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| Section | Weight | Objectives |
|---|---|---|
| Design Applications | 14% | - Define functional requirements - Design and architect applications - Select appropriate models |
| Evaluation and Monitoring | 12% | - Monitor deployed applications - Evaluate model quality and performance |
| Governance | 8% | - Manage permissions and logging - Apply safety filters and guidelines |
| Application Development | 30% | - Develop LLM chains - Integrate Databricks tools (LangChain, etc.) - Implement prompt engineering |
| Assembling and Deploying Apps | 22% | - Build and deploy RAG pipelines - Use Vector Search - Package and deploy models |
| Data Preparation | 14% | - Manage data quality and chunking - Prepare data for RAG applications |
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NEW QUESTION # 37
A Generative AI Engineer at a legal firm is designing a RAG system to analyze historical legal cases. The system needs to process millions of court opinions and legal documents, already organized by time and topic, to track how interpretations of specific laws have evolved over time. All of these documents are in plain-text. The engineer needs to choose a chunking method that would most effectively preserve continuity and the temporal nature of the cases. Which method do they choose?
Answer: A
Explanation:
In the context of legal document analysis where the "evolution of interpretation" is the primary goal, preserving narrative continuity is paramount. Windowed summarization with overlapping chunks is the most effective method for this use case. Overlapping (e.g., 10-15% of the chunk size) ensures that sentences or concepts split at the boundary of one chunk are preserved in the next, preventing the loss of critical context that often occurs in legal jargon. Furthermore, windowed summarization allows the system to condense long-form court opinions into manageable parts while maintaining the chronological "thread" of the argument. While sentence-level embeddings with metadata (D) are useful for filtering, they often lack the sufficient context required to understand the nuances of a legal ruling. A windowed approach provides the LLM with enough surrounding text to understand the "why" behind a legal evolution, rather than just the "when."
NEW QUESTION # 38
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?
Answer: B
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 # 39
A Generative Al Engineer is helping a cinema extend its website ' s chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?
Answer: A
Explanation:
The task is to extend a cinema chatbot to provide movie showtime information using a RAG application, leveraging user location and a continuously updated Delta table, with minimal effort and high performance.
Let's evaluate the options.
* Option A: Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation
* Databricks Feature Serving provides low-latency access to real-time data from Delta tables via an online store. Syncing the Delta table to a Feature Serving Endpoint allows the chatbot to query showtimes efficiently, integrating seamlessly into the RAG agent's tool logic. This leverages Databricks' native infrastructure, minimizing effort and ensuring performance.
* Databricks Reference : " Feature Serving Endpoints provide real-time access to Delta table data with low latency, ideal for production systems " ( " Databricks Feature Engineering Guide, "
2023).
* Option B: Query the Delta table directly via a SQL query constructed from the user ' s input using a text-to-SQL LLM in the agent logic / tool
* Using a text-to-SQL LLM to generate queries adds complexity (e.g., ensuring accurate SQL generation) and latency (LLM inference + SQL execution). While feasible, it's less performant and requires more effort than a pre-built serving solution.
* Databricks Reference : " Direct SQL queries are flexible but may introduce overhead in real- time applications " ( " Building LLM Applications with Databricks " ).
* Option C: Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation
* Converting structured Delta table data (e.g., showtimes) into text, embedding it, and using vector search is inefficient for structured lookups. It's effort-intensive (preprocessing, embedding) and less precise than direct queries, undermining performance.
* Databricks Reference : " Vector search excels for unstructured data, not structured tabular lookups " ( " Databricks Vector Search Documentation " ).
* Option D: Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation
* Exporting to an external database (e.g., MySQL) adds setup effort (workflow, external DB management) and latency (periodic updates vs. real-time). It's less performant and more complex than using Databricks' native tools.
* Databricks Reference : " Avoid external systems when Delta tables provide real-time data natively " ( " Databricks Workflows Guide " ).
Conclusion : Option A minimizes effort by using Databricks Feature Serving for real-time, low-latency access to the Delta table, ensuring high performance in a production-ready RAG chatbot.
NEW QUESTION # 40
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.)
Answer: B,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 # 41
A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?
Answer: D
Explanation:
When deploying an LLM application for customer service inquiries, the primary focus is on measuring the operational efficiency and quality of the responses. Here's whyAis the correct metric:
* Number of customer inquiries processed per unit of time: This metric tracks the throughput of the customer service system, reflecting how many customer inquiries the LLM application can handle in a given time period (e.g., per minute or hour). High throughput is crucial in customer service applications where quick response times are essential to user satisfaction and business efficiency.
* Real-time performance monitoring: Monitoring the number of queries processed is an important part of ensuring that the model is performing well under load, especially during peak traffic times. It also helps ensure the system scales properly to meet demand.
Why other options are not ideal:
* B. Energy usage per query: While energy efficiency is a consideration, it is not the primary concern for a customer-facing application where user experience (i.e., fast and accurate responses) is critical.
* C. Final perplexity scores for the training of the model: Perplexity is a metric for model training, but it doesn't reflect the real-time operational performance of an LLM in production.
* D. HuggingFace Leaderboard values for the base LLM: The HuggingFace Leaderboard is more relevant during model selection and benchmarking. However, it is not a direct measure of the model's performance in a specific customer service application in production.
Focusing on throughput (inquiries processed per unit time) ensures that the LLM application is meeting business needs for fast and efficient customer service responses.
NEW QUESTION # 42
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