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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Assembling and Deploying Apps | 22% | - Use Vector Search - Package and deploy models - Build and deploy RAG pipelines |
| Topic 2: Application Development | 30% | - Develop LLM chains - Integrate Databricks tools (LangChain, etc.) - Implement prompt engineering |
| Topic 3: Data Preparation | 14% | - Prepare data for RAG applications - Manage data quality and chunking |
| Topic 4: Design Applications | 14% | - Select appropriate models - Design and architect applications - Define functional requirements |
| Topic 5: Governance | 8% | - Apply safety filters and guidelines - Manage permissions and logging |
| Topic 6: Evaluation and Monitoring | 12% | - Evaluate model quality and performance - Monitor deployed applications |
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질문 # 69
A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?
정답：A
설명：
To build an LLM-powered system that accesses up-to-date news articles and stock prices, the best approach is tocreate an agentthat has access to specific tools (option D).
* Agent with SQL and Web Search Capabilities:By using an agent-based architecture, the LLM can interact with external tools. The agent can query Delta tables (for up-to-date stock prices) via SQL and perform web searches to retrieve the latest news articles. This modular approach ensures the system can access both structured (stock prices) and unstructured (news) data sources dynamically.
* Why This Approach Works:
* SQL Queries for Stock Prices: Delta tables store stock prices, which the agent can query directly for the latest data.
* Web Search for News: For news articles, the agent can generate search queries and retrieve the most relevant and recent articles, then pass them to the LLM for processing.
* Why Other Options Are Less Suitable:
* A (Summarizing News for Stock Prices): This convoluted approach would not ensure accuracy when retrieving stock prices, which are already structured and stored in Delta tables.
* B (Stock Price Volatility Queries): While this could retrieve relevant information, it doesn't address how to obtain the most up-to-date news articles.
* C (Vector Store): Storing news articles and stock prices in a vector store might not capture the real-time nature of stock data and news updates, as it relies on pre-existing data rather than dynamic querying.
Thus, using an agent with access to both SQL for querying stock prices and web search for retrieving news articles is the best approach for ensuring up-to-date and accurate responses.
질문 # 70
A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?
정답：A
설명：
* Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
* Explanation of Options:
* Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
* Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
* Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
* Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.
질문 # 71
A Generative AI Engineer at an automotive company would like to build a question-answering chatbot to help customers answer specific questions about their vehicles. They have:
A catalog with hundreds of thousands of cars manufactured since the 1960s Historical searches with user queries and successful matches Descriptions of their own cars in multiple languages They have already selected an open-source LLM and created a test set of user queries. They need to discard techniques that will not help them build the chatbot. Which do they discard?
정답：A
설명：
According to Generative AI engineering standards for Retrieval-Augmented Generation (RAG), chunking strategy is a critical optimization variable. Setting the chunk size to match the model's maximum context window (e.g., 4k or 8k tokens) is a poor practice and should be discarded. Large chunks introduce significant "noise" into the LLM's context, as only a small portion of a massive chunk usually contains the answer to a specific query. This leads to the "lost in the middle" phenomenon where LLMs struggle to extract relevant information from bloated contexts. Furthermore, large chunks reduce the precision of the vector search. Standard best practices involve using smaller, semantically meaningful chunks (typically 256-512 tokens) with overlap to maintain context. In contrast, metadata filtering (B) is essential for narrowing searches to specific car years, fine-tuning embeddings (C) improves retrieval accuracy for domain-specific technical terms, and few-shot examples (D) guide the LLM's output format and tone.
질문 # 72
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?
정답：C
설명：
* 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.
질문 # 73
A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.
Which strategy for picking an embedding model should they choose?
정답：A
설명：
The task involves improving a Retrieval-Augmented Generation (RAG) application's performance by experimenting with embedding models. The choice of embedding model impacts retrieval accuracy, which is critical for RAG systems. Let's evaluate the options based on Databricks Generative AI Engineer best practices.
Option A: Pick an embedding model trained on related domain knowledge
Embedding models trained on domain-specific data (e.g., industry-specific corpora) produce vectors that better capture the semantics of the application's context, improving retrieval relevance. For RAG, this is a key strategy to enhance performance.
Databricks Reference: "For optimal retrieval in RAG systems, select embedding models aligned with the domain of your data" ("Building LLM Applications with Databricks," 2023).
Option B: Pick the most recent and most performant open LLM released at the time LLMs are not embedding models; they generate text, not embeddings for retrieval. While recent LLMs may be performant for generation, this doesn't address the embedding step in RAG. This option misunderstands the component being selected.
Databricks Reference: Embedding models and LLMs are distinct in RAG workflows: "Embedding models convert text to vectors, while LLMs generate responses" ("Generative AI Cookbook").
Option C: Pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace The MTEB leaderboard ranks models across general tasks, but high overall performance doesn't guarantee suitability for a specific domain. A top-ranked model might excel in generic contexts but underperform on the engineer's unique data.
Databricks Reference: General performance is less critical than domain fit: "Benchmark rankings provide a starting point, but domain-specific evaluation is recommended" ("Databricks Generative AI Engineer Guide").
Option D: Pick an embedding model with multilingual support to support potential multilingual user questions Multilingual support is useful only if the application explicitly requires it. Without evidence of multilingual needs, this adds complexity without guaranteed performance gains for the current use case.
Databricks Reference: "Choose features like multilingual support based on application requirements" ("Building LLM-Powered Applications").
Conclusion: Option A is the best strategy because it prioritizes domain relevance, directly improving retrieval accuracy in a RAG system-aligning with Databricks' emphasis on tailoring models to specific use cases.
질문 # 74
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지난 몇년동안 IT산업의 지속적인 발전과 성장을 통해Databricks 인증Databricks-Generative-AI-Engineer-Associate시험은 IT인증시험중의 이정표로 되어 많은 인기를 누리고 있습니다. IT인증시험을DumpTOP덤프로 준비해야만 하는 이유는DumpTOP덤프는 IT업계전문가들이 실제시험문제를 연구하여 시험문제에 대비하여 예상문제를 제작했다는 점에 있습니다.
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