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Databricks-Generative-AI-Engineer-Associate Practice Questions

Question # 1
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. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
D. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.


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.

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.


Question # 2
A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint’s incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server. Which Databricks feature should they use instead which will perform the same task?

A. Vector Search
B. Lakeview
C. DBSQL
D. Inference Tables


D. Inference Tables

Explanation:

Problem Context:

The goal is to monitor theserving endpointfor incoming requests and outgoing responses in aprovisioned throughput model serving endpointwithin aRetrieval-Augmented Generation (RAG) application. The current approach involves using a microservice to log requests and responses to a remote server, but the Generative AI Engineer is looking for a more streamlined solution within Databricks.

Explanation of Options:

Option A: Vector Search: This feature is used to perform similarity searches within vector databases. It doesn’t provide functionality for logging or monitoring requests and responses in a serving endpoint, so it’s not applicable here.

Option B: Lakeview: Lakeview is not a feature relevant to monitoring or logging request-response cycles for serving endpoints. It might be more related to viewing data in Databricks Lakehouse but doesn’t fulfill the specific monitoring requirement.

Option C: DBSQL: Databricks SQL (DBSQL) is used for running SQL queries on data stored in Databricks, primarily for analytics purposes. It doesn’t provide the direct functionality needed to monitor requests and responses in real-time for an inference endpoint.

Option D: Inference Tables: This is the correct answer.Inference Tablesin Databricks are designed to store the results and metadata of inference runs. This allows the system to logincoming requests and outgoing responsesdirectly within Databricks, making it an ideal choice for monitoring the behavior of a provisioned serving endpoint. Inference Tables can be queried and analyzed, enabling easier monitoring and debugging compared to a custom microservice.

Thus,Inference Tablesare the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.


Question # 3
A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs. Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?
A. Limit the number of relevant documents available for the RAG application to retrieve from
B. Pick a smaller LLM that is domain-specific
C. Limit the number of queries a customer can send per day
D. Use the largest LLM possible because that gives the best performance for any general queries


B. Pick a smaller LLM that is domain-specific


Question # 4
A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from. Which will fulfill their need?
A. context length 514; smallest model is 0.44GB and embedding dimension 768
B. context length 2048: smallest model is 11GB and embedding dimension 2560
C. context length 32768: smallest model is 14GB and embedding dimension 4096
D. context length 512: smallest model is 0.13GB and embedding dimension 384


D. context length 512: smallest model is 0.13GB and embedding dimension 384


Question # 5
A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application. What strategy should the Generative AI Engineer use?
A. Switch to using External Models instead
B. Deploy the model using pay-per-token throughput as it comes with cost guarantees
C. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues
D. Throttle the incoming batch of requests manually to avoid rate limiting issues


B. Deploy the model using pay-per-token throughput as it comes with cost guarantees

Explanation:

Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.

Explanation of Options:

Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.

Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.

Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.

Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.

OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.

Question # 6
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. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
D. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.


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.

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.

Question # 7
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. Add guardrails to filter outputs from the LLM before it is shown to the user
B. Fine-tune the model on your data, hoping it will learn what is appropriate and not
C. Limit the data available based on the user’s access level
D. Use a strong system prompt to ensure the model aligns with your needs.


B. Fine-tune the model on your data, hoping it will learn what is appropriate and not

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.


Question # 8
A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible. Which combination of chaining components and configuration meets these requirements?
A. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.
B. The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
C. For the question-answering application, prompt engineering and an LLM are required to generate answers.
D. For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.


A. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.

Explanation:

Problem Context: The task is to build an LLM-based question-answering application that integrates new documents frequently with minimal costs and development efforts.

Explanation of Options:

Option A: Utilizes a prompt and a retriever, with the retriever output being fed into the LLM. This setup is efficient because it dynamically updates the data pool via the retriever, allowing the LLM to provide up-to-date answers based on the latest documents without needing tofrequently retrain the model. This method offers a balance of cost-effectiveness and functionality.

Option B: Requires frequent retraining of the LLM, which is costly and labor-intensive.

Option C: Only involves prompt engineering and an LLM, which may not adequately handle the requirement for incorporating new documents unless it’s part of an ongoing retraining or updating mechanism, which would increase costs.

Option D: Involves an agent and a fine-tuned LLM, which could be overkill and lead to higher development and operational costs.

Option Ais the most suitable as it provides a cost-effective, minimal development approach while ensuring the application remains up-to-date with new information.


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Databricks Databricks-Generative-AI-Engineer-Associate Exam Dumps

Exam Name: Databricks Certified Generative AI Engineer Associate
Certification Name: Generative AI Engineer

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