Pass Exam With Full Sureness – Databricks-Generative-AI-Engineer-Associate Dumps with 94 Questions [Q10-Q28]

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Pass Exam With Full Sureness – Databricks-Generative-AI-Engineer-Associate Dumps with 94 Questions

Verified Databricks-Generative-AI-Engineer-Associate dumps Q&As – 100% Pass from Real4dumps

Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Generative AI Engineer Associate
Exam Number:	Databricks-Generative-AI-Engineer-Associate
Available Languages:	English
Exam Format:	Multiple choice, Multiple select
Exam Price:	USD 200
Real Exam Qty:	45-60
Certificate Validity Period:	2 years
Related Certifications:	Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate
Exam Duration:	90 minutes
Recommended Training:	Databricks Academy – Generative AI Courses Mosaic AI Documentation
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions
Exam Way:	Online proctored exam
Pre Condition:	No strict prerequisites, but experience with Python, machine learning fundamentals, and Databricks platform is recommended.
Official Syllabus URL:	https://www.databricks.com/learn/certification

NEW QUESTION 10
A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.
Which of the following components will NOT be useful in building such a chatbot?

Response-generating LLM

Invite users to submit long, rather than concise, questions

Vector database

Embedding model

The task involves building a question-answering chatbot for an automotive company using a database of vehicle-related documents. The chatbot must efficiently process customer inquiries and provide accurate responses. Let’s evaluate each component to determine which isnotuseful, per Databricks Generative AI Engineer principles.
* Option A: Response-generating LLM
* An LLM is essential for generating natural language responses to customer queries based on retrieved information. This is a core component of any chatbot.
* Databricks Reference:”The response-generating LLM processes retrieved context to produce coherent answers”(“Building LLM Applications with Databricks,” 2023).
* Option B: Invite users to submit long, rather than concise, questions
* Encouraging long questions is a user interaction design choice, not a technical component of the chatbot’s architecture. Moreover, long, verbose questions can complicate intent detection and retrieval, reducing efficiency and accuracy-counter to best practices for chatbot design. Concise questions are typically preferred for clarity and performance.
* Databricks Reference: While not explicitly stated, Databricks’ “Generative AI Cookbook” emphasizes efficient query processing, implying that simpler, focused inputs improve LLM performance. Inviting long questions doesn’t align with this.
* Option C: Vector database
* A vector database stores embeddings of the vehicle documents, enabling fast retrieval of relevant information via semantic search. This is critical for a question-answering system with a large document corpus.
* Databricks Reference:”Vector databases enable scalable retrieval of context from large datasets”(“Databricks Generative AI Engineer Guide”).
* Option D: Embedding model
* An embedding model converts text (documents and queries) into vector representations for similarity search. It’s a foundational component for retrieval-augmented generation (RAG) in chatbots.
* Databricks Reference:”Embedding models transform text into vectors, facilitating efficient matching of queries to documents”(“Building LLM-Powered Applications”).
Conclusion: Option B is not a usefulcomponentin building the chatbot. It’s a user-facing suggestion rather than a technical building block, and it could even degrade performance by introducing unnecessary complexity. Options A, C, and D are all integral to a Databricks-aligned chatbot architecture.

NEW QUESTION 11
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error:
Python
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
prompt_template = ” Tell me a {adjective} joke “
prompt = PromptTemplate(input_variables=[ ” adjective ” ], template=prompt_template)
# … (Error-prone section)
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

(Incorrect structure)

prompt_template = ” Tell me a {adjective} joke “
prompt = PromptTemplate(input_variables=[ ” adjective ” ], template=prompt_template) llm = OpenAI() llm_chain = LLMChain(prompt=prompt, llm=llm) llm_chain.generate([{ ” adjective ” : ” funny ” }])

(Incorrect structure)

NEW QUESTION 12
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?

Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.

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.

Benchmark multiple LLMs with the same data and pick the best LLM for the job.

Use an LLM-as-a-judge to evaluate the quality of the final answers generated.

NEW QUESTION 13
A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team’s latest standings.
How could the Generative AI Engineer best design these capabilities into their system?

Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.

Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.

Instruct the LLM to respond with “RAG”, “API”, or “TABLE” depending on the query, then use text parsing and conditional statements to resolve the query.

Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.

In this scenario, the Generative AI Engineer needs to design a system that can handle different types of queries about the monster truck team. The queries may involve text-based information, API lookups for event dates, or table queries for standings. The best solution is to implement atool-based agent system.
Here’s how option B works, and why it’s the most appropriate answer:
* System Design Using Agent-Based Model:In modern agent-based LLM systems, you can design a system where the LLM (Large Language Model) acts as a central orchestrator. The model can “decide” which tools to use based on the query. These tools can include API calls, table lookups, or natural language searches. The system should contain asystem promptthat informs the LLM about the available tools.
* System Prompt Listing Tools:By creating a well-craftedsystem prompt, the LLM knows which tools are at its disposal. For instance, one tool may query an external API for event dates, another might look up standings in a database, and a third may involve searching a vector database for general text-based information. Theagentwill be responsible for calling the appropriate tool depending on the query.
* Agent Orchestration of Calls:The agent system is designed to execute a series of steps based on the incoming query. If a user asks for the next event date, the system will recognize this as a task that requires an API call. If the user asks about standings, the agent might query the appropriate table in the database. For text-based questions, it may call a search function over ingested data. The agent orchestrates this entire process, ensuring the LLM makes calls to the right resources dynamically.
* Generative AI Tools and Context:This is a standard architecture for integrating multiple functionalities into a system where each query requires different actions. The core design in option B is efficient because it keeps the system modular and dynamic by leveraging tools rather than overloading the LLM with static information in a system prompt (like option D).
* Why Other Options Are Less Suitable:
* A (RAG Architecture): While relevant, simply ingesting PDFs into a vector store only helps with text-based retrieval. It wouldn’t help with API lookups or table queries.
* C (Conditional Logic with RAG/API/TABLE): Although this approach works, it relies heavily on manual text parsing and might introduce complexity when scaling the system.
* D (System Prompt with Event Dates and Standings): Hardcoding dates and table information into a system prompt isn’t scalable. As the standings or events change, the system would need constant updating, making it inefficient.
By bundling multiple tools into a single agent-based system (as in option B), the Generative AI Engineer can best handle the diverse requirements of this system.

NEW QUESTION 14
A Generative AI Engineer is deploying an agent using Mosaic AI Model Serving. The agent needs to access various Databricks resources, including Vector Search, Databricks SQL, and Functions. They need to find the easiest and best-practice way to authenticate the deployed agent to access these resources.
What approach should they choose?

Log the authentication token while logging the agent; this token will be automatically used for authentication.

Set appropriate permissions on the Model Serving endpoint for the agent, as these permissions will be used when connecting to other resources.

Embed authentication credentials within the agent’s code to access the required resources.

Define resource dependencies while logging the agent and deploy it with the Agent Framework.

NEW QUESTION 15
A Generative Al Engineer is building a system that will answer questions on currently unfolding news topics. As such, it pulls information from a variety of sources including articles and social media posts. They are concerned about toxic posts on social media causing toxic outputs from their system.
Which guardrail will limit toxic outputs?

Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM.

Implement rate limiting

Reduce the amount of context Items the system will Include in consideration for its response.

Log all LLM system responses and perform a batch toxicity analysis monthly.

The system answers questions on unfolding news topics using articles and social media, with a concern about toxic outputs from toxic inputs. A guardrail must limit toxicity in the LLM’s responses. Let’s evaluate the options.
Option A: Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM Curating input sources (e.g., verified accounts) reduces exposure to toxic content at the data ingestion stage, directly limiting toxic outputs. This is a proactive guardrail aligned with data quality control.
Databricks Reference: “Control input data quality to mitigate unwanted LLM behavior, such as toxicity” (“Building LLM Applications with Databricks,” 2023).
Option B: Implement rate limiting
Rate limiting controls request frequency, not content quality. It prevents overload but doesn’t address toxicity in social media inputs or outputs.
Databricks Reference: Rate limiting is for performance, not safety: “Use rate limits to manage compute load” (“Generative AI Cookbook”).
Option C: Reduce the amount of context items the system will include in consideration for its response Reducing context might limit exposure to some toxic items but risks losing relevant information, and it doesn’t specifically target toxicity. It’s an indirect, imprecise fix.
Databricks Reference: Context reduction is for efficiency, not safety: “Adjust context size based on performance needs” (“Databricks Generative AI Engineer Guide”).
Option D: Log all LLM system responses and perform a batch toxicity analysis monthly Logging and analyzing responses is reactive, identifying toxicity after it occurs rather than preventing it. Monthly analysis doesn’t limit real-time toxic outputs.
Databricks Reference: Monitoring is for auditing, not prevention: “Log outputs for post-hoc analysis, but use input filters for safety” (“Building LLM-Powered Applications”).
Conclusion: Option A is the most effective guardrail, proactively filtering toxic inputs from unverified sources, which aligns with Databricks’ emphasis on data quality as a primary safety mechanism for LLM systems.

NEW QUESTION 16
A Generative AI Engineer is building an interactive catalog for a company’s inventory system that allows users to search for any item using a plain-text description. There are currently about 17,000 items, and new items are not frequently added. They need a solution that will be the most cost-effective and easy for the company to maintain.
Which solution should the engineer choose?

Storage-optimized vector search with a Direct Vector Access index, triggered sync.

Standard vector search with Databricks-managed embeddings and a Delta Sync index, continuous sync.

Standard vector search with self-managed embeddings and a Delta Sync index, continuous sync.

Standard vector search with Databricks-managed embeddings and a Delta Sync index, triggered sync.

NEW QUESTION 17
A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here’s a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.

You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
Here’s an example: {“date”: “April 16, 2024”, “sender_email”: “[email protected]”, “order_id”:
“RE987D”}

You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.

You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.

NEW QUESTION 18
A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?

Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.

Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language

Incorporate manual reviews to correct any problematic outputs prior to sending to the users

NEW QUESTION 19
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?

Limit the number of relevant documents available for the RAG application to retrieve from

Pick a smaller LLM that is domain-specific

Limit the number of queries a customer can send per day

Use the largest LLM possible because that gives the best performance for any general queries

NEW QUESTION 20
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?

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

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

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

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

NEW QUESTION 21
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.

Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

NEW QUESTION 22
A Generative AI Engineer has been reviewing issues with their company’s LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?

It allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses.

It allows you to reduce the latency of your applications. By having multiple chains participating in the response as a chain, you increase the rate at which the response is generated.

It allows you to decrease the effort involved in crafting a prompt. Chains make it possible to reuse prompt text across multiple different use cases.

It reduces the average cost of a typical request. Chains make more efficient use of the tokens produced to generate higher quality responses with fewer tokens.

NEW QUESTION 23
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?

context length 514; smallest model is 0.44GB and embedding dimension 768

context length 2048: smallest model is 11GB and embedding dimension 2560

context length 32768: smallest model is 14GB and embedding dimension 4096

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

NEW QUESTION 24
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?

flask

beautifulsoup

unstructured

numpy

NEW QUESTION 25
A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM’s on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?

ROUGE metric

BLEU metric

NDCG metric

RECALL metric

NEW QUESTION 26
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?

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

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

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

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

NEW QUESTION 27
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.

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

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

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

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

NEW QUESTION 28
A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?

Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.

Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language

Incorporate manual reviews to correct any problematic outputs prior to sending to the users

Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM’s response, LLM guardrails, and the best LLM based on the attributes of the application.
Topic 2	Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 3	Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
Topic 4	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.

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Pass Exam With Full Sureness – Databricks-Generative-AI-Engineer-Associate Dumps with 94 Questions [Q10-Q28]

Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:

Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

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