Databricks Certified Generative AI Engineer Associate Practice Exams & Study Guide

Design a prompt that elicits a specifically formatted response. Select model tasks to accomplish a given business requirement. Select chain components for a desired model input and output. Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline. Define and order tools that gather knowledge or take actions for multi-stage reasoning.

Apply a chunking strategy for a given document structure and model constraints. Filter extraneous content in source documents that degrades quality of a RAG application. Choose the appropriate Python package to extract document content from provided source data and format. Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog. Identify needed source documents that provide necessary knowledge and quality for a given RAG application. Identify prompt and response pairs that align with a given model task. Use tools and metrics to evaluate retrieval performance. Design retrieval systems using advanced chunking strategies. Explain the role of re-ranking in the information retrieval process. Apply chunking strategy for a given document structure.

Create tools needed to extract data for a given data retrieval need. Select LangChain or similar tools for use in a Generative AI application. Identify how prompt formats can change model outputs and results. Qualitatively assess responses to identify common issues such as quality and safety. Select chunking strategy based on model and retrieval evaluation. Augment a prompt with additional context from a user's input based on key fields, terms, and intents. Create a prompt that adjusts an LLM's response from a baseline to a desired output. Implement LLM guardrails to prevent negative outcomes. Write metaprompts that minimize hallucinations or leaking private data. Build agent prompt templates exposing available functions. Select the best LLM based on the attributes of the application to be developed. Select an embedding model context length based on source documents, expected queries, and optimization strategy. Select a model from a model hub or marketplace for a task based on model metadata or model cards. Select the best model for a given task based on common metrics generated in experiments. Utilize Agent Framework for developing agentic systems.

Code a chain using a pyfunc model with pre- and post-processing. Control access to resources from model serving endpoints. Code a simple chain according to requirements. Code a simple chain using LangChain. Choose the basic elements needed to create a RAG application: model flavor, embedding model, retriever, dependencies, input examples, model signature. Register the model to Unity Catalog using MLflow. Sequence the steps needed to deploy an endpoint for a basic RAG application. Create and query a Vector Search index. Identify how to serve an LLM application that leverages Foundation Model APIs. Identify resources needed to serve features for a RAG application. Explain the key concepts and components of Mosaic AI Vector Search. Identify batch inference workloads and apply ai_query() appropriately.

Use masking techniques as guardrails to meet a performance objective. Select guardrail techniques to protect against malicious user inputs to a Gen AI application. Recommend an alternative for problematic text mitigation in a data source feeding a RAG application. Use legal and licensing requirements for data sources to avoid legal risk.

Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics. Select key metrics to monitor for a specific LLM deployment scenario. Evaluate model performance in a RAG application using MLflow. Use inference logging to assess deployed RAG application performance. Use Databricks features to control LLM costs for RAG applications. Use inference tables and Agent Monitoring to track a live LLM endpoint. Identify evaluation judges that require ground truth. Compare the evaluation and monitoring phases of the Gen AI application life cycle.