DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE TEST COLLECTION, TEST DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE VCE FREE

Databricks-Generative-AI-Engineer-Associate Test Collection, Test Databricks-Generative-AI-Engineer-Associate Vce Free

Databricks-Generative-AI-Engineer-Associate Test Collection, Test Databricks-Generative-AI-Engineer-Associate Vce Free

Blog Article

Tags: Databricks-Generative-AI-Engineer-Associate Test Collection, Test Databricks-Generative-AI-Engineer-Associate Vce Free, Databricks-Generative-AI-Engineer-Associate Mock Exam, New Databricks-Generative-AI-Engineer-Associate Test Review, Databricks-Generative-AI-Engineer-Associate Test Dump

If you want to achieve maximum results with minimum effort in a short period of time, and want to pass the Databricks Databricks-Generative-AI-Engineer-Associate Exam. You can use Lead1Pass's Databricks Databricks-Generative-AI-Engineer-Associate exam training materials. The training materials of Lead1Pass are the product that through the test of practice. Many candidates proved it does 100% pass the exam. With it, you will reach your goal, and can get the best results.

Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
Topic 2
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.
Topic 3
  • 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.

>> Databricks-Generative-AI-Engineer-Associate Test Collection <<

The Best Databricks-Generative-AI-Engineer-Associate Test Collection | Databricks-Generative-AI-Engineer-Associate 100% Free Test Vce Free

Some of our customers are white-collar workers with no time to waste, and need a Databricks certification urgently to get their promotions, meanwhile the other customers might aim at improving their skills. Our reliable Databricks-Generative-AI-Engineer-Associate question dumps are developed by our experts who have rich experience in the fields. Constant updating of the Databricks-Generative-AI-Engineer-Associate Prep Guide keeps the high accuracy of exam questions thus will help you get use the Databricks-Generative-AI-Engineer-Associate exam quickly. During the exam, you would be familiar with the questions, which you have practiced in our Databricks-Generative-AI-Engineer-Associate question dumps. That’s the reason why most of our customers always pass exam easily.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q39-Q44):

NEW QUESTION # 39
Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?

  • A. Chat loaders
  • B. Vector Stores
  • C. (Q)
  • D. React Components
  • E. Conversation Buffer Memory
  • F. External tools

Answer: B,E

Explanation:
Building a basic LLM-enabled chat application with conversational capabilities, knowledge retrieval, and contextual memory requires specific components that work together to process queries, maintain context, and retrieve relevant information. Databricks' Generative AI Engineer documentation outlines key components for such systems, particularly in the context of frameworks like LangChain or Databricks' MosaicML integrations. Let's evaluate the required components:
* Understanding the Requirements:
* Conversational capabilities: The app must generate natural, coherent responses.
* Knowledge retrieval: It must access external or domain-specific knowledge.
* Contextual memory: It must remember prior interactions in the conversation.
* Databricks Reference:"A typical LLM chat application includes a memory component to track conversation history and a retrieval mechanism to incorporate external knowledge"("Databricks Generative AI Cookbook," 2023).
* Evaluating the Options:
* A. (Q): This appears incomplete or unclear (possibly a typo). Without further context, it's not a valid component.
* B. Vector Stores: These store embeddings of documents or knowledge bases, enabling semantic search and retrieval of relevant information for the LLM. This is critical for knowledge retrieval in a chat application.
* Databricks Reference:"Vector stores, such as those integrated with Databricks' Lakehouse, enable efficient retrieval of contextual data for LLMs"("Building LLM Applications with Databricks").
* C. Conversation Buffer Memory: This component stores the conversation history, allowing the LLM to maintain context across multiple turns. It's essential for contextual memory.
* Databricks Reference:"Conversation Buffer Memory tracks prior user inputs and LLM outputs, ensuring context-aware responses"("Generative AI Engineer Guide").
* D. External tools: These (e.g., APIs or calculators) enhance functionality but aren't required for a basicchat app with the specified capabilities.
* E. Chat loaders: These might refer to data loaders for chat logs, but they're not a core chain component for conversational functionality or memory.
* F. React Components: These relate to front-end UI development, not the LLM chain's backend functionality.
* Selecting the Two Required Components:
* Forknowledge retrieval, Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks' RAG-based chat systems.
* Forcontextual memory, Conversation Buffer Memory (C) is required to maintain conversation history, ensuring coherent and context-aware responses.
* While an LLM itself is implied as the core generator, the question asks for chain components beyond the model, making B and C the minimal yet sufficient pair for a basic application.
Conclusion: The two required chain components areB. Vector StoresandC. Conversation Buffer Memory, as they directly address knowledge retrieval and contextual memory, respectively, aligning with Databricks' documented best practices for LLM-enabled chat applications.


NEW QUESTION # 40
A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?

  • A. MPT-7b
  • B. BGE-large
  • C. Llama2-70b
  • D. CodeLlama-34B

Answer: D

Explanation:
For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here's the reasoning:
* Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks.
This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.
* Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.
* Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.
* Why Other Options Are Less Suitable:
* A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine- tuned for code generation as CodeLlama.
* B (BGE-large): This model may not specifically focus on code generation.
* C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.
Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.


NEW QUESTION # 41
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. Add credentials using environment variables
  • B. Use spark.conf.set ()
  • C. Pass variables using the Databricks Feature Store API
  • D. Pass the secrets in plain text

Answer: A

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 # 42
A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?

  • A. There should be two different chatbots handling different types of user queries.
  • B. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it's an upcoming event question, send the query to a text-to-SQL model. If it's about ticket purchasing, the customer should be redirected to a payment platform.
  • C. The chatbot should only look at previous event information
  • D. The chatbot should only process payments

Answer: B

Explanation:
* Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.
* Explanation of Options:
* Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.
* Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.
* Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.
* Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.
Option Coffers a comprehensive workflow that maximizes the chatbot's utility and responsiveness to different user needs, aligning perfectly with the business requirements.


NEW QUESTION # 43
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. MPT-30B
  • C. DBRX
  • D. Llama2-70B

Answer: D

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 # 44
......

Compared with other education platform on the market, Lead1Pass is more reliable and highly efficiently. It provide candidates who want to pass the Databricks-Generative-AI-Engineer-Associate exam with high pass rate Databricks-Generative-AI-Engineer-Associate study materials, all customers have passed the Databricks-Generative-AI-Engineer-Associate Exam in their first attempt. They all need 20-30 hours to learn on our website can pass the Databricks-Generative-AI-Engineer-Associate exam. It is really a high efficiently exam tool that can help you save much time and energy to do other things.

Test Databricks-Generative-AI-Engineer-Associate Vce Free: https://www.lead1pass.com/Databricks/Databricks-Generative-AI-Engineer-Associate-practice-exam-dumps.html

Report this page