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| Section | Weight | Objectives |
|---|---|---|
| Assembling and Deploying Apps | 22% | - Use Vector Search - Package and deploy models - Build and deploy RAG pipelines |
| Evaluation and Monitoring | 12% | - Monitor deployed applications - Evaluate model quality and performance |
| Design Applications | 14% | - Design and architect applications - Define functional requirements - Select appropriate models |
| Governance | 8% | - Apply safety filters and guidelines - Manage permissions and logging |
| Application Development | 30% | - Implement prompt engineering - Integrate Databricks tools (LangChain, etc.) - Develop LLM chains |
| Data Preparation | 14% | - Manage data quality and chunking - Prepare data for RAG applications |
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NEW QUESTION # 36
A company has a typical RAG-enabled, customer-facing chatbot on its website.
Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.
Answer: C
Explanation:
To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let's go through the correct sequence as depicted in the diagram and explained in option A:
* Embedding Model (1):The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.
* Vector Search (2):The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.
* Context-Augmented Prompt (3):The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.
* Response-Generating LLM (4):Finally, the context-augmented prompt is fed into a response- generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.
Why Other Options Are Less Suitable:
* B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.
Thus, the correct sequence isembedding model, vector search, context-augmented prompt, response- generating LLM, which is option A.
NEW QUESTION # 37
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?
Answer: A
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 # 38
A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?
Answer: C
Explanation:
When prioritizing cost and latency over quality in a Large Language Model (LLM)-based application, it is crucial to select a configuration that minimizes both computational resources and latency while still providing reasonable performance. Here's why D is the best choice:
Context length: The context length of 512 tokens aligns with the chunk size used for the documents (maximum of 512 tokens per chunk). This is sufficient for capturing the needed information and generating responses without unnecessary overhead.
Smallest model size: The model with a size of 0.13GB is significantly smaller than the other options. This small footprint ensures faster inference times and lower memory usage, which directly reduces both latency and cost.
Embedding dimension: While the embedding dimension of 384 is smaller than the other options, it is still adequate for tasks where cost and speed are more important than precision and depth of understanding.
This setup achieves the desired balance between cost-efficiency and reasonable performance in a latency-sensitive, cost-conscious application.
NEW QUESTION # 39
A Generative Al Engineer is developing a RAG system for their company to perform internal document Q&A for structured HR policies, but the answers returned are frequently incomplete and unstructured It seems that the retriever is not returning all relevant context The Generative Al Engineer has experimented with different embedding and response generating LLMs but that did not improve results.
Which TWO options could be used to improve the response quality?
Choose 2 answers
Answer: B,E
Explanation:
The problem describes a Retrieval-Augmented Generation (RAG) system for HR policy Q&A where responses are incomplete and unstructured due to the retriever failing to return sufficient context. The engineer has already tried different embedding and response-generating LLMs without success, suggesting the issue lies in the retrieval process-specifically, how documents are chunked and indexed. Let's evaluate the options.
* Option A: Add the section header as a prefix to chunks
* Adding section headers provides additional context to each chunk, helping the retriever understand the chunk's relevance within the document structure (e.g., "Leave Policy: Annual Leave" vs. just "Annual Leave"). This can improve retrieval precision for structured HR policies.
* Databricks Reference:"Metadata, such as section headers, can be appended to chunks to enhance retrieval accuracy in RAG systems"("Databricks Generative AI Cookbook," 2023).
* Option B: Increase the document chunk size
* Larger chunks include more context per retrieval, reducing the chance of missing relevant information split across smaller chunks. For structured HR policies, this can ensure entire sections or rules are retrieved together.
* Databricks Reference:"Increasing chunk size can improve context completeness, though it may trade off with retrieval specificity"("Building LLM Applications with Databricks").
* Option C: Split the document by sentence
* Splitting by sentence creates very small chunks, which could exacerbate the problem by fragmenting context further. This is likely why the current system fails-it retrieves incomplete snippets rather than cohesive policy sections.
* Databricks Reference: No specific extract opposes this, but the emphasis on context completeness in RAG suggests smaller chunks worsen incomplete responses.
* Option D: Use a larger embedding model
* A larger embedding model might improve vector quality, but the question states that experimenting with different embedding models didn't help. This suggests the issue isn't embedding quality but rather chunking/retrieval strategy.
* Databricks Reference: Embedding models are critical, but not the focus when retrieval context is the bottleneck.
* Option E: Fine tune the response generation model
* Fine-tuning the LLM could improve response coherence, but if the retriever doesn't provide complete context, the LLM can't generate full answers. The root issue is retrieval, not generation.
* Databricks Reference: Fine-tuning is recommended for domain-specific generation, not retrieval fixes ("Generative AI Engineer Guide").
Conclusion: Options A and B address the retrieval issue directly by enhancing chunk context-either through metadata (A) or size (B)-aligning with Databricks' RAG optimization strategies. C would worsen the problem, while D and E don't target the root cause given prior experimentation.
NEW QUESTION # 40
A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team's latest standings.
How could the Generative AI Engineer best design these capabilities into their system?
Answer: B
Explanation:
In this scenario, the Generative AI Engineer needs to design a system that can handle different types of queries about the monster truck team. The queries may involve text-based information, API lookups for event dates, or table queries for standings. The best solution is to implement atool-based agent system.
Here's how option B works, and why it's the most appropriate answer:
* System Design Using Agent-Based Model:In modern agent-based LLM systems, you can design a system where the LLM (Large Language Model) acts as a central orchestrator. The model can "decide" which tools to use based on the query. These tools can include API calls, table lookups, or natural language searches. The system should contain asystem promptthat informs the LLM about the available tools.
* System Prompt Listing Tools:By creating a well-craftedsystem prompt, the LLM knows which tools are at its disposal. For instance, one tool may query an external API for event dates, another might look up standings in a database, and a third may involve searching a vector database for general text-based information. Theagentwill be responsible for calling the appropriate tool depending on the query.
* Agent Orchestration of Calls:The agent system is designed to execute a series of steps based on the incoming query. If a user asks for the next event date, the system will recognize this as a task that requires an API call. If the user asks about standings, the agent might query the appropriate table in the database. For text-based questions, it may call a search function over ingested data. The agent orchestrates this entire process, ensuring the LLM makes calls to the right resources dynamically.
* Generative AI Tools and Context:This is a standard architecture for integrating multiple functionalities into a system where each query requires different actions. The core design in option B is efficient because it keeps the system modular and dynamic by leveraging tools rather than overloading the LLM with static information in a system prompt (like option D).
* Why Other Options Are Less Suitable:
* A (RAG Architecture): While relevant, simply ingesting PDFs into a vector store only helps with text-based retrieval. It wouldn't help with API lookups or table queries.
* C (Conditional Logic with RAG/API/TABLE): Although this approach works, it relies heavily on manual text parsing and might introduce complexity when scaling the system.
* D (System Prompt with Event Dates and Standings): Hardcoding dates and table information into a system prompt isn't scalable. As the standings or events change, the system would need constant updating, making it inefficient.
By bundling multiple tools into a single agent-based system (as in option B), the Generative AI Engineer can best handle the diverse requirements of this system.
NEW QUESTION # 41
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