DatabricksFree levelPage 4 of 4

Databricks Generative AI Engineer Associate exam dumps: questions 301 to 305 of 305

Page 4 of the free Databricks Generative AI Engineer Associate question bank for the Databricks Certified Generative AI Engineer Associate exam. Questions 301 to 305 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 301 to 305

Free Databricks Generative AI Engineer Associate practice questions

Questions 301 to 305 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 301

Select 4

You are building a Retrieval-Augmented Generation (RAG) application on Databricks that uses a large language model (LLM) for answering user queries. The application frequently queries the LLM, leading to high costs. Which strategies can you implement using Databricks features to control these costs while maintaining application performance?

  1. A

    Implement a caching mechanism using Databricks Delta tables to store frequently queried LLM responses.

  2. B

    Use fine-tuning on a smaller, domain-specific LLM instead of relying on a general-purpose large LLM.

  3. C

    Leverage Databricks AutoML to replace the LLM entirely for generating responses.

  4. D

    Optimize the retrieval step by using vector databases like Databricks Lakehouse with embeddings to minimize unnecessary LLM calls.

  5. E

    Batch user queries and process them together to reduce the number of individual LLM calls.

Show answer and explanation

Correct answers: A, B, D, E

Explanation

Controlling LLM costs in RAG applications involves reducing the frequency and scope of LLM queries while maintaining performance. Strategies like caching responses, fine-tuning smaller LLMs, optimizing retrieval steps, and batching queries are effective cost-saving techniques that can be implemented using Databricks features. Replacing the LLM entirely with AutoML is not a valid approach, as it does not fulfill the generative requirements of the RAG workflow.

  • A. Correct.

    Caching frequently queried responses in Databricks Delta tables reduces the number of repeated LLM queries, thereby lowering costs. It is an effective and scalable approach supported in Databricks.

  • B. Correct.

    Using a smaller, fine-tuned LLM tailored for your specific domain reduces costs while still delivering high-quality results, as smaller models are cheaper to run.

  • C. Incorrect.

    Databricks AutoML focuses on traditional machine learning tasks and is not designed to replace LLMs for natural language generation. This is not a suitable option for RAG applications.

  • D. Correct.

    Optimizing the retrieval step by using vector databases like Databricks Lakehouse ensures only the most relevant information is sent to the LLM, reducing unnecessary queries and associated costs.

  • E. Correct.

    Batching user queries allows you to process multiple inputs together, which can reduce the frequency of LLM calls and improve cost efficiency.

Databricks Generative AI Engineer Associate Question 302

Select 3

You are tasked with building a Retrieval-Augmented Generation (RAG) application on Databricks that uses a Large Language Model (LLM) for question answering. To control costs, you want to minimize the number of LLM calls while maintaining high performance. Which strategies could you implement using Databricks features?

  1. A

    Cache embeddings and intermediate results in Delta tables to reduce redundant computations.

  2. B

    Use Databricks Auto-Loader to directly preprocess user inputs before sending them to the LLM.

  3. C

    Leverage vector search in Databricks to retrieve relevant context before making an LLM call.

  4. D

    Apply batching to group multiple user queries and send them to the LLM as a single request.

  5. E

    Run all LLM queries in real-time without pre-filtering to ensure the highest accuracy.

Show answer and explanation

Correct answers: A, C, D

Explanation

To control costs for RAG applications in Databricks, strategies like caching intermediate results in Delta tables, leveraging vector search, and batching queries can minimize unnecessary LLM usage while maintaining performance. These approaches align with the principles of cost-effective and efficient LLM utilization on Databricks.

  • A. Correct.

    Caching embeddings and intermediate results in Delta tables reduces redundant computations and avoids unnecessary LLM calls, effectively lowering costs.

  • B. Incorrect.

    Databricks Auto-Loader is used for ingesting streaming or batch data, not for preprocessing user inputs for LLM queries. It is unrelated to this scenario.

  • C. Correct.

    Using vector search in Databricks allows you to retrieve the most relevant context for the query, reducing the LLM's workload and limiting expenditure.

  • D. Correct.

    Batching queries reduces the number of LLM calls by grouping multiple inputs together, which can significantly cut costs.

  • E. Incorrect.

    Running all LLM queries in real-time without pre-filtering leads to higher costs and is not an efficient or scalable approach.

Databricks Generative AI Engineer Associate Question 303

Select 2

You are building a Retrieval-Augmented Generation (RAG) application on Databricks that uses a large language model (LLM) for answering user queries based on your enterprise's proprietary documents. To control costs associated with LLM usage, which of the following strategies can be implemented on Databricks?

  1. A

    Use Databricks Feature Store to cache embeddings and retrieve them instead of calling the LLM repeatedly.

  2. B

    Reduce the context size of prompts sent to the LLM by performing pre-filtering of retrieved documents.

  3. C

    Employ a smaller, fine-tuned LLM hosted on Databricks instead of calling an external API for a larger base model.

  4. D

    Increase the number of tokens sent to the LLM to improve response accuracy regardless of cost considerations.

  5. E

    Utilize Databricks Auto Loader to batch queries and send them to the LLM in bulk to save costs.

Show answer and explanation

Correct answers: B, C

Explanation

To control costs in a RAG application, it is essential to minimize the number of tokens sent to the LLM and reduce dependency on expensive external APIs by using smaller, fine-tuned models. Pre-filtering retrieved documents reduces the prompt size, thereby lowering token usage, while hosting a fine-tuned model on Databricks eliminates external API costs.

  • A. Incorrect.

    Incorrect. While the Databricks Feature Store is useful for managing and sharing features in ML pipelines, it is not directly applicable for caching embeddings or reducing LLM costs in a RAG application.

  • B. Correct.

    Correct. Reducing the context size by pre-filtering retrieved documents before sending them to the LLM ensures that fewer tokens are processed, which directly reduces LLM usage costs.

  • C. Correct.

    Correct. Using a smaller, fine-tuned LLM hosted on Databricks can significantly reduce costs compared to relying on large external models through API calls.

  • D. Incorrect.

    Incorrect. Increasing the number of tokens sent to the LLM increases costs and is counterproductive to cost control.

  • E. Incorrect.

    Incorrect. Databricks Auto Loader is used for efficient ingestion of streaming data, and it cannot be used to batch queries for LLMs.

Databricks Generative AI Engineer Associate Question 304

Select 4

You are designing a Retrieval-Augmented Generation (RAG) application on Databricks that utilizes a large language model (LLM). To minimize operational costs, which of the following actions should you take when configuring your Databricks environment and workflow?

  1. A

    Implement caching mechanisms to store frequently used embeddings or query results.

  2. B

    Use smaller fine-tuned LLMs for query handling instead of defaulting to large, general-purpose models.

  3. C

    Always use the largest LLM available to ensure high accuracy for all use cases.

  4. D

    Leverage Databricks Auto Loader to preprocess and filter data before sending it to the LLM.

  5. E

    Optimize the retrieval process by embedding documents in advance and storing them in a vector database.

Show answer and explanation

Correct answers: A, B, D, E

Explanation

To control LLM costs in a RAG application on Databricks, it is important to optimize both the data processing pipeline and the LLM usage. Strategies such as caching, using smaller fine-tuned models, preprocessing data, and leveraging pre-computed embeddings ensure efficient resource utilization and reduce the frequency and intensity of LLM calls. Avoiding the largest models unless necessary further helps in cost reduction.

  • A. Correct.

    Implementing caching mechanisms reduces redundant LLM calls for repeated or similar queries, lowering compute costs.

  • B. Correct.

    Using smaller fine-tuned LLMs is cost-effective and can still provide sufficient accuracy for specific tasks, avoiding the high computational expense of larger models.

  • C. Incorrect.

    Using the largest LLM available increases costs significantly and is unnecessary for all use cases, especially when smaller models or optimized workflows can suffice.

  • D. Correct.

    Databricks Auto Loader helps preprocess and filter data, ensuring only relevant inputs are sent to the LLM, which reduces unnecessary compute expenses.

  • E. Correct.

    Optimizing the retrieval process by pre-embedding documents and storing them in a vector database minimizes real-time computation, reducing costs associated with embedding generation.

Databricks Generative AI Engineer Associate Question 305

Select 4

You are designing a Retrieval-Augmented Generation (RAG) application on Databricks to integrate with a Large Language Model (LLM). The application needs to minimize costs while maintaining performance. Which of the following approaches can help optimize LLM usage costs in Databricks?

  1. A

    Implement caching for frequently used query results.

  2. B

    Use Databricks' Auto Loader to directly query the LLM for each request.

  3. C

    Fine-tune the LLM on your specific dataset using Databricks' MLflow integration.

  4. D

    Leverage vector databases to retrieve relevant context and reduce token input size.

  5. E

    Set up usage logging and monitoring to identify and optimize high-cost queries.

Show answer and explanation

Correct answers: A, C, D, E

Explanation

Optimizing LLM costs in Databricks for a RAG application involves strategies like caching frequently used results, fine-tuning models for targeted accuracy, and leveraging vector databases to reduce the token input size. Additionally, usage monitoring helps identify and address inefficiencies. Directly querying the LLM for every request without optimization is not cost-effective.

  • A. Correct.

    Caching frequently used query results reduces the number of calls to the LLM, lowering costs while speeding up response times.

  • B. Incorrect.

    Querying the LLM for every request without optimization increases costs unnecessarily and is not a cost-effective solution.

  • C. Correct.

    Fine-tuning the LLM on your dataset can improve its accuracy for your use case, reducing the need for excessive token usage or redundant queries.

  • D. Correct.

    Using vector databases allows the system to efficiently retrieve relevant context, reducing the token input size sent to the LLM, which directly reduces costs.

  • E. Correct.

    Setting up usage logging and monitoring enables you to identify high-cost queries and optimize them for cost efficiency.

Timed practice exam

Take a Databricks Generative AI Engineer Associate practice test under exam conditions

45 questions in 90 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam

Databricks Generative AI Engineer Associate practice questions 301 to 305 of 305

Every question has a page with the answer and explanation. Numbers are stable, so you can bookmark or share them. The bank is split into 4 pages of up to 100 questions.

  1. 301.You are building a Retrieval-Augmented Generation (RAG) application on Databricks that uses a large language...
  2. 302.You are tasked with building a Retrieval-Augmented Generation (RAG) application on Databricks that uses a...
  3. 303.You are building a Retrieval-Augmented Generation (RAG) application on Databricks that uses a large language...
  4. 304.You are designing a Retrieval-Augmented Generation (RAG) application on Databricks that utilizes a large...
  5. 305.You are designing a Retrieval-Augmented Generation (RAG) application on Databricks to integrate with a Large...

More Databricks exam dumps

All Databricks exams
312 questions

Databricks Data Engineer Professional exam dumps

Databricks Certified Data Engineer Professional

531 questions

Databricks Data Engineer Associate exam dumps

Databricks Certified Data Engineer Associate

655 questions

Databricks Machine Learning Associate exam dumps

Databricks Certified Machine Learning Associate

279 questions

Databricks Machine Learning Professional exam dumps

Databricks Certified Machine Learning Professional