SPECIAL LIMITED TIME DISCOUNT OFFER. USE DISCOUNT CODE TO GET 20% OFF DP2021

PDF Only

$80.00 Free Updates Upto 90 Days

Databricks-Generative-AI-Engineer-Associate Dumps PDF
45 Questions
Updated On October 10, 2024

PDF + Test Engine

$160.00 Free Updates Upto 90 Days

Databricks-Generative-AI-Engineer-Associate Question Answers
45 Questions
Updated On October 10, 2024

Test Engine

$120.00 Free Updates Upto 90 Days

Databricks-Generative-AI-Engineer-Associate Practice Questions
45 Questions
Updated On October 10, 2024

Check Our Free Databricks Databricks-Generative-AI-Engineer-Associate Online Test Engine Demo.

Free Demo

How to pass Databricks Databricks-Generative-AI-Engineer-Associate exam with the help of dumps?

Name: Databricks-Generative-AI-Engineer-Associate
Brand: DumpsPool
SKU: Databricks-Generative-AI-Engineer-Associate
Rating: 4.7 (3762 reviews)

DumpsPool provides you the finest quality resources you’ve been looking for to no avail. So, it's due time you stop stressing and get ready for the exam. Our Online Test Engine provides you with the guidance you need to pass the certification exam. We guarantee top-grade results because we know we’ve covered each topic in a precise and understandable manner. Our expert team prepared the latest Databricks Databricks-Generative-AI-Engineer-Associate Dumps to satisfy your need for training. Plus, they are in two different formats: Dumps PDF and Online Test Engine.

How Do I Know Databricks Databricks-Generative-AI-Engineer-Associate Dumps are Worth it?

Did we mention our latest Databricks-Generative-AI-Engineer-Associate Dumps PDF is also available as Online Test Engine? And that’s just the point where things start to take root. Of all the amazing features you are offered here at DumpsPool, the money-back guarantee has to be the best one. Now that you know you don’t have to worry about the payments. Let us explore all other reasons you would want to buy from us. Other than affordable Real Exam Dumps, you are offered three-month free updates.

You can easily scroll through our large catalog of certification exams. And, pick any exam to start your training. That’s right, DumpsPool isn’t limited to just Databricks Exams. We trust our customers need the support of an authentic and reliable resource. So, we made sure there is never any outdated content in our study resources. Our expert team makes sure everything is up to the mark by keeping an eye on every single update. Our main concern and focus are that you understand the real exam format. So, you can pass the exam in an easier way!

IT Students Are Using our Databricks Certified Generative AI Engineer Associate Dumps Worldwide!

It is a well-established fact that certification exams can’t be conquered without some help from experts. The point of using Databricks Certified Generative AI Engineer Associate Practice Question Answers is exactly that. You are constantly surrounded by IT experts who’ve been through you are about to and know better. The 24/7 customer service of DumpsPool ensures you are in touch with these experts whenever needed. Our 100% success rate and validity around the world, make us the most trusted resource candidates use. The updated Dumps PDF helps you pass the exam on the first attempt. And, with the money-back guarantee, you feel safe buying from us. You can claim your return on not passing the exam.

How to Get Databricks-Generative-AI-Engineer-Associate Real Exam Dumps?

Getting access to the real exam dumps is as easy as pressing a button, literally! There are various resources available online, but the majority of them sell scams or copied content. So, if you are going to attempt the Databricks-Generative-AI-Engineer-Associate exam, you need to be sure you are buying the right kind of Dumps. All the Dumps PDF available on DumpsPool are as unique and the latest as they can be. Plus, our Practice Question Answers are tested and approved by professionals. Making it the top authentic resource available on the internet. Our expert has made sure the Online Test Engine is free from outdated & fake content, repeated questions, and false plus indefinite information, etc. We make every penny count, and you leave our platform fully satisfied!

Frequently Asked Questions

Databricks Databricks-Generative-AI-Engineer-Associate Sample Question Answers

Question # 1

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?

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.

Answer: D
Explanation: Problem Context: The problem involves matching team members to new projects based on two main factors: Availability: Ensure the team members are available during the project dates.Profile-Project Match: Use the employee profiles (unstructured text) to find the best match for a project’s scope (also unstructured text). Explanation of Options: Let's break down the provided options to understand why D is the most optimal answer. Option Asuggests embedding project scopes into a vector store and then performing retrieval using team member profiles. While embedding project scopes into a vector store is a valid technique, it skips an important detail: the focus should primarily be on embedding employee profiles because we're matching the profiles to a new project, not the other way around.Option Binvolves using a large language model (LLM) to extract keywords from the project scope and perform keyword matching on employee profiles. While LLMs can help with keyword extraction, this approach is too simplistic and doesn’t leverage advanced retrieval techniques like vector embeddings, which can handle the nuanced and rich semantics of unstructured data. This approach may miss out on subtle but important similarities.Option Csuggests calculating a similarity score between each team member's profile and project scope. While this is a good idea, it doesn’t specify how to handle the unstructured nature of data efficiently. Iterating through each member’s profile individually could be computationally expensive in large teams. It also lacks the mention of using a vector store or an efficient retrieval mechanism.Option Dis the correct approach. Here’s why: Embedding team profiles into a vector store: Using a vector store allows for efficient similarity searches on unstructured data. Embedding the team member profiles into vectors captures their semantics in a way that is far more flexible than keyword-based matching.Using project scope for retrieval: Instead of matching keywords, this approach suggests using vector embeddings and similarity search algorithms (e.g., cosine similarity) to find the team members whose profiles most closely align with the project scope.Filtering based on availability: Once the best-matched candidates are retrieved based on profile similarity, filtering them by availability ensures that the system provides a practically useful result. This method efficiently handles large-scale datasets by leveragingvector embeddingsandsimilarity searchtechniques, both of which are fundamental tools inGenerative AI engineeringfor handling unstructured text. Technical References: Vector embeddings: In this approach, the unstructured text (employee profiles and project scopes) is converted into high-dimensional vectors using pretrained models (e.g., BERT, Sentence-BERT, or custom embeddings). These embeddings capture the semantic meaning of the text, making it easier to perform similarity-based retrieval.Vector stores: Solutions likeFAISSorMilvusallow storing and retrieving large numbers of vector embeddings quickly. This is critical when working with large teams where querying through individual profiles sequentially would be inefficient.LLM Integration: Large language models can assist in generating embeddings for both employee profiles and project scopes. They can also assist in fine-tuning similarity measures, ensuring that the retrieval system captures the nuances of the text data.Filtering: After retrieving the most similar profiles based on the project scope, filtering based on availability ensures that only team members who are free for the project are considered.
This system is scalable, efficient, and makes use of the latest techniques inGenerative AI, such as vector embeddings and semantic search. The two main inputs are theemployee profilesandproject scopes, both of which are unstructured. This means traditional rule-based systems (e.g., simple keyword matching) would be inefficient, especially when working with large datasets.

Question # 2

A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.Which approach will NOT improve the LLM’s response to achieve the desired response?

A. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style
B. Use a neutralizer to normalize the tone and style of the underlying documents
C. Include few-shot examples in the prompt to the LLM
D. Fine-tune the LLM on a dataset of desired tone and style

Question # 3

A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient’s question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor’s office and suggest a few relevant pre-approved medical articles for reading. If the patient’s question is urgent, direct the patient to calling their local emergency services.Given the following user input:“I have been experiencing severe headaches and dizziness for the past two days.”Which response is most appropriate for the chatbot to generate?

A. Here are a few relevant articles for your browsing. Let me know if you have questions after reading them.
B. Please call your local emergency services.
C. Headaches can be tough. Hope you feel better soon!
D. Please provide your age, recent activities, and any other symptoms you have noticed along with your headaches and dizziness.

Question # 4

Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?

A. The ability to generate responses in code
B. The similarity to the previous language
C. The latency of the response and the length of text generated
D. The accuracy and relevance of the responses

Question # 5

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?

A. Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.
B. Increase the compute to improve processing speed of questions to allow greater relevancy analysisC Implement a profanity filter to screen out offensive language
C. Incorporate manual reviews to correct any problematic outputs prior to sending to the users

Question # 6

A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?

A. DatabrickslQ
B. Foundation Model APIs
C. Feature Serving
D. AutoML

Question # 7

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?

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

Answer: B
Explanation: 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.

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:

Question # 8

A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they’re willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.Which model meets all the Generative Al Engineer’s needs in this situation?

A. Dolly 1.5B
B. OpenAI GPT-4
C. BGE-large
D. Llama2-70B

Question # 9

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?

A. flask
B. beautifulsoup
C. unstructured
D. numpy

PDF Only

$80.00 Free Updates Upto 90 Days

PDF + Test Engine

$160.00 Free Updates Upto 90 Days

Test Engine

$120.00 Free Updates Upto 90 Days

How to pass Databricks Databricks-Generative-AI-Engineer-Associate exam with the help of dumps?

How Do I Know Databricks Databricks-Generative-AI-Engineer-Associate Dumps are Worth it?

IT Students Are Using our Databricks Certified Generative AI Engineer Associate Dumps Worldwide!

How to Get Databricks-Generative-AI-Engineer-Associate Real Exam Dumps?

Frequently Asked Questions

Question # 1

Question # 2

Question # 3

Question # 4

Question # 5

Question # 6

Question # 7

Question # 8

Question # 9

Related Exams

Databricks Databricks-Certified-Professional-Data-Scientist Dumps

Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Dumps

Databricks Databricks-Certified-Data-Engineer-Associate Dumps

Databricks Databricks-Machine-Learning-Associate Dumps

Databricks Databricks-Machine-Learning-Professional Dumps

Databricks Databricks-Certified-Professional-Data-Engineer Dumps

Databricks Databricks-Certified-Data-Analyst-Associate Dumps

Databricks Generative AI Engineer Dumps

What our clients say about Databricks-Generative-AI-Engineer-Associate Certification Prep Material

Leave a comment

Links

Our Contacts