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NEW QUESTION # 73
A Generative AI Engineer is developing an agent system using a popular agent-authoring library. The agent comprises multiple parallel and sequential chains. The engineer encounters challenges as the agent fails at one of the steps, making it difficult to debug the root cause. They need to find an appropriate approach to research this issue and discover the cause of failure. Which approach do they choose?
Answer: A
Explanation:
For complex agentic systems (like those built with LangGraph or Autogen), standard logging is often insufficient because the "state" of the agent changes dynamically. MLflow Tracing is the designated Generative AI engineering standard for debugging these systems. Tracing provides a visual, hierarchical timeline of every call made during an agent's execution-including internal LLM reasoning, tool calls, and data transformations. When a step fails, the trace allows the engineer to click into that specific node to see the exact input sent to the LLM and the raw output received. This is much faster and more comprehensive than manually deconstructing the agent (D) or adding manual logs (C). While mlflow.evaluate (B) is useful for measuring performance across a whole dataset, it is not a tool for real-time debugging of a single execution failure.
NEW QUESTION # 74
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: C
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 # 75
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: A
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 # 76
A Generative AI Engineer is building a Databricks-hosted assistant that must (1) query Unity Catalog tables with row and column permissions enforced, and (2) avoid managing any external infrastructure. The team wants the LLM to use governed data access through tools exposed via MCP.
Which MCP server choice meets these constraints?
Answer: B
Explanation:
A managed Databricks MCP server provides tools for accessing Databricks services without requiring the team to host an external server. For structured table queries, a suitable managed SQL or Genie tool uses Databricks-governed data access. Unity Catalog remains responsible for authorization instead of delegating security decisions to the language model. When permissions must reflect the individual requester, the application must use the appropriate user authorization configuration so row filters and column masks are evaluated for that identity. Passing credentials in prompts exposes secrets unnecessarily. A self-managed virtual machine violates the infrastructure requirement. Direct JDBC access also fails the requested MCP architecture and introduces custom integration work. Managed hosting simplifies deployment while preserving the platform's access-control model. Databricks tools documentation , authentication documentation
NEW QUESTION # 77
A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.
Which input/output pair will support their goal?
Answer: B
Explanation:
Context: The goal is to improve the online customer experience in a restaurant by handling common inquiries about bookings, minimizing escalations, and maintaining personalized interactions.
Explanation of Options:
* Option A: Grouping and summarizing chat logs by user could provide insights into customer interactions but does not directly address the task of handling booking inquiries or minimizing escalations.
* Option B: Using chat logs to generate interactive buttons for booking details directly supports the goal of facilitating online bookings, minimizing the need for human intervention by providing clear, interactive options for customers to self-serve.
* Option C: Classifying sentiment of customer reviews does not directly help with booking inquiries, although it might provide valuable feedback insights.
* Option D: Providing cancellation options is helpful but narrowly focuses on one aspect of the booking process and doesn't support the broader goal of handling common inquiries about bookings.
Option Bbest supports the goal of improving online interactions by using chat logs to generate actionable items for customers, helping them complete booking tasks efficiently and reducing the need for human intervention.
NEW QUESTION # 78
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