Free Practice Questions for Databricks Certified Generative AI Engineer Associate Certification

- 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 - Determine how and when to use Agent Bricks (Knowledge Assistant, Multiagent Supervisor, Information Extraction) to solve problems

- 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 - 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

- Select Langchain/similar tools for use in a Generative AI application. - Qualitatively assess responses to identify common issues such as quality and safety - Select chunking strategy based on model & 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 - 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/model cards - Select the best model for a given task based on common metrics generated in experiments - Utilize MLflow and Agent Framework for developing agentic systems - Compare the evaluation and monitoring phases of the Gen AI application life cycle - Enable multi-agent systems to leverage Genie Spaces or conversational API to retrieve data

- 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 - 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 - Create and query a Vector Search index - Identify how to serve an LLM application that leverages Foundation Model APIs - Explain the key concepts and components of Mosaic AI Vector Search - Identify batch inference workloads and apply ai_query() appropriately - Configure vector search for a particular solution based on number of embeddings, update frequency, latency, and cost requirements. - Configure a persistent datastore to store and retrieve intermediate memory or structured information. - Apply CI/CD best practices such as updating a Vector Search index, promoting prompts across environments, and testing individual components of an agent. - Integrate managed, external, and custom MCP servers based on a given application requirements - Apply prompt version control and manage prompt lifecycle - Develop an appropriate interactive user facing interface for an agent usage scenario (Apps, Slack, Teams, etc.)

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

- 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 agent performance using MLflow scoring and tracing - Use inference logging to assess deployed RAG application performance - Use Databricks features to control LLM costs - Use inference tables and Agent Monitoring to track a live LLM endpoint - Identify evaluation judges that require ground truth - Use AI Gateway (Inference Tables, Usage Tables, and rate limiting) to track an LLM or agent deployed via Agent Framework. - Use Databricks custom Scorers for evaluating agents and LLMs - Incorporate SME feedback to improve agent performance