Eduqualify | Quality education awaits you here.

Hugh Miller Hugh Miller

0 Course Enrolled • 0 Course Completed

Biography

Latest Databricks-Generative-AI-Engineer-Associate Test Online & Databricks-Generative-AI-Engineer-Associate Free Dumps

Reliable Databricks-Generative-AI-Engineer-Associate Databricks-Generative-AI-Engineer-Associate exam questions pdf, exam questions answers and latest test book can help customer success in their field. Databricks offers 365 days updates. Customers can download Latest Databricks-Generative-AI-Engineer-Associate Exam Questions pdf and exam book. And Databricks Certified Generative AI Engineer Associate Databricks-Generative-AI-Engineer-Associatefee is affordable. It is now time to begin your preparation by downloading the free demo of Databricks Certified Generative AI Engineer Associate Databricks-Generative-AI-Engineer-Associate Exam Dumps.

All praise and high values lead us to higher standard of Databricks-Generative-AI-Engineer-Associate practice engine. So our work ethic is strongly emphasized on your interests which profess high regard for interests of exam candidates. Our Databricks-Generative-AI-Engineer-Associate study materials capture the essence of professional knowledge and lead you to desirable results effortlessly. So let us continue with our reference to advantages of our Databricks-Generative-AI-Engineer-Associate learning questions.

>> Latest Databricks-Generative-AI-Engineer-Associate Test Online <<

Databricks-Generative-AI-Engineer-Associate Exam Torrent & Databricks-Generative-AI-Engineer-Associate Test Collection & Databricks-Generative-AI-Engineer-Associate Top Quiz

Our Databricks-Generative-AI-Engineer-Associate exam questions are specified as one of the most successful training materials in the line. And our Databricks-Generative-AI-Engineer-Associate study guide can renew your knowledge with high utility with favorable prices. Form time to time, we will give some attractive discounts on our Databricks-Generative-AI-Engineer-Associate learning quiz as well. So, our Databricks-Generative-AI-Engineer-Associate actual exam is reliably rewarding with high utility value.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q59-Q64):

NEW QUESTION # 59
A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

A. Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint
B. Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint
C. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container
D. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn

Answer: A

NEW QUESTION # 60
A Generative Al Engineer is helping a cinema extend its website's chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?

A. implementation. Write the Delta table contents to a text column.then embed those texts using an embedding model and store these in the vector index Look up the information based on the embedding as part of the agent logic / tool implementation.
B. Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation.
C. Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation.
D. Query the Delta table directly via a SQL query constructed from the user's input using a text-to-SQL LLM in the agent logic / tool

Answer: B

Explanation:
The task is to extend a cinema chatbot to provide movie showtime information using a RAG application, leveraging user location and a continuously updated Delta table, with minimal effort and high performance.
Let's evaluate the options.
* Option A: Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation
* Databricks Feature Serving provides low-latency access to real-time data from Delta tables via an online store. Syncing the Delta table to a Feature Serving Endpoint allows the chatbot to query showtimes efficiently, integrating seamlessly into the RAG agent'stool logic. This leverages Databricks' native infrastructure, minimizing effort and ensuring performance.
* Databricks Reference:"Feature Serving Endpoints provide real-time access to Delta table data with low latency, ideal for production systems"("Databricks Feature Engineering Guide," 2023).
* Option B: Query the Delta table directly via a SQL query constructed from the user's input using a text-to-SQL LLM in the agent logic / tool
* Using a text-to-SQL LLM to generate queries adds complexity (e.g., ensuring accurate SQL generation) and latency (LLM inference + SQL execution). While feasible, it's less performant and requires more effort than a pre-built serving solution.
* Databricks Reference:"Direct SQL queries are flexible but may introduce overhead in real-time applications"("Building LLM Applications with Databricks").
* Option C: Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation
* Converting structured Delta table data (e.g., showtimes) into text, embedding it, and using vector search is inefficient for structured lookups. It's effort-intensive (preprocessing, embedding) and less precise than direct queries, undermining performance.
* Databricks Reference:"Vector search excels for unstructured data, not structured tabular lookups"("Databricks Vector Search Documentation").
* Option D: Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation
* Exporting to an external database (e.g., MySQL) adds setup effort (workflow, external DB management) and latency (periodic updates vs. real-time). It's less performant and more complex than using Databricks' native tools.
* Databricks Reference:"Avoid external systems when Delta tables provide real-time data natively"("Databricks Workflows Guide").
Conclusion: Option A minimizes effort by using Databricks Feature Serving for real-time, low-latency access to the Delta table, ensuring high performance in a production-ready RAG chatbot.

NEW QUESTION # 61
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?

A. Implement caching for frequently asked questions
B. Use a different semantic similarity search algorithm
C. Assess the quality of the retrieved context
D. Use a different LLM to improve the generated response

Answer: C

Explanation:
In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here's why option A is the most appropriate solution:
* Context Relevance:The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.
* Vector Search and Embedding Similarity:RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing the semantic similarity searchprocess ensures that the closest matches are actually relevant to the query.
* Fine-tuning the Retrieval Process:By improving theretrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.
* Why Other Options Are Less Suitable:
* B (Caching FAQs): Caching can speed up responses for frequently asked questions but won't improve the relevance of the retrieved content for less frequent or new queries.
* C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.
* D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.
Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.

NEW QUESTION # 62
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. numpy
B. beautifulsoup
C. flask
D. unstructured

Answer: D

Explanation:
* Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.
* Explanation of Options:
* Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from PDFs.
* Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.
* Option C: unstructured: This Python package is specifically designed to work with unstructured data, including extracting text from PDFs. It provides functionalities to handle various types of content in documents with minimal coding, making it ideal for the task.
* Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.
Given the requirement,Option C(unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.

NEW QUESTION # 63
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. Fine-tune the model on your data, hoping it will learn what is appropriate and not
B. Use a strong system prompt to ensure the model aligns with your needs.
C. Add guardrails to filter outputs from the LLM before it is shown to the user
D. Limit the data available based on the user's access level

Answer: A

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 # 64
......

There are a lot of experts and professors in or company in the field. In order to meet the demands of all people, these excellent experts and professors from our company have been working day and night. They tried their best to design the best Databricks-Generative-AI-Engineer-Associate study materials from our company for all people. By our study materials, all people can prepare for their Databricks-Generative-AI-Engineer-Associate exam in the more efficient method. We can guarantee that our study materials will be suitable for all people and meet the demands of all people, including students, workers and housewives and so on. If you decide to buy and use the Databricks-Generative-AI-Engineer-Associate Study Materials from our company with dedication on and enthusiasm step and step, it will be very easy for you to pass the exam without doubt. We sincerely hope that you can achieve your dream in the near future by the Databricks-Generative-AI-Engineer-Associate study materials of our company.

Databricks-Generative-AI-Engineer-Associate Free Dumps: https://www.newpassleader.com/Databricks/Databricks-Generative-AI-Engineer-Associate-exam-preparation-materials.html

Databricks Latest Databricks-Generative-AI-Engineer-Associate Test Online Some choices are so important that you cannot treat them casually, Databricks Latest Databricks-Generative-AI-Engineer-Associate Test Online We provide 24*7 online service support: pre-sale and after-sale, Databricks Latest Databricks-Generative-AI-Engineer-Associate Test Online To know why we said that, you can look what we mentioned as follows, Databricks Latest Databricks-Generative-AI-Engineer-Associate Test Online Besides, we offer considerate aftersales services 24/7 and our staff treasures all your constructive opinions to be better of our services in all respects, The efficiency of our Databricks-Generative-AI-Engineer-Associate study materials can be described in different aspects.

But this isn't the only option, So—where to put the antenna, Some Databricks-Generative-AI-Engineer-Associate Real Brain Dumps choices are so important that you cannot treat them casually, We provide 24*7 online service support: pre-sale and after-sale.

Prepare With Databricks Databricks-Generative-AI-Engineer-Associate Exam Questions [2025] A Genuine Information For You

To know why we said that, you can look what we mentioned as follows, Besides, Databricks-Generative-AI-Engineer-Associate we offer considerate aftersales services 24/7 and our staff treasures all your constructive opinions to be better of our services in all respects.

The efficiency of our Databricks-Generative-AI-Engineer-Associate study materials can be described in different aspects.