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Free and Premium Databricks Databricks-Generative-AI-Engineer-Associate Dumps Questions Answers

Databricks Certified Generative AI Engineer Associate Questions and Answers

Question 1

A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot’s focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:

“Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.”

Which framework type should be implemented to solve this?

Options:

A.

Safety Guardrail

B.

Security Guardrail

C.

Contextual Guardrail

D.

Compliance Guardrail

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Question 2

A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. Thematch should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.

How should the Generative Al Engineer architect their system?

Options:

A.

Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.

B.

Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members’ profiles and perform keyword matching to find the best available team member.

C.

Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.

D.

Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.

Question 3

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?

Options:

A.

The chatbot should only look at previous event information

B.

There should be two different chatbots handling different types of user queries.

C.

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.

D.

The chatbot should only process payments

Question 4

A company has a typical RAG-enabled, customer-facing chatbot on its website.

Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.

Options:

A.

1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM

B.

1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM

C.

1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model

D.

1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model

Question 5

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?

Options:

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.

Question 6

When developing an LLM application, it’s crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.

Which action is NOT appropriate to avoid legal risks?

Options:

A.

Reach out to the data curators directly before you have started using the trained model to let them know.

B.

Use any available data you personally created which is completely original and you can decide what license to use.

C.

Only use data explicitly labeled with an open license and ensure the license terms are followed.

D.

Reach out to the data curators directly after you have started using the trained model to let them know.

Question 7

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?

Options:

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 –> User submits queries against an LLM –> LLM retrieves relevant documents –> LLM generates a response -> Evaluate model –> Deploy it using Model Serving

C.

Ingest documents from a source –> Index the documents and save to Vector Search –> Evaluate model –> Deploy it using Model Serving

D.

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

Question 8

A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.

Which model fits this need?

Options:

A.

DistilBERT

B.

MPT-30B

C.

Llama2-70B

D.

DBRX

Question 9

Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.

What can the engineer do to improve the relevance of the RAG’s response?

Options:

A.

Assess the quality of the retrieved context

B.

Implement caching for frequently asked questions

C.

Use a different LLM to improve the generated response

D.

Use a different semantic similarity search algorithm

Question 10

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?

Options:

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

Question 11

A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author’s web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user’s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.

Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

Options:

A.

Change embedding models and compare performance.

B.

Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.

C.

Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.

Choose the strategy that gives the best performance metric.

D.

Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.

E.

Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.

Question 12

A Generative Al Engineer is responsible for developing a chatbot to enable their company’s internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration:

call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives’ call resolution from fields call_duration and call start_time.

transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files.

call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use.

call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active.

maintenance_schedule – a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes.

They need sources that could add context to best identify ticket root cause and resolution.

Which TWO sources do that? (Choose two.)

Options:

A.

call_cust_history

B.

maintenance_schedule

C.

call_rep_history

D.

call_detail

E.

transcript Volume

Question 13

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.)

Options:

A.

Use a smaller embedding model to generate

B.

Reduce the maximum output tokens of the new model

C.

Decrease the chunk size of embedded documents

D.

Reduce the number of records retrieved from the vector database

E.

Retrain the response generating model using ALiBi