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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation and Monitoring | 12% | - Monitor deployed applications - Evaluate model quality and performance |
| Topic 2: Data Preparation | 14% | - Prepare data for RAG applications - Manage data quality and chunking |
| Topic 3: Assembling and Deploying Apps | 22% | - Use Vector Search - Build and deploy RAG pipelines - Package and deploy models |
| Topic 4: Governance | 8% | - Manage permissions and logging - Apply safety filters and guidelines |
| Topic 5: Design Applications | 14% | - Design and architect applications - Define functional requirements - Select appropriate models |
| Topic 6: Application Development | 30% | - Integrate Databricks tools (LangChain, etc.) - Implement prompt engineering - Develop LLM chains |
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NEW QUESTION # 23
A Generative AI Engineer is evaluating a customer-support agent in Databricks. The team needs to score each response on a domain-specific policy: the answer must cite an approved refund rule and must not mention unsupported escalation paths. Built-in evaluation metrics do not capture this logic. The team wants the metric to run during agent evaluation in Databricks and return a repeatable, structured score for each trace.
Which approach should the engineer use?
Answer: A
Explanation:
MLflow supports custom scorers for application-specific evaluation requirements that built-in metrics do not address. The engineer can define a scorer that examines the generated answer and, when necessary, the complete execution trace. It can verify references against an approved refund-rule list and check whether escalation statements violate the policy. The scorer returns structured feedback, such as a Boolean result, numerical score, or Feedback object, and is supplied to mlflow.genai.evaluate(). Deterministic checks are appropriate when repeatability is essential; semantic checks may require a carefully specified judge. Latency and token counts measure operational behavior rather than policy compliance. Reviewing traces manually does not create an automated metric, and the generating model's self-assessment is not an independent compliance evaluation. Databricks documentation
NEW QUESTION # 24
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: C
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 # 25
A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?
Answer: B
Explanation:
The task requires an open generative AI model for a transcription (speech-to-text) task where speed is essential. Let's assess the options based on their suitability for transcription and performance characteristics, referencing Databricks' approach to model selection.
Option A: Llama-2-70b-chat-hf
Llama-2 is a text-based LLM optimized for chat and text generation, not speech-to-text. It lacks transcription capabilities.
Databricks Reference: "Llama models are designed for natural language generation, not audio processing" ("Databricks Model Catalog").
Option B: MPT-30B-Instruct
MPT-30B is another text-based LLM focused on instruction-following and text generation, not transcription. It's irrelevant for speech-to-text tasks.
Databricks Reference: No specific mention, but MPT is categorized under text LLMs in Databricks' ecosystem, not audio models.
Option C: DBRX
DBRX, developed by Databricks, is a powerful text-based LLM for general-purpose generation. It doesn't natively support speech-to-text and isn't optimized for transcription.
Databricks Reference: "DBRX excels at text generation and reasoning tasks" ("Introducing DBRX," 2023)-no mention of audio capabilities.
Option D: whisper-large-v3 (1.6B)
Whisper, developed by OpenAI, is an open-source model specifically designed for speech-to-text transcription. The "large-v3" variant (1.6 billion parameters) balances accuracy and efficiency, with optimizations for speed via quantization or deployment on GPUs-key for the application's requirements.
Databricks Reference: "For audio transcription, models like Whisper are recommended for their speed and accuracy" ("Generative AI Cookbook," 2023). Databricks supports Whisper integration in its MLflow or Lakehouse workflows.
Conclusion: Only D. whisper-large-v3 is a speech-to-text model, making it the sole suitable choice. Its design prioritizes transcription, and its efficiency (e.g., via optimized inference) meets the speed requirement, aligning with Databricks' model deployment best practices.
NEW QUESTION # 26
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: D
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 # 27
An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?
Answer: A,D
Explanation:
To optimize for code generation performance and hosting costs, a Generative AI engineer must look at specific metrics.
Big Code Models Leaderboard (A): This is the industry-standard benchmark for code-specific LLMs (like StarCoder or CodeLlama). It measures performance on tasks like HumanEval and MBPP, providing a direct indicator of how well the model handles programming logic.
Number of model parameters (B): This is the primary driver of hosting costs. Larger models (e.g., 70B) require more GPU memory (VRAM) and more expensive compute instances (like A100s/H100s) than smaller models (e.g., 7B or 13B). To minimize costs, the team should look for the smallest model that achieves a high score on the Big Code Leaderboard.
Note: MTEB (C) is for embeddings, and Chatbot Arena (D) is for general-purpose chat, neither of which is the primary metric for specialized code generation fine-tuning.
NEW QUESTION # 28
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