retrieval and generation components.
D. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.

Answer: C

Explanation:
* Problem Context: After receiving positive feedback for the RAG application prototype, the next step is to formally evaluate the system to pinpoint areas for improvement.
* Explanation of Options:
* Option A: While cosine similarity scores are useful, they primarily measure similarity rather than the overall performance of an RAG system.
* Option B: This option provides a systematic approach to evaluation by testing both retrieval and generation components separately. This allows for targeted improvements and a clear understanding of each component's performance, using MLflow's metrics for a structured and standardized assessment.
* Option C: Benchmarking multiple LLMs does not focus on evaluating the existing system's components but rather on comparing different models.
* Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.
OptionBis the most comprehensive and structured approach, facilitating precise evaluations and improvements on specific components of the RAG system.

NEW QUESTION # 39
When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

A. Reach out to the data curators directly before you have started using the trained model to let them know.
B. Use any available data you personally created which is completely original and you can decide what license to use.
C. Only use data explicitly labeled with an open license and ensure the license terms are followed.
D. Reach out to the data curators directly after you have started using the trained model to let them know.

Answer: D

Explanation:
* Problem Context: When using data to train a model, it's essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.
* Explanation of Options:
* Option A: Reaching out to data curatorsbeforeusing the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.
* Option B: Usingoriginal datathat you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.
* Option C: Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.
* Option D: Reaching out to the data curatorsafteryou have already started using the trained model isnot appropriate. If you've already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications. It's essential to clarify the licensing termsbeforeusing the data, not after.
Thus,Option Dis not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.

NEW QUESTION # 40
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)

Answer: B

NEW QUESTION # 41
A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible.
Which combination of chaining components and configuration meets these requirements?

A. The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
B. For the question-answering application, prompt engineering and an LLM are required to generate answers.
C. For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.
D. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.

Answer: D

Explanation:
Problem Context: The task is to build an LLM-based question-answering application that integrates new documents frequently with minimal costs and development efforts.
Explanation of Options:
* Option A: Utilizes a prompt and a retriever, with the retriever output being fed into the LLM. This setup is efficient because it dynamically updates the data pool via the retriever, allowing the LLM to provide up-to-date answers based on the latest documents without needing tofrequently retrain the model. This method offers a balance of cost-effectiveness and functionality.
* Option B: Requires frequent retraining of the LLM, which is costly and labor-intensive.
* Option C: Only involves prompt engineering and an LLM, which may not adequately handle the requirement for incorporating new documents unless it's part of an ongoing retraining or updating mechanism, which would increase costs.
* Option D: Involves an agent and a fine-tuned LLM, which could be overkill and lead to higher development and operational costs.
Option Ais the most suitable as it provides a cost-effective, minimal development approach while ensuring the application remains up-to-date with new information.

NEW QUESTION # 42
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?

A. beautifulsoup
B. numpy
C. flask
D. unstructured

Answer: A

Explanation:
* Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.
* Explanation of Options:
* Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from PDFs.
* Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.
* Option C: unstructured: This Python package is specifically designed to work with unstructured data, including extracting text from PDFs. It provides functionalities to handle various types of content in documents with minimal coding, making it ideal for the task.
* Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.
Given the requirement,Option C(unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.

NEW QUESTION # 43
......

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