2025 DATABRICKS DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE: PERFECT PASS4SURE DATABRICKS CERTIFIED GENERATIVE AI ENGINEER ASSOCIATE STUDY MATERIALS

2025 Databricks Databricks-Generative-AI-Engineer-Associate: Perfect Pass4sure Databricks Certified Generative AI Engineer Associate Study Materials

2025 Databricks Databricks-Generative-AI-Engineer-Associate: Perfect Pass4sure Databricks Certified Generative AI Engineer Associate Study Materials

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

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 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.
Topic 4
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.
Topic 5
  • 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.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q10-Q15):

NEW QUESTION # 10
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.

Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

  • A.
  • B.
  • C.
  • D.

Answer: D

Explanation:
To fix the error in the LangChain code provided for using a simple prompt template, the correct approach is Option C. Here's a detailed breakdown of why Option C is the right choice and how it addresses the issue:
* Proper Initialization: In Option C, the LLMChain is correctly initialized with the LLM instance specified as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it specifies which model to use for generating responses.
* Correct Use of Classes and Methods:
* The PromptTemplate is defined with the correct format, specifying that adjective is a variable within the template. This allows dynamic insertion of values into the template when generating text.
* The prompt variable is properly linked with the PromptTemplate, and the final template string is passed correctly.
* The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the template and the model are properly linked for generating output.
Why Other Options Are Incorrect:
* Option A: Misuses the parameter passing in generate method by incorrectly structuring the dictionary.
* Option B: Incorrectly uses prompt.format method which does not exist in the context of LLMChain and PromptTemplate configuration, resulting in potential errors.
* Option D: Incorrect order and setup in the initialization parameters for LLMChain, which would likely lead to a failure in recognizing the correct configuration for prompt and LLM usage.
Thus, Option C is correct because it ensures that the LangChain components are correctly set up and integrated, adhering to proper syntax and logical flow required by LangChain's architecture. This setup avoids common pitfalls such as type errors or method misuses, which are evident in other options.


NEW QUESTION # 11
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

  • A. Reduce the number of records retrieved from the vector database
  • B. Reduce the maximum output tokens of the new model
  • C. Decrease the chunk size of embedded documents
  • D. Retrain the response generating model using ALiBi
  • E. Use a smaller embedding model to generate

Answer: A,C

Explanation:
* Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options:
* Option A: Use a smaller embedding model to generate- This wouldn't necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model- This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents- This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.
* Option D: Reduce the number of records retrieved from the vector database- By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi- Retraining the model is contrary to the stipulation not to change the response generating model.
OptionsCandDare the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.


NEW QUESTION # 12
A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.
What are the steps needed to build this RAG application and deploy it?

  • A. Ingest documents from a source -> Index the documents and saves to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> Evaluate model -> LLM generates a response -> Deploy it using Model Serving
  • B. Ingest documents from a source -> Index the documents and save to Vector Search -> Evaluate model -
    > Deploy it using Model Serving
  • C. User submits queries against an LLM -> Ingest documents from a source -> Index the documents and save to Vector Search -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
  • D. Ingest documents from a source -> Index the documents and save to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving

Answer: D

Explanation:
The Generative AI Engineer needs to follow a methodical pipeline to build and deploy a Retrieval- Augmented Generation (RAG) application. The steps outlined in optionBaccurately reflect this process:
* Ingest documents from a source: This is the first step, where the engineer collects documents (e.g., technical regulations) that will be used for retrieval when the application answers user questions.
* Index the documents and save to Vector Search: Once the documents are ingested, they need to be embedded using a technique like embeddings (e.g., with a pre-trained model like BERT) and stored in a vector database (such as Pinecone or FAISS). This enables fast retrieval based on user queries.
* User submits queries against an LLM: Users interact with the application by submitting their queries.
These queries will be passed to the LLM.
* LLM retrieves relevant documents: The LLM works with the vector store to retrieve the most relevant documents based on their vector representations.
* LLM generates a response: Using the retrieved documents, the LLM generates a response that is tailored to the user's question.
* Evaluate model: After generating responses, the system must be evaluated to ensure the retrieved documents are relevant and the generated response is accurate. Metrics such as accuracy, relevance, and user satisfaction can be used for evaluation.
* Deploy it using Model Serving: Once the RAG pipeline is ready and evaluated, it is deployed using a model-serving platform such as Databricks Model Serving. This enables real-time inference and response generation for users.
By following these steps, the Generative AI Engineer ensures that the RAG application is both efficient and effective for the task of answering technical regulation questions.


NEW QUESTION # 13
A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn't hallucinate or leak confidential data.
Which approach should NOT be used to mitigate hallucination or confidential data leakage?

  • A. Limit the data available based on the user's access level
  • B. Fine-tune the model on your data, hoping it will learn what is appropriate and not
  • C. Use a strong system prompt to ensure the model aligns with your needs.
  • D. Add guardrails to filter outputs from the LLM before it is shown to the user

Answer: B

Explanation:
When addressing concerns of hallucination and data leakage in an LLM application for internal company policies, fine-tuning the model on internal data with the hope it learns data boundaries can be problematic:
* Risk of Data Leakage: Fine-tuning on sensitive or confidential data does not guarantee that the model will not inadvertently include or reference this data in its outputs. There's a risk of overfitting to the specific data details, which might lead to unintended leakage.
* Hallucination: Fine-tuning does not necessarily mitigate the model's tendency to hallucinate; in fact, it might exacerbate it if the training data is not comprehensive or representative of all potential queries.
Better Approaches:
* A,C, andDinvolve setting up operational safeguards and constraints that directly address data leakage and ensure responses are aligned with specific user needs and security levels.
Fine-tuning lacks the targeted control needed for such sensitive applications and can introduce new risks, making it an unsuitable approach in this context.


NEW QUESTION # 14
A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?

  • A. There should be two different chatbots handling different types of user queries.
  • B. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it's an upcoming event question, send the query to a text-to-SQL model. If it's about ticket purchasing, the customer should be redirected to a payment platform.
  • C. The chatbot should only process payments
  • D. The chatbot should only look at previous event information

Answer: B

Explanation:
* Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.
* Explanation of Options:
* Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.
* Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.
* Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.
* Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.
Option Coffers a comprehensive workflow that maximizes the chatbot's utility and responsiveness to different user needs, aligning perfectly with the business requirements.


NEW QUESTION # 15
......

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