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

Topic	Details
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	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
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	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 5	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 (Q31-Q36):

NEW QUESTION # 31
What is the most suitable library for building a multi-step LLM-based workflow?

A. PySpark
B. Pandas
C. TensorFlow
D. LangChain

Answer: D

Explanation:
* Problem Context: The Generative AI Engineer needs a tool to build amulti-step LLM-based workflow. This type of workflow often involves chaining multiple steps together, such as query generation, retrieval of information, response generation, and post-processing, with LLMs integrated at several points.
* Explanation of Options:
* Option A: Pandas: Pandas is a powerful data manipulation library for structured data analysis, but it is not designed for managing or orchestrating multi-step workflows, especially those involving LLMs.
* Option B: TensorFlow: TensorFlow is primarily used for training and deploying machine learning models, especially deep learning models. It is not designed for orchestrating multi-step tasks in LLM-based workflows.
* Option C: PySpark: PySpark is a distributed computing framework used for large-scale data processing. While useful for handling big data, it is not specialized for chaining LLM-based operations.
* Option D: LangChain: LangChain is a purpose-built framework designed specifically for orchestrating multi-step workflowswith large language models (LLMs). It enables developers to easily chain different tasks, such as retrieving documents, summarizing information, and generating responses, all in a structured flow. This makes it the best tool for building complex LLM-based workflows.
Thus,LangChainis the most suitable library for creating multi-step LLM-based workflows.

NEW QUESTION # 32
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 cosine similarity score to comprehensively evaluate the quality of the final generated answers.
B. 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.
C. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.
D. Benchmark multiple LLMs with the same data and pick the best LLM for the job.

Answer: B

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 # 33
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. flask
B. numpy
C. unstructured
D. beautifulsoup

Answer: D

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 # 34
A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?

A. DistilBERT
B. DBRX
C. MPT-30B
D. Llama2-70B

Answer: D

Explanation:
* Problem Context: The engineer needs an open-source LLM with a large context window to develop an application.
* Explanation of Options:
* Option A: DistilBERT: While an efficient and smaller version of BERT, DistilBERT does not provide a particularly large context window.
* Option B: MPT-30B: This model, while large, is not specified as being particularly notable for its context window capabilities.
* Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a large context window. It is also available as an open-source model, making it ideal for applications requiring extensive contextual understanding.
* Option D: DBRX: This is not a recognized standard model in the context of large language models with extensive context windows.
Thus,Option C(Llama2-70B) is the best fit as it meets the criteria of having a large context window and being available for open-source use, suitable for developing robust language understanding applications.

NEW QUESTION # 35
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. Repetition of responses
D. Lack of relevance

Answer: B

Explanation:
In the context of designing a chatbot to engage users on a gaming platform,diversity of responses(option B) is a key metric to increase user engagement and retention. Here's why:
* Diverse and Engaging Interactions:A chatbot that provides varied and interesting responses will keep users engaged, especially in an interactive environment like a gaming platform. Gamers typically enjoy dynamic and evolving conversations, anddiversity of responseshelps prevent monotony, encouraging users to interact more frequently with the bot.
* Increasing Retention:By offering different types of responses to similar queries, the chatbot can create a sense of novelty and excitement, which enhances the user's experience and makes them more likely to return to the platform.
* Why Other Options Are Less Effective:
* A (Randomness): Random responses can be confusing or irrelevant, leading to frustration and reducing engagement.
* C (Lack of Relevance): If responses are not relevant to the user's queries, this will degrade the user experience and lead to disengagement.
* D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feel uninteresting and reducing the likelihood of continued interaction.
Thus,diversity of responses(option B) is the most effective way to keep users engaged and retain them on the platform.

NEW QUESTION # 36
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