HIGH-EFFICIENT DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE EXAM DUMPS: DATABRICKS CERTIFIED GENERATIVE AI ENGINEER ASSOCIATE AND PREPARATION MATERIALS - PREP4PASS

High-Efficient Databricks-Generative-AI-Engineer-Associate Exam Dumps: Databricks Certified Generative AI Engineer Associate and preparation materials - Prep4pass

High-Efficient Databricks-Generative-AI-Engineer-Associate Exam Dumps: Databricks Certified Generative AI Engineer Associate and preparation materials - Prep4pass

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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
  • 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 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
  • 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.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q15-Q20):

NEW QUESTION # 15
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer's system perform?

  • A. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
  • B. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
  • C. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
  • D. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.

Answer: C

Explanation:
To design an LLM-based application that can answer employee HR questions using HR PDF documentation, the most effective approach is option D. Here's why:
* Chunking and Vector Store Embedding:HR documentation tends to be lengthy, so splitting it into smaller, manageable chunks helps optimize retrieval. These chunks are then embedded into avector store(a database that stores vector representations of text). Each chunk of text is transformed into an embeddingusing a transformer-based model, which allows for efficient similarity-based retrieval.
* Using Vector Search for Retrieval:When an employee asks a question, the system converts their query into an embedding as well. This embedding is then compared with the embeddings of the document chunks in the vector store. The most semantically similar chunks are retrieved, which ensures that the answer is based on the most relevant parts of the documentation.
* LLM to Generate a Response:Once the relevant chunks are retrieved, these chunks are passed into the LLM, which uses them as context to generate a coherent and accurate response to the employee's question.
* Why Other Options Are Less Suitable:
* A (Calculate Averaged Embeddings): Averaging embeddings might dilute important information. It doesn't provide enough granularity to focus on specific sections of documents.
* B (Summarize HR Documentation): Summarization loses the detail necessary for HR-related queries, which are often specific. It would likely miss the mark for more detailed inquiries.
* C (Interaction Matrix and ALS): This approach is better suited for recommendation systems and not for HR queries, as it's focused on collaborative filtering rather than text-based retrieval.
Thus, option D is the most effective solution for providing precise and contextual answers based on HR documentation.


NEW QUESTION # 16
A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.
What is the most performant way to store this dataframe?

  • A. Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table
  • B. First create a unique identifier for each document, then save to a Delta table
  • C. Split the data into train and test set, create a unique identifier for each document, then save to a Delta table
  • D. Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section

Answer: A

Explanation:
* Problem Context: The engineer needs an efficient way to store chunks of unstructured documents to facilitate easy retrieval and search. The current dataframe consists of document filenames and associated text chunks.
* Explanation of Options:
* Option A: Splitting into train and test sets is more relevant for model training scenarios and not directly applicable to storage for retrieval in a Vector Search index.
* Option B: Flattening the dataframe such that each row contains a single chunk with a unique identifier is the most performant for storage and retrieval. This structure aligns well with how data is indexed and queried in vector search applications, making it easier to retrieve specific chunks efficiently.
* Option C: Creating a unique identifier for each document only does not address the need to access individual chunks efficiently, which is critical in a Vector Search application.
* Option D: Storing each chunk as an independent JSON file creates unnecessary overhead and complexity in managing and querying large volumes of files.
OptionBis the most efficient and practical approach, allowing for streamlined indexing and retrieval processes in a Delta table environment, fitting the requirements of a Vector Search index.


NEW QUESTION # 17
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. Llama2-70B
  • B. Dolly 1.5B
  • C. BGE-large
  • D. OpenAI GPT-4

Answer: C

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 # 18
A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint's incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server.
Which Databricks feature should they use instead which will perform the same task?

  • A. Lakeview
  • B. Vector Search
  • C. DBSQL
  • D. Inference Tables

Answer: D

Explanation:
Problem Context: The goal is to monitor theserving endpointfor incoming requests and outgoing responses in aprovisioned throughput model serving endpointwithin aRetrieval-Augmented Generation (RAG) application. The current approach involves using a microservice to log requests and responses to a remote server, but the Generative AI Engineer is looking for a more streamlined solution within Databricks.
Explanation of Options:
* Option A: Vector Search: This feature is used to perform similarity searches within vector databases.
It doesn't provide functionality for logging or monitoring requests and responses in a serving endpoint, so it's not applicable here.
* Option B: Lakeview: Lakeview is not a feature relevant to monitoring or logging request-response cycles for serving endpoints. It might be more related to viewing data in Databricks Lakehouse but doesn't fulfill the specific monitoring requirement.
* Option C: DBSQL: Databricks SQL (DBSQL) is used for running SQL queries on data stored in Databricks, primarily for analytics purposes. It doesn't provide the direct functionality needed to monitor requests and responses in real-time for an inference endpoint.
* Option D: Inference Tables: This is the correct answer.Inference Tablesin Databricks are designed to store the results and metadata of inference runs. This allows the system to logincoming requests and outgoing responsesdirectly within Databricks, making it an ideal choice for monitoring the behavior of a provisioned serving endpoint. Inference Tables can be queried and analyzed, enabling easier monitoring and debugging compared to a custom microservice.
Thus,Inference Tablesare the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.


NEW QUESTION # 19
When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

  • A. Use any available data you personally created which is completely original and you can decide what license to use.
  • B. Reach out to the data curators directly before you have started using the trained model to let them know.
  • C. Only use data explicitly labeled with an open license and ensure the license terms are followed.
  • D. Reach out to the data curators directly after you have started using the trained model to let them know.

Answer: D

Explanation:
* Problem Context: When using data to train a model, it's essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.
* Explanation of Options:
* Option A: Reaching out to data curatorsbeforeusing the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.
* Option B: Usingoriginal datathat you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.
* Option C: Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.
* Option D: Reaching out to the data curatorsafteryou have already started using the trained model isnot appropriate. If you've already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications. It's essential to clarify the licensing termsbeforeusing the data, not after.
Thus,Option Dis not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.


NEW QUESTION # 20
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