Sam Walker Sam Walker's Profile Page

Sam Walker Sam Walker

0 Course Enrolled • 0 Course Completed

Biography

Confused About Where to Start Your Databricks Databricks-Generative-AI-Engineer-Associate Exam Preparation? Here's What You Need to Know

Databricks-Generative-AI-Engineer-Associate study material applies to all types of candidates. Buying a set of learning materials is not difficult, but it is difficult to buy one that is suitable for you. For example, some learning materials can really help students get high scores, but they usually require users to have a lot of study time, which is difficult for office workers. However, Databricks-Generative-AI-Engineer-Associate Study Material is to help students improve their test scores by improving their learning efficiency. Therefore, users can pass exams with very little learning time.

The TorrentVCE is a reliable and trusted platform that is committed to making the Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam preparation instant, simple and successful. To do this the TorrentVCE is offering top-rated and real Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam questions with high-in-demand features. These features are inclusively designed to ace the Databricks Databricks-Generative-AI-Engineer-Associate exam preparation.

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

100% Pass Quiz Databricks - Databricks-Generative-AI-Engineer-Associate - Accurate Databricks Certified Generative AI Engineer Associate Reliable Test Review

We can assure you that you will get the latest version of our Databricks-Generative-AI-Engineer-Associate training materials for free from our company in the whole year after payment. For we promise to give all of our customers one year free updates of our Databricks-Generative-AI-Engineer-Associate exam questions and we update our Databricks-Generative-AI-Engineer-Associate Study Guide fast and constantly. Do not miss the opportunity to buy the best Databricks-Generative-AI-Engineer-Associate preparation questions in the international market which will also help you to advance with the times.

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

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.

Topic 3

Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
licensing requirements in this topic.

Topic 4

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 5

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.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

A. Pass the secrets in plain text
B. Add credentials using environment variables
C. Pass variables using the Databricks Feature Store API
D. Use spark.conf.set ()

Answer: B

Explanation:
Context: Deploying an application that uses an MLflow Pyfunc model involves managing sensitive information such as secrets and credentials securely.
Explanation of Options:
* Option A: Use spark.conf.set(): While this method can pass configurations within Spark jobs, using it for secrets is not recommended because it may expose them in logs or Spark UI.
* Option B: Pass variables using the Databricks Feature Store API: The Feature Store API is designed for managing features for machine learning, not for handling secrets or credentials.
* Option C: Add credentials using environment variables: This is a common practice for managing credentials in a secure manner, as environment variables can be accessed securely by applications without exposing them in the codebase.
* Option D: Pass the secrets in plain text: This is highly insecure and not recommended, as it exposes sensitive information directly in the code.
Therefore,Option Cis the best method for securely passing secrets and credentials to an application, protecting them from exposure.

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?

A. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
B. 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.
C. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
D. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.

Answer: B

Explanation:
* Problem Context: After receiving positive feedback for the RAG application prototype, the next step is to formally evaluate the system to pinpoint areas for improvement.
* Explanation of Options:
* Option A: While cosine similarity scores are useful, they primarily measure similarity rather than the overall performance of an RAG system.
* Option B: This option provides a systematic approach to evaluation by testing both retrieval and generation components separately. This allows for targeted improvements and a clear understanding of each component's performance, using MLflow's metrics for a structured and standardized assessment.
* Option C: Benchmarking multiple LLMs does not focus on evaluating the existing system's components but rather on comparing different models.
* Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.
OptionBis the most comprehensive and structured approach, facilitating precise evaluations and improvements on specific components of the RAG system.

NEW QUESTION # 13
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?

A. Response-generating LLM
B. Embedding model
C. Vector database
D. Invite users to submit long, rather than concise, questions

Answer: D

Explanation:
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 # 14
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?

A. Use the largest LLM possible because that gives the best performance for any general queries
B. Limit the number of relevant documents available for the RAG application to retrieve from
C. Limit the number of queries a customer can send per day
D. Pick a smaller LLM that is domain-specific

Answer: D

Explanation:
For a small, cost-conscious startup in the cancer research field, choosing a domain-specific and smaller LLM is the most effective strategy. Here's whyBis the best choice:
* Domain-specific performance: A smaller LLM that has been fine-tuned for the domain of cancer research will outperform a general-purpose LLM for specialized queries. This ensures high-quality responses without needing to rely on a large, expensive LLM.
* Cost-efficiency: Smaller models are cheaper to run, both in terms of compute resources and API usage costs. A domain-specific smaller LLM can deliver good quality responses without the need for the extensive computational power required by larger models.
* Focused knowledge: In a specialized field like cancer research, having an LLM tailored to the subject matter provides better relevance and accuracy for queries, while keeping costs low.Large, general- purpose LLMs may provide irrelevant information, leading to inefficiency and higher costs.
This approach allows the startup to balance quality, cost, and customer satisfaction effectively, making it the most suitable strategy.

NEW QUESTION # 15
A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?

A. Switch to using External Models instead
B. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues
C. Deploy the model using pay-per-token throughput as it comes with cost guarantees
D. Throttle the incoming batch of requests manually to avoid rate limiting issues

Answer: C

Explanation:
* Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
* Explanation of Options:
* Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
* Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
* Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
* Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.

NEW QUESTION # 16
......

All the TorrentVCE Databricks Databricks-Generative-AI-Engineer-Associate practice questions are real and based on actual Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam topics. The web-based Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) practice test is compatible with all operating systems like Mac, IOS, Android, and Windows. Because of its browser-based Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) practice exam, it requires no installation to proceed further. Similarly, Chrome, IE, Firefox, Opera, Safari, and all the major browsers support the Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) practice test.

Databricks-Generative-AI-Engineer-Associate Interactive EBook: https://www.torrentvce.com/Databricks-Generative-AI-Engineer-Associate-valid-vce-collection.html