Jack Martin Jack Martin's Profile Page

Jack Martin Jack Martin

0 Course Enrolled • 0 Course Completed

Biography

New Databricks-Generative-AI-Engineer-Associate Braindumps Questions - Databricks-Generative-AI-Engineer-Associate High Quality

These formats are Databricks Databricks-Generative-AI-Engineer-Associate PDF dumps, web-based practice test software, and desktop practice test software. All these three Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam questions contain the real, valid, and updated Databricks Exams that will provide you with everything that you need to learn, prepare and pass the challenging but career advancement Databricks-Generative-AI-Engineer-Associate Certification Exam with good scores.

The Databricks-Generative-AI-Engineer-Associate certificate enjoys a high reputation among the labor market circle and is widely recognized as the proof of excellent talents and if you are one of them and you want to pass the test smoothly you can choose our Databricks-Generative-AI-Engineer-Associate practice questions. Our Databricks-Generative-AI-Engineer-Associate Study Materials concentrate the essence of exam materials and seize the focus information to let the learners master the key points. You will pass the exam for sure if you choose our Databricks-Generative-AI-Engineer-Associate exam braindumps.

>> New Databricks-Generative-AI-Engineer-Associate Braindumps Questions <<

100% Pass Databricks Databricks-Generative-AI-Engineer-Associate - First-grade New Databricks Certified Generative AI Engineer Associate Braindumps Questions

How can you quickly change your present situation and be competent for the new life, for jobs, in particular? The answer is using Databricks-Generative-AI-Engineer-Associate practice materials. From my perspective, our free demo is possessed with high quality which is second to none. This is no exaggeration at all. Just as what have been reflected in the statistics, the pass rate for those who have chosen our Databricks-Generative-AI-Engineer-Associate Exam Guide is as high as 99%, which in turn serves as the proof for the high quality of our Databricks-Generative-AI-Engineer-Associate study engine.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q13-Q18):

NEW QUESTION # 13
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. Add guardrails to filter outputs from the LLM before it is shown to the user
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: 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 # 14
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. BGE-large
B. Dolly 1.5B
C. Llama2-70B
D. OpenAI GPT-4

Answer: A

Explanation:
Problem Context: The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns. The key factors areconfidentialityandsensitivityof the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.
Explanation of Options:
* Option A: Dolly 1.5B: This model does not typically support RAG applications as it's more focused on image generation tasks.
* Option B: OpenAI GPT-4: While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.
* Option C: BGE-large: The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements.
Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project's sensitivity and confidentiality needs.
* Option D: Llama2-70B: Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.
Given the sensitivity and confidentiality concerns,BGE-largeis assumed to be configurable for secure internal use, making it the optimal choice for this scenario.

NEW QUESTION # 15
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?

A. context length 512: smallest model is 0.13GB and embedding dimension 384
B. context length 514; smallest model is 0.44GB and embedding dimension 768
C. context length 32768: smallest model is 14GB and embedding dimension 4096
D. context length 2048: smallest model is 11GB and embedding dimension 2560

Answer: A

Explanation:
When prioritizing cost and latency over quality in a Large Language Model (LLM)-based application, it is crucial to select a configuration that minimizes both computational resources and latency while still providing reasonable performance. Here's whyDis the best choice:
* Context length: The context length of 512 tokens aligns with the chunk size used for the documents (maximum of 512 tokens per chunk). This is sufficient for capturing the needed information and generating responses without unnecessary overhead.
* Smallest model size: The model with a size of 0.13GB is significantly smaller than the other options.
This small footprint ensures faster inference times and lower memory usage, which directly reduces both latency and cost.
* Embedding dimension: While the embedding dimension of 384 is smaller than the other options, it is still adequate for tasks where cost and speed are more important than precision and depth of understanding.
This setup achieves the desired balance between cost-efficiency and reasonable performance in a latency- sensitive, cost-conscious application.

NEW QUESTION # 16
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. 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
B. 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.
C. 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.
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.

Answer: C

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

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
......

As we all know, Databricks-Generative-AI-Engineer-Associate certification is of great significance to highlight your resume, thus helping you achieve success in your workplace. So with our Databricks-Generative-AI-Engineer-Associate preparation materials, you are able to pass the exam more easily in the most efficient and productive way and learn how to study with dedication and enthusiasm, which can be a valuable asset in your whole life. There are so many advantages of our Databricks-Generative-AI-Engineer-Associate Guide dumps which will let you interested and satisfied.

Databricks-Generative-AI-Engineer-Associate High Quality: https://www.dumpleader.com/Databricks-Generative-AI-Engineer-Associate_exam.html

Although the three major versions of our Databricks-Generative-AI-Engineer-Associate exam dumps provide a demo of the same content for all customers, they will meet different unique requirements from a variety of users based on specific functionality, Databricks New Databricks-Generative-AI-Engineer-Associate Braindumps Questions Many people are worried that the installation process will cost a lot of time, Based on the past experience our users prepare for exam with our Databricks-Generative-AI-Engineer-Associate VCE dumps, the average time spending on our products may be 15-40 hours so that you have no need to do much useless efforts.

Boolean Data Type, This article walks you through the process and includes installing an app and unrooting it, Although the three major versions of our Databricks-Generative-AI-Engineer-Associate exam dumps provide a demo of the same content for all customers, Databricks-Generative-AI-Engineer-Associate High Quality they will meet different unique requirements from a variety of users based on specific functionality.

Pass Guaranteed Quiz Databricks - Databricks-Generative-AI-Engineer-Associate - Databricks Certified Generative AI Engineer Associate –Professional New Braindumps Questions

Many people are worried that the installation process Databricks-Generative-AI-Engineer-Associate will cost a lot of time, Based on the past experience our users prepare for exam with our Databricks-Generative-AI-Engineer-Associate VCE dumps, the average time spending on our products may be 15-40 hours so that you have no need to do much useless efforts.

Databricks-Generative-AI-Engineer-Associate dumps demo is the free resource in our website, which has attracted lots of candidates, And we will send the newest Databricks Databricks-Generative-AI-Engineer-Associate valid materials to you by e-mail at once.

Jack Martin Jack Martin

Biography

Join Our Community