PASS GUARANTEED QUIZ 2025 DATABRICKS DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE–VALID NEW LEARNING MATERIALS

Pass Guaranteed Quiz 2025 Databricks Databricks-Generative-AI-Engineer-Associate–Valid New Learning Materials

Pass Guaranteed Quiz 2025 Databricks Databricks-Generative-AI-Engineer-Associate–Valid New Learning Materials

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These formats hold high demand in the market and offer a great solution for quick and complete Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam preparation. These formats are 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.

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

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 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
  • 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 5
  • 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.

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Databricks-Generative-AI-Engineer-Associate Review Guide & Databricks-Generative-AI-Engineer-Associate Boot Camp

Hence, memorizing them will help you get prepared for the Databricks Databricks-Generative-AI-Engineer-Associate examination in a short time. The product of ITCertMagic comes in PDF, desktop practice exam software, and Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) web-based practice test. To give you a complete understanding of these formats, we have discussed their features below.

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

NEW QUESTION # 39
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. Return the extracted information in JSON format.
  • B. 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.
  • 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": "sarah.lee925@gmail.com", "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 # 40
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. Llama2-70B
  • B. MPT-30B
  • C. DistilBERT
  • D. DBRX

Answer: A

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 # 41
What is the most suitable library for building a multi-step LLM-based workflow?

  • A. Pandas
  • B. PySpark
  • C. LangChain
  • D. TensorFlow

Answer: C

Explanation:
* Problem Context: The Generative AI Engineer needs a tool to build amulti-step LLM-based workflow. This type of workflow often involves chaining multiple steps together, such as query generation, retrieval of information, response generation, and post-processing, with LLMs integrated at several points.
* Explanation of Options:
* Option A: Pandas: Pandas is a powerful data manipulation library for structured data analysis, but it is not designed for managing or orchestrating multi-step workflows, especially those involving LLMs.
* Option B: TensorFlow: TensorFlow is primarily used for training and deploying machine learning models, especially deep learning models. It is not designed for orchestrating multi-step tasks in LLM-based workflows.
* Option C: PySpark: PySpark is a distributed computing framework used for large-scale data processing. While useful for handling big data, it is not specialized for chaining LLM-based operations.
* Option D: LangChain: LangChain is a purpose-built framework designed specifically for orchestrating multi-step workflowswith large language models (LLMs). It enables developers to easily chain different tasks, such as retrieving documents, summarizing information, and generating responses, all in a structured flow. This makes it the best tool for building complex LLM-based workflows.
Thus,LangChainis the most suitable library for creating multi-step LLM-based workflows.


NEW QUESTION # 42
A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot's focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
"Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." Which framework type should be implemented to solve this?

  • A. Safety Guardrail
  • B. Security Guardrail
  • C. Compliance Guardrail
  • D. Contextual Guardrail

Answer: A

Explanation:
In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing aSafety Guardrailis the appropriate solution for this:
* What is a Safety Guardrail?Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.
* Preventing Responses to Political Questions:The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: "Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance."
* How It Works in Practice:The LLM system can include aclassification layeror trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.
* Why Other Options Are Less Suitable:
* B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.
* C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.
* D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.
Therefore, aSafety Guardrailis the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.


NEW QUESTION # 43
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. Limit the data available based on the user's access level
  • 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. Fine-tune the model on your data, hoping it will learn what is appropriate and not

Answer: D

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 # 44
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