100% Pass 2025 Databricks Reliable Databricks-Generative-AI-Engineer-Associate: Reliable Databricks Certified Generative AI Engineer Associate Test Book

Our Databricks-Generative-AI-Engineer-Associate test prep attaches great importance to a skilled, trained and motivated workforce as well as the company’s overall performance. Adhere to new and highly qualified Databricks-Generative-AI-Engineer-Associate quiz guide to meet the needs of customer, we are also committed to providing the first -class after-sale service. There will be our customer service agents available 24/7 for your supports; any request for further assistance or information about Databricks-Generative-AI-Engineer-Associate Exam Torrent will receive our immediate attention.

Choosing valid Databricks dumps means closer to success. Before you buy our products, you can download the free demo of Databricks-Generative-AI-Engineer-Associate test questions to check the accuracy of our dumps. Besides, there are 24/7 customer assisting to support you in case you may have any questions about Databricks-Generative-AI-Engineer-Associate Dumps PDF or download link.

>> Reliable Databricks-Generative-AI-Engineer-Associate Test Book <<

Valid Databricks-Generative-AI-Engineer-Associate Test Materials & Databricks-Generative-AI-Engineer-Associate Online Training Materials

Our society is in the jumping constantly changes and development. So we need to face the more live pressure to handle much different things and face more intense competition. The essential method to solve these problems is to have the faster growing speed than society developing. In a field, you can try to get the Databricks-Generative-AI-Engineer-Associate Certification to improve yourself, for better you and the better future. With it, you are acknowledged in your profession. The Databricks-Generative-AI-Engineer-Associate exam torrent can prove your ability to let more big company to attention you. Then you have more choice to get a better job and going to suitable workplace.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q17-Q22):

NEW QUESTION # 17
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?

A. DistilBERT
B. DBRX
C. Llama2-70B
D. MPT-30B

Answer: C

Explanation:
* Problem Context: The engineer needs an open-source LLM with a large context window to develop an application.
* Explanation of Options:
* Option A: DistilBERT: While an efficient and smaller version of BERT, DistilBERT does not provide a particularly large context window.
* Option B: MPT-30B: This model, while large, is not specified as being particularly notable for its context window capabilities.
* Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a large context window. It is also available as an open-source model, making it ideal for applications requiring extensive contextual understanding.
* Option D: DBRX: This is not a recognized standard model in the context of large language models with extensive context windows.
Thus,Option C(Llama2-70B) is the best fit as it meets the criteria of having a large context window and being available for open-source use, suitable for developing robust language understanding applications.

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?

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

Answer: C

Explanation:
In the context of ensuring that outputs are relevant to financial news, increasing compute power (option B) does not directly improve therelevanceof the LLM-generated outputs. Here's why:
* Compute Power and Relevancy:Increasing compute power can help the model process inputs faster, but it does not inherentlyimprove therelevanceof the answers. Relevancy depends on the data sources, the retrieval method, and the filtering mechanisms in place, not on how quickly the model processes the query.
* What Actually Helps with Relevance:Other methods, like content filtering, guardrails, or manual review, can directly impact the relevance of the model's responses by ensuring the model focuses on pertinent financial content. These methods help tailor the LLM's responses to the financial domain and avoid irrelevant or harmful outputs.
* Why Other Options Are More Relevant:
* A (Comprehensive Guardrail Framework): This will ensure that the model avoids generating content that is irrelevant or inappropriate in the finance sector.
* C (Profanity Filter): While not directly related to financial relevancy, ensuring the output is clean and professional is still important in maintaining the quality of responses.
* D (Manual Review): Incorporating human oversight to catch and correct issues with the LLM's output ensures the final answers are aligned with financial content expectations.
Thus, increasing compute power does not help with ensuring the outputs are more relevant to financial news, making option B the correct answer.

NEW QUESTION # 19
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. 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.
B. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.
C. 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.
D. 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.

Answer: C

Explanation:
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 # 20
A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

A. vsc.get_index()
B. vsc.create_delta_sync_index()
C. vsc.create_direct_access_index()
D. vsc.similarity_search()

Answer: B

Explanation:
Context: The Generative AI Engineer is setting up a vector store using Databricks' VectorSearchClient. This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.
Explanation of Options:
* Option A: vsc.get_index(): This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.
* Option B: vsc.create_delta_sync_index(): After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.
* Option C: vsc.create_direct_access_index(): This function would create an index that directly accesses the data without synchronization. While also a valid approach, it's less likely to be the next logical step if the default setup (typically accommodating changes) is intended.
* Option D: vsc.similarity_search(): This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.
Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, henceOption B.

NEW QUESTION # 21
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. Use a strong system prompt to ensure the model aligns with your needs.
D. Limit the data available based on the user's access level

Answer: B

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.

NEW QUESTION # 22
......

Prepared by experts and approved by experienced professionals, our Databricks-Generative-AI-Engineer-Associate exam torrent is well-designed high quality products and they are revised and updated based on changes in syllabus and the latest developments in theory and practice. With the guidance of our Databricks-Generative-AI-Engineer-Associate Guide Torrent, you can make progress by a variety of self-learning and self-assessing features to test learning outcomes. And as the high pass rate of our Databricks-Generative-AI-Engineer-Associate exam questions is 99% to 100%, you will be bound to pass the Databricks-Generative-AI-Engineer-Associate exam with ease.

Valid Databricks-Generative-AI-Engineer-Associate Test Materials: https://www.examstorrent.com/Databricks-Generative-AI-Engineer-Associate-exam-dumps-torrent.html

We also have strict requirements for the questions and answers of Databricks-Generative-AI-Engineer-Associate exam materials, we ensure you that the Databricks-Generative-AI-Engineer-Associate training materials are most useful tool, which can help you pass the exam just one time, Databricks Reliable Databricks-Generative-AI-Engineer-Associate Test Book Not only these practice tests will increase your preparation level, but they will also increase your confidence when you are attempting a real exam, The use of Databricks-Generative-AI-Engineer-Associate test preparation exam questions helps them to practice thoroughly.

Future titles will focus on specific policy areas such as Databricks-Generative-AI-Engineer-Associate Online Training Materials housing and medical research, The disadvantage is that you have to manage both drawing and strokes simultaneously.

We also have strict requirements for the questions and answers of Databricks-Generative-AI-Engineer-Associate Exam Materials, we ensure you that the Databricks-Generative-AI-Engineer-Associate training materials are most useful tool, which can help you pass the exam just one time.

Realistic Reliable Databricks-Generative-AI-Engineer-Associate Test Book - Valid Databricks Certified Generative AI Engineer Associate Test Materials Free PDF Quiz

Not only these practice tests will increase your preparation level, but they will also increase your confidence when you are attempting a real exam, The use of Databricks-Generative-AI-Engineer-Associate test preparation exam questions helps them to practice thoroughly.

In this way, you can enjoy great benefit by just Databricks-Generative-AI-Engineer-Associate paying a little attention to our Generative AI Engineer Databricks Certified Generative AI Engineer Associate valid training test, Besides, we offer you free demo for Databricks-Generative-AI-Engineer-Associate exam materials for you to have a try, so that you can know the mode of the complete version.

Ray Hill Ray Hill

Biography

100% Pass 2025 Databricks Reliable Databricks-Generative-AI-Engineer-Associate: Reliable Databricks Certified Generative AI Engineer Associate Test Book

Valid Databricks-Generative-AI-Engineer-Associate Test Materials & Databricks-Generative-AI-Engineer-Associate Online Training Materials

Databricks Certified Generative AI Engineer Associate Sample Questions (Q17-Q22):

Realistic Reliable Databricks-Generative-AI-Engineer-Associate Test Book - Valid Databricks Certified Generative AI Engineer Associate Test Materials Free PDF Quiz

Quick Links

Resources

Support