DATABRICKS DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE TEST OBJECTIVES PDF & DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE TEST TUTORIALS

Databricks Databricks-Generative-AI-Engineer-Associate Test Objectives Pdf & Databricks-Generative-AI-Engineer-Associate Test Tutorials

Databricks Databricks-Generative-AI-Engineer-Associate Test Objectives Pdf & Databricks-Generative-AI-Engineer-Associate Test Tutorials

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

TopicDetails
Topic 1
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.
Topic 2
  • 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 3
  • 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 (Q41-Q46):

NEW QUESTION # 41
A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.
Which metric would help them increase user engagement and retention for their platform?

  • A. Repetition of responses
  • B. Randomness
  • C. Diversity of responses
  • D. Lack of relevance

Answer: C

Explanation:
In the context of designing a chatbot to engage users on a gaming platform,diversity of responses(option B) is a key metric to increase user engagement and retention. Here's why:
* Diverse and Engaging Interactions:A chatbot that provides varied and interesting responses will keep users engaged, especially in an interactive environment like a gaming platform. Gamers typically enjoy dynamic and evolving conversations, anddiversity of responseshelps prevent monotony, encouraging users to interact more frequently with the bot.
* Increasing Retention:By offering different types of responses to similar queries, the chatbot can create a sense of novelty and excitement, which enhances the user's experience and makes them more likely to return to the platform.
* Why Other Options Are Less Effective:
* A (Randomness): Random responses can be confusing or irrelevant, leading to frustration and reducing engagement.
* C (Lack of Relevance): If responses are not relevant to the user's queries, this will degrade the user experience and lead to disengagement.
* D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feel uninteresting and reducing the likelihood of continued interaction.
Thus,diversity of responses(option B) is the most effective way to keep users engaged and retain them on the platform.


NEW QUESTION # 42
A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM's on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?

  • A. NDCG metric
  • B. BLEU metric
  • C. ROUGE metric
  • D. RECALL metric

Answer: B

Explanation:
The task is to benchmark LLMs for text translation using an evaluation set with known high-quality examples, requiring a performant metric. Let's evaluate the options.
* Option A: ROUGE metric
* ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures overlap between generated and reference texts, primarily for summarization. It's less suited for translation, where precision and word order matter more.
* Databricks Reference:"ROUGE is commonly used for summarization, not translation evaluation"("Generative AI Cookbook," 2023).
* Option B: BLEU metric
* BLEU (Bilingual Evaluation Understudy) evaluates translation quality by comparing n-gram overlap with reference translations, accounting for precision and brevity. It's widely used, performant, and appropriate for this task.
* Databricks Reference:"BLEU is a standard metric for evaluating machine translation, balancing accuracy and efficiency"("Building LLM Applications with Databricks").
* Option C: NDCG metric
* NDCG (Normalized Discounted Cumulative Gain) assesses ranking quality, not text generation.
It's irrelevant for translation evaluation.
* Databricks Reference:"NDCG is suited for ranking tasks, not generative output scoring" ("Databricks Generative AI Engineer Guide").
* Option D: RECALL metric
* Recall measures retrieved relevant items but doesn't evaluate translation quality (e.g., fluency, correctness). It's incomplete for this use case.
* Databricks Reference: No specific extract, but recall alone lacks the granularity of BLEU for text generation tasks.
Conclusion: Option B (BLEU) is the best metric for translation evaluation, offering a performant and standard approach, as endorsed by Databricks' guidance on generative tasks.


NEW QUESTION # 43
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. OpenAI GPT-4
  • C. Dolly 1.5B
  • D. BGE-large

Answer: D

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 # 44
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?

  • A. flask
  • B. unstructured
  • C. beautifulsoup
  • D. numpy

Answer: C

Explanation:
* Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.
* Explanation of Options:
* Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from PDFs.
* Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.
* Option C: unstructured: This Python package is specifically designed to work with unstructured data, including extracting text from PDFs. It provides functionalities to handle various types of content in documents with minimal coding, making it ideal for the task.
* Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.
Given the requirement,Option C(unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.


NEW QUESTION # 45
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. Limit the number of relevant documents available for the RAG application to retrieve from
  • B. Pick a smaller LLM that is domain-specific
  • C. Use the largest LLM possible because that gives the best performance for any general queries
  • D. Limit the number of queries a customer can send per day

Answer: B

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 # 46
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