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Databricks Generative AI Engineer Associate exam dumps: questions 201 to 300 of 305

Page 3 of the free Databricks Generative AI Engineer Associate question bank for the Databricks Certified Generative AI Engineer Associate exam. Questions 201 to 300 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated April 2025

Start timed practice exam Questions 201 to 300

Free Databricks Generative AI Engineer Associate practice questions

Questions 201 to 205 of 305

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

Databricks Generative AI Engineer Associate Question 201

Select 4

You are tasked with creating a simple chain using LangChain that concatenates a user's input with a predefined string. Which of the following steps are required to code this chain successfully?

A
Import necessary modules from LangChain, such as SimpleChain and PromptTemplate.
B
Define a PromptTemplate that specifies the format of your input and output.
C
Create a SimpleChain by combining the PromptTemplate with a Language Model (e.g., OpenAI model).
D
Explicitly define a custom tokenizer for the input processing.
E
Initialize the chain and call it with the input data to execute it.

Show answer and explanation

Correct answers: A, B, C, E

Explanation

To create a simple chain in LangChain, you need to import the relevant classes, define a PromptTemplate for input/output structure, combine it with a Language Model using SimpleChain, and execute the chain with input data. Custom tokenization is not required for basic chain creation.

A. Correct.
Correct: Importing necessary modules like SimpleChain and PromptTemplate is required to build a chain in LangChain.
B. Correct.
Correct: PromptTemplate is essential to define the structure of the input and output in the chain.
C. Correct.
Correct: A SimpleChain combines a PromptTemplate with a Language Model to process and generate responses.
D. Incorrect.
Incorrect: Defining a custom tokenizer is not mandatory for creating a simple chain in LangChain. Default tokenization suffices for most use cases.
E. Correct.
Correct: Initializing the chain and calling it with input data is necessary to execute the functionality of the chain.

Databricks Generative AI Engineer Associate Question 202

Select 4

You are tasked with creating a simple LangChain chain that takes a user input question, processes it through an OpenAI LLM, and returns a response. Which of the following steps are necessary to correctly set up and execute this chain?

A
Import the necessary modules from LangChain, such as OpenAI and SimpleChain.
B
Define a Language Model (LLM) using LangChain's OpenAI wrapper.
C
Create a PromptTemplate to structure the input to the LLM.
D
Initialize the SimpleChain with the LLM, passing the PromptTemplate as input.
E
Write custom logic to process the LLM's response since LangChain does not support automatic chaining.

Show answer and explanation

Correct answers: A, B, C, D

Explanation

To create a simple LangChain chain, you need to import the necessary modules, define the LLM, and create a PromptTemplate to structure the input. Then, you initialize the SimpleChain with the LLM and PromptTemplate to handle the input and output flow. LangChain automates many aspects of chaining, so there’s no need for custom logic to process responses unless additional customization is required.

A. Correct.
Correct. Importing the necessary modules is required to use LangChain's functionality, including the OpenAI wrapper and SimpleChain.
B. Correct.
Correct. Defining an LLM is a critical step in setting up LangChain chains as it acts as the core model to process the input data.
C. Correct.
Correct. PromptTemplates are essential to structure the input for the LLM to ensure it receives contextually appropriate queries.
D. Correct.
Correct. SimpleChain is initialized with the LLM and PromptTemplate to define the flow of input and output within the chain.
E. Incorrect.
Incorrect. LangChain has built-in functionality for chaining processes, so custom response processing is not necessary for this scenario.

Databricks Generative AI Engineer Associate Question 203

Select 4

You are tasked with building a simple chain in LangChain that takes a user query as input, passes it to an LLM for processing, and then outputs the LLM's response. Which of the following steps are required to correctly implement this chain?

A
Initialize an LLM instance using LangChain's LLM class or a specific LLM wrapper such as OpenAI.
B
Define a PromptTemplate to structure the input query for the LLM.
C
Use the LangChain Chain class directly to connect the prompt and LLM without any configuration.
D
Combine the PromptTemplate and LLM into an LLMChain to create the chain.
E
Call the LLMChain's run() method with the user query to execute the chain.

Show answer and explanation

Correct answers: A, B, D, E

Explanation

To create a simple chain using LangChain, you need to initialize an LLM instance, define a PromptTemplate to structure the input, and combine these components into an LLMChain. The LLMChain class simplifies the process of connecting the prompt and the LLM. Finally, you execute the chain by calling its run() method with the user query. Directly using the generic Chain class without proper configuration is not sufficient for this task.

A. Correct.
Correct: Initializing an LLM instance is essential as it serves as the core component of the chain that processes the query.
B. Correct.
Correct: A PromptTemplate is necessary to ensure the input query is structured in a way that the LLM can understand effectively.
C. Incorrect.
Incorrect: The LangChain Chain class cannot be directly used without configuration. Instead, specific chain implementations like LLMChain should be used.
D. Correct.
Correct: The LLMChain is the appropriate class to combine the PromptTemplate and the LLM into a functional chain.
E. Correct.
Correct: The run() method of the LLMChain is used to execute the chain by passing the user query as input.

Databricks Generative AI Engineer Associate Question 204

Select 3

You are building a simple chain using LangChain that takes a user's question as input, queries an OpenAI LLM model for an answer, and then returns the response. Which of the following steps are necessary to properly implement this chain?

A
Import the necessary LangChain modules, including the OpenAI LLM model and chain functionality.
B
Define a PromptTemplate to structure the input question before sending it to the LLM.
C
Directly send the user's question as input to the LLM without using any prompt template.
D
Initialize the OpenAI LLM with the required API key or credentials.
E
Implement a SequentialChain for this single-step process to connect the input and output.

Show answer and explanation

Correct answers: A, B, D

Explanation

To code a simple chain using LangChain, you need to import the necessary modules, structure the input using a PromptTemplate, and initialize the LLM with proper credentials. While SequentialChain is useful for multi-step processes, it is not required for this single-step scenario.

A. Correct.
Correct: LangChain requires importing the essential modules, including the OpenAI LLM and chain utilities, to build and execute the chain.
B. Correct.
Correct: Using a PromptTemplate ensures that the input question is formatted appropriately before being processed by the LLM.
C. Incorrect.
Incorrect: Directly sending the user's question without a prompt template is not recommended, as it may lead to inconsistent or poorly structured responses.
D. Correct.
Correct: The OpenAI LLM requires proper initialization with an API key or credentials to authenticate and function correctly.
E. Incorrect.
Incorrect: A SequentialChain is unnecessary for a single-step process, as it is designed for multi-step workflows.

Databricks Generative AI Engineer Associate Question 205

Select 4

You are tasked with building a Retrieval-Augmented Generation (RAG) application on Databricks. Which of the following are essential elements required to create the application?

A
Model flavor
B
Embedding model
C
Retriever
D
Input prompts
E
Model signature
F
Training dataset

Show answer and explanation

Correct answers: A, B, C, E

Explanation

To create a RAG application, you need the basic components such as the model flavor to select the generative model, the embedding model to encode data for retrieval, the retriever to fetch relevant documents, and the model signature to define how the model interacts with inputs and outputs. Input prompts and training datasets are not core requirements for the RAG application setup.

A. Correct.
Model flavor is essential as it specifies the type of generative model (e.g., OpenAI, Hugging Face) being used in the RAG pipeline.
B. Correct.
Embedding model is crucial for converting documents and queries into vector representations for similarity search.
C. Correct.
Retriever is necessary to fetch relevant documents or data from the knowledge base to enhance the generative model's responses.
D. Incorrect.
Input prompts are not a mandatory element for building the RAG application itself; they are typically used during inference or testing.
E. Correct.
Model signature is required to define the input-output structure of the model, ensuring compatibility within the RAG pipeline.
F. Incorrect.
Training dataset is not an essential component for deploying a RAG application. Pretrained models and embeddings are often used.

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Databricks Generative AI Engineer Associate practice questions 201 to 300 of 305

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