EXAM DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE OBJECTIVES PDF & EXAMCOLLECTION DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE VCE

Exam Databricks-Generative-AI-Engineer-Associate Objectives Pdf & Examcollection Databricks-Generative-AI-Engineer-Associate Vce

Exam Databricks-Generative-AI-Engineer-Associate Objectives Pdf & Examcollection Databricks-Generative-AI-Engineer-Associate Vce

Blog Article

Tags: Exam Databricks-Generative-AI-Engineer-Associate Objectives Pdf, Examcollection Databricks-Generative-AI-Engineer-Associate Vce, Databricks-Generative-AI-Engineer-Associate Exam Simulations, Databricks-Generative-AI-Engineer-Associate Reliable Braindumps Book, Databricks-Generative-AI-Engineer-Associate Latest Test Simulator

After taking a bird's eye view of applicants' issues, TrainingDump has decided to provide them with the real Databricks-Generative-AI-Engineer-Associate Questions. These Databricks-Generative-AI-Engineer-Associate dumps pdf is according to the new and updated syllabus so they can prepare for Databricks-Generative-AI-Engineer-Associate certification anywhere, anytime, with ease. A team of professionals has made the product of TrainingDump after much hard work with their complete potential so the candidates can prepare for Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) practice test in a short time.

Computers are changing our life day by day. We can do many things on computers. Technology changes the world. If you have dream to be a different people, obtaining a Databricks certification will be the first step. Databricks-Generative-AI-Engineer-Associate learning materials will be useful for you. As you can see the Forbes World's Billionaires List shows people starting bare-handed are mostly engaging in IT field. Databricks-Generative-AI-Engineer-Associate Learning Materials may be the first step to help you a different road to success.

>> Exam Databricks-Generative-AI-Engineer-Associate Objectives Pdf <<

Examcollection Databricks-Generative-AI-Engineer-Associate Vce, Databricks-Generative-AI-Engineer-Associate Exam Simulations

In order to make all customers feel comfortable, our company will promise that we will offer the perfect and considerate service for all customers. If you buy the Databricks-Generative-AI-Engineer-Associate training files from our company, you will have the right to enjoy the perfect service. If you have any questions about the Databricks-Generative-AI-Engineer-Associate learning materials, do not hesitate and ask us in your anytime, we are glad to answer your questions and help you use our Databricks-Generative-AI-Engineer-Associate study questions well. We believe our perfect service will make you feel comfortable when you are preparing for your Databricks-Generative-AI-Engineer-Associate exam and you will pass the Databricks-Generative-AI-Engineer-Associate exam.

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

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.
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 (Q49-Q54):

NEW QUESTION # 49
A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?

  • A. L!ama-2-70b-chat-hf
  • B. DBRX
  • C. MPT-30B-lnstruct
  • D. whisper-large-v3 (1.6B)

Answer: D

Explanation:
The task requires an open generative AI model for a transcription (speech-to-text) task where speed is essential. Let's assess the options based on their suitability for transcription and performance characteristics, referencing Databricks' approach to model selection.
* Option A: Llama-2-70b-chat-hf
* Llama-2 is a text-based LLM optimized for chat and text generation, not speech-to-text. It lacks transcription capabilities.
* Databricks Reference:"Llama models are designed for natural language generation, not audio processing"("Databricks Model Catalog").
* Option B: MPT-30B-Instruct
* MPT-30B is another text-based LLM focused on instruction-following and text generation, not transcription. It's irrelevant for speech-to-text tasks.
* Databricks Reference: No specific mention, but MPT is categorized under text LLMs in Databricks' ecosystem, not audio models.
* Option C: DBRX
* DBRX, developed by Databricks, is a powerful text-based LLM for general-purpose generation.
It doesn't natively support speech-to-text and isn't optimized for transcription.
* Databricks Reference:"DBRX excels at text generation and reasoning tasks"("Introducing DBRX," 2023)-no mention of audio capabilities.
* Option D: whisper-large-v3 (1.6B)
* Whisper, developed by OpenAI, is an open-source model specifically designed for speech-to-text transcription. The "large-v3" variant (1.6 billion parameters) balances accuracy and efficiency, with optimizations for speed via quantization or deployment on GPUs-key for the application's requirements.
* Databricks Reference:"For audio transcription, models like Whisper are recommended for their speed and accuracy"("Generative AI Cookbook," 2023). Databricks supports Whisper integration in its MLflow or Lakehouse workflows.
Conclusion: OnlyD. whisper-large-v3is a speech-to-text model, making it the sole suitable choice. Its design prioritizes transcription, and its efficiency (e.g., via optimized inference) meets the speed requirement, aligning with Databricks' model deployment best practices.


NEW QUESTION # 50
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

  • A. Decrease the chunk size of embedded documents
  • B. Retrain the response generating model using ALiBi
  • C. Reduce the number of records retrieved from the vector database
  • D. Reduce the maximum output tokens of the new model
  • E. Use a smaller embedding model to generate

Answer: A,C

Explanation:
* Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options:
* Option A: Use a smaller embedding model to generate- This wouldn't necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model- This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents- This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.
* Option D: Reduce the number of records retrieved from the vector database- By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi- Retraining the model is contrary to the stipulation not to change the response generating model.
OptionsCandDare the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.


NEW QUESTION # 51
A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

  • A. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.
  • B. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
  • C. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
  • D. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
    Here's an example: {"date": "April 16, 2024", "sender_email": "[email protected]", "order_id":
    "RE987D"}

Answer: D

Explanation:
Problem Context: The goal is to parse emails to extract certain pieces of information and output this in a structured JSON format. Clarity and specificity in the prompt design will ensure higher accuracy in the LLM' s responses.
Explanation of Options:
* Option A: Provides a general guideline but lacks an example, which helps an LLM understand the exact format expected.
* Option B: Includes a clear instruction and a specific example of the output format. Providing an example is crucial as it helps set the pattern and format in which the information should be structured, leading to more accurate results.
* Option C: Does not specify that the output should be in JSON format, thus not meeting the requirement.
* Option D: While it correctly asks for JSON format, it lacks an example that would guide the LLM on how to structure the JSON correctly.
Therefore,Option Bis optimal as it not only specifies the required format but also illustrates it with an example, enhancing the likelihood of accurate extraction and formatting by the LLM.


NEW QUESTION # 52
A Generative Al Engineer is working with a retail company that wants to enhance its customer experience by automatically handling common customer inquiries. They are working on an LLM-powered Al solution that should improve response times while maintaining a personalized interaction. They want to define the appropriate input and LLM task to do this.
Which input/output pair will do this?

  • A. Input: Customer reviews; Output Group the reviews by users and aggregate per-user average rating, then respond
  • B. Input: Customer reviews: Output Classify review sentiment
  • C. Input: Customer service chat logs; Output Group the chat logs by users, followed by summarizing each user's interactions, then respond
  • D. Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary

Answer: D

Explanation:
The task described in the question involves enhancing customer experience by automatically handling common customer inquiries using an LLM-powered AI solution. This requires the system to process input data (customer inquiries) and generate personalized, relevant responses efficiently. Let's evaluate the options step-by-step in the context of Databricks Generative AI Engineer principles, which emphasize leveraging LLMs for tasks like question answering, summarization, and retrieval-augmented generation (RAG).
* Option A: Input: Customer reviews; Output: Group the reviews by users and aggregate per-user average rating, then respond
* This option focuses on analyzing customer reviews to compute average ratings per user. While this might be useful for sentiment analysis or user profiling, it does not directly address the goal of handling common customer inquiries or improving response times for personalized interactions. Customer reviews are typically feedback data, not real-time inquiries requiring immediate responses.
* Databricks Reference: Databricks documentation on LLMs (e.g., "Building LLM Applications with Databricks") emphasizes that LLMs excel at tasks like question answering and conversational responses, not just aggregation or statistical analysis of reviews.
* Option B: Input: Customer service chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions, then respond
* This option uses chat logs as input, which aligns with customer service scenarios. However, the output-grouping by users and summarizing interactions-focuses on user-specific summaries rather than directly addressing inquiries. While summarization is an LLM capability, this approach lacks the specificity of finding answers to common questions, which is central to the problem.
* Databricks Reference: Per Databricks' "Generative AI Cookbook," LLMs can summarize text, but for customer service, the emphasis is on retrieval and response generation (e.g., RAG workflows) rather than user interaction summaries alone.
* Option C: Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary
* This option uses chat logs (real customer inquiries) as input and tasks the LLM with identifying answers to similar questions, then providing a summarized response. This directly aligns with the goal of handling common inquiries efficiently while maintaining personalization (by referencing past interactions or similar cases). It leverages LLM capabilities like semantic search, retrieval, and response generation, which are core to Databricks' LLM workflows.
* Databricks Reference: From Databricks documentation ("Building LLM-Powered Applications," 2023), an exact extract states:"For customer support use cases, LLMs can be used to retrieve relevant answers from historical data like chat logs and generate concise, contextually appropriate responses."This matches Option C's approach of finding answers and summarizing them.
* Option D: Input: Customer reviews; Output: Classify review sentiment
* This option focuses on sentiment classification of reviews, which is a valid LLM task but unrelated to handling customer inquiries or improving response times in a conversational context.
It's more suited for feedback analysis than real-time customer service.
* Databricks Reference: Databricks' "Generative AI Engineer Guide" notes that sentiment analysis is a common LLM task, but it's not highlighted for real-time conversational applications like customer support.
Conclusion: Option C is the best fit because it uses relevant input (chat logs) and defines an LLM task (finding answers and summarizing) that meets the requirements of improving response times and maintaining personalized interaction. This aligns with Databricks' recommended practices for LLM-powered customer service solutions, such as retrieval-augmented generation (RAG) workflows.


NEW QUESTION # 53
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

  • A. Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.
  • B. Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.
  • C. Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.
  • D. Consolidate all SnoPen AI related documents into a single chunk in the vector database.

Answer: A

Explanation:
In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is byincluding a clear instruction in the system prompt(option C).
* System Prompt as Guidance:The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on- topic, even if irrelevant content is present in the dataset.
* Why This Approach Works:The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.
* Why Other Options Are Less Suitable:
* A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.
* B (Include in the System Prompt about SnoPen AI): This option doesn't address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.
* D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won't help filter out irrelevant content effectively.
Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant information.


NEW QUESTION # 54
......

To give you an idea about the top features of TrainingDump Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam questions, a free demo of TrainingDump Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam dumps is being offered free of cost. Just download TrainingDump Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam questions demo and checks out the top features of TrainingDump Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam dumps. If you feel that TrainingDump Databricks Databricks-Generative-AI-Engineer-Associate exam questions work for you then buy the full and final TrainingDump Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam dumps at an affordable price and start Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam preparation.

Examcollection Databricks-Generative-AI-Engineer-Associate Vce: https://www.trainingdump.com/Databricks/Databricks-Generative-AI-Engineer-Associate-practice-exam-dumps.html

Report this page