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Databricks Generative AI Engineer Associate exam dumps: questions 101 to 200 of 305

Page 2 of the free Databricks Generative AI Engineer Associate question bank for the Databricks Certified Generative AI Engineer Associate exam. Questions 101 to 200 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

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Free Databricks Generative AI Engineer Associate practice questions

Questions 101 to 105 of 305

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Databricks Generative AI Engineer Associate Question 101

Select 2

You are tasked with building a Generative AI application that integrates external data from multiple APIs, provides conversational capabilities, and requires document retrieval for prompt generation. Which of the following tools or approaches would be most appropriate to streamline the development of this application?

A
LangChain for managing the chain of prompts, data retrieval, and API integrations
B
Hugging Face Transformers for fine-tuning a pre-trained model to improve conversational accuracy
C
PyTorch for building and training a custom model from scratch
D
Vector databases (e.g., Pinecone, Weaviate) for efficient document retrieval and similarity search
E
OpenCV for processing and analyzing images within the application

Show answer and explanation

Correct answers: A, D

Explanation

The scenario involves building a Generative AI application with conversational capabilities, external API integrations, and document retrieval. LangChain is an excellent tool for managing workflows, such as chaining prompts and integrating APIs, while vector databases provide efficient document retrieval and similarity search. Together, these tools streamline the development of the required application. Other options, like fine-tuning a model or using OpenCV, are not directly relevant to the requirements described in the question.

A. Correct.
LangChain is highly suitable for managing complex workflows in a Generative AI application, such as chaining prompts, retrieving data from APIs, and structuring interactions with external tools.
B. Incorrect.
Fine-tuning a Hugging Face Transformers model can improve conversational accuracy, but it is not directly related to managing workflows or integrating external tools, which is the focus of the question.
C. Incorrect.
Building a custom model from scratch using PyTorch is unnecessary for this scenario. Pre-trained models and existing frameworks (e.g., LangChain) are better suited for simplifying development.
D. Correct.
Vector databases are essential for document retrieval and similarity search, which is a key part of the application's requirements. They work well with tools like LangChain to fetch relevant data efficiently.
E. Incorrect.
OpenCV is a library for image processing and analysis. It is unrelated to the requirements of this Generative AI application, which focus on conversational capabilities, data retrieval, and APIs.

Databricks Generative AI Engineer Associate Question 102

Select 2

You are building a Generative AI application for summarizing customer support conversations. The application needs to integrate a Large Language Model (LLM) and retrieve conversation history stored in a database. Which of the following tool(s) would be most appropriate to use in this scenario?

A
LangChain
B
Hugging Face Transformers
C
Pandas
D
Pinecone
E
TensorFlow

Show answer and explanation

Correct answers: A, D

Explanation

LangChain is particularly useful for building workflows that connect LLMs with external data sources, like conversation history stored in databases. Pinecone adds value by serving as a vector database for efficient retrieval of conversation embeddings, making these tools ideal for this scenario. While Hugging Face Transformers and TensorFlow are powerful frameworks, they are not designed for the specific needs of this Generative AI application. Similarly, Pandas is better suited for data manipulation than for application or workflow development.

A. Correct.
LangChain is designed for building applications that integrate LLMs with external data sources, such as databases. It provides tools for chaining prompts and retrieving data, making it highly suitable for this scenario.
B. Incorrect.
Hugging Face Transformers is a library for working with pre-trained models, but it does not inherently handle integration with databases or provide tools for prompt chaining, which are necessary for this use case.
C. Incorrect.
Pandas is a data manipulation tool, but it is not designed for interacting with LLMs or building applications that integrate databases with LLMs.
D. Correct.
Pinecone is a vector database designed to store and retrieve embeddings efficiently, making it a good choice for retrieving conversation history in a way that complements LLM-based summarization.
E. Incorrect.
TensorFlow is a machine learning framework primarily used for training and deploying models, but it is not designed for integrating LLMs with databases or handling application workflows.

Databricks Generative AI Engineer Associate Question 103

Single answer

You are working on a generative AI model to summarize articles. Initially, you use the prompt: 'Summarize this article in one sentence.' However, you notice the summaries are too vague. To improve the output, you modify the prompt to: 'Summarize this article in one sentence, focusing on key events and specific details.' What is the impact of this change in the prompt format?

A
The modified prompt helps the model generate more detailed and specific summaries.
B
The modified prompt reduces the model's ability to summarize articles accurately.
C
The modified prompt has no impact as the model outputs are entirely dependent on its training data.
D
The modified prompt causes the model to focus more on generating creative rather than factual summaries.

Show answer and explanation

Correct answer: A

Explanation

The way a prompt is structured can significantly impact the output of a generative AI model. By modifying the prompt to include specific instructions, such as focusing on key events and details, the model is better guided to produce the desired type of response. This demonstrates the importance of prompt engineering in leveraging generative AI effectively.

A. Correct.
This is correct. The modified prompt provides additional guidance to the model, leading to more detailed and specific outputs.
B. Incorrect.
This is incorrect. Adding specificity to the prompt usually improves the quality of the output, not reduces it.
C. Incorrect.
This is incorrect. While the model's training data plays a role, the prompt format significantly influences the output by guiding the model's response.
D. Incorrect.
This is incorrect. The modified prompt is designed to focus on factual details, not creative content.

Databricks Generative AI Engineer Associate Question 104

Select 3

You are fine-tuning a generative AI model with Databricks to summarize customer support tickets. During testing, you observe that the summaries are either too detailed or too vague depending on how the prompt is structured. Which of the following prompt adjustments can help you achieve more consistent and concise summaries?

A
Clearly specify the desired summary length, such as 'Summarize this in no more than 50 words.'
B
Include examples of desired outputs in the prompt to guide the model's response style.
C
Avoid using specific instructions in the prompt as they may confuse the model.
D
Use open-ended prompts such as 'What do you think about this?' to encourage creativity.
E
Incorporate keywords like 'concise' or 'brief' to influence the tone of the summary.

Show answer and explanation

Correct answers: A, B, E

Explanation

Prompt engineering is critical when working with generative AI models. Clear instructions, examples, and guiding keywords help models produce consistent and high-quality outputs. Ambiguous or open-ended prompts can lead to unpredictable results, especially in tasks like summarization where structure and precision are essential.

A. Correct.
Specifying the desired summary length helps set clear expectations for the model, leading to outputs that align with your requirements.
B. Correct.
Providing examples of desired outputs in the prompt helps the model better understand the expected format and tone, leading to more consistent results.
C. Incorrect.
Avoiding specific instructions can lead to ambiguous outputs, as the model may not clearly understand your requirements.
D. Incorrect.
Open-ended prompts like 'What do you think about this?' are unsuitable for tasks requiring specific outcomes, such as generating concise summaries.
E. Correct.
Incorporating keywords like 'concise' or 'brief' provides additional guidance to the model, influencing the style and tone of the generated summaries.

Databricks Generative AI Engineer Associate Question 105

Single answer

You are working with a large language model (LLM) to generate customer support responses. You observe that when you include more detailed instructions in the prompt, the responses are more relevant to the query. Which of the following statements best explains why prompt formats impact model outputs?

A
Detailed prompts provide the model with more context, helping it generate better-aligned outputs.
B
Adding more details in the prompt increases the model's processing power, resulting in better responses.
C
Changing the prompt format alters the model's underlying training data, leading to different outputs.
D
Simpler prompts always produce better results as they reduce the complexity of the model's task.

Show answer and explanation

Correct answer: A

Explanation

The way a prompt is structured significantly influences the quality of the model's outputs. Detailed prompts provide the necessary context and constraints, enabling the model to generate responses that are better aligned with the desired outcome. This does not change the model's capabilities or training data but rather optimizes how the model interprets the input.

A. Correct.
This is correct. Providing detailed and clear instructions in the prompt gives the model more context to generate outputs that align with the user's expectations.
B. Incorrect.
This is incorrect. The model's processing power is fixed and does not change based on the input prompt.
C. Incorrect.
This is incorrect. Adjusting prompt formats does not modify the model's training data; it only changes how the input is structured.
D. Incorrect.
This is incorrect. While simpler prompts may work in some cases, they often lack sufficient detail to guide the model effectively, especially for complex tasks.

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Databricks Generative AI Engineer Associate practice questions 101 to 200 of 305

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