Free Practice Questions for Databricks Certified Generative AI Engineer Associate Certification

1.1: Design a prompt that elicits a specifically formatted response

1.2: Select model tasks to accomplish a given business requirement

1.3: Select chain components for a desired model input and output

1.4: Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline

1.5: Define and order tools that gather knowledge or take actions for multi-stage reasoning

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

2.1: Apply a chunking strategy for a given document structure and model constraints

2.2: Filter extraneous content in source documents that degrades quality of a RAG application

2.3: Choose the appropriate Python package to extract document content from provided source data and format.

2.4: Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog

2.5: Identify needed source documents that provide necessary knowledge and quality for a given RAG application

2.6: Use tools and metrics to evaluate retrieval performance

2.7: Design retrieval systems using advanced chunking strategies

2.8: Explain the role of re-ranking in the information retrieval process

3.1: Select Langchain/similar tools for use in a Generative AI application.

3.2: Qualitatively assess responses to identify common issues such as quality and safety

3.3: Select chunking strategy based on model & retrieval evaluation

3.4: Augment a prompt with additional context from a user's input based on key fields, terms, and intents

3.5: Create a prompt that adjusts an LLM's response from a baseline to a desired output

3.6: Implement LLM guardrails to prevent negative outcomes

3.7: Select the best LLM based on the attributes of the application to be developed

3.8: Select an embedding model context length based on source documents, expected queries, and optimization strategy

3.9: Select a model from a model hub or marketplace for a task based on model metadata/model cards

3.10: Select the best model for a given task based on common metrics generated in experiments

3.11: Utilize MLflow and Agent Framework for developing agentic systems

3.12: Compare the evaluation and monitoring phases of the Gen AI application life cycle

3.13: Enable multi-agent systems to leverage Genie Spaces or conversational API to retrieve data

4.1: Code a chain using a pyfunc model with pre- and post-processing

4.2: Control access to resources from model serving endpoints

4.3: Code a simple chain according to requirements

4.4: Choose the basic elements needed to create a RAG application: model flavor, embedding model, retriever, dependencies, input examples, model signature

4.5: Register the model to Unity Catalog using MLflow

4.6: Create and query a Vector Search index

4.7: Identify how to serve an LLM application that leverages Foundation Model APIs

4.8: Explain the key concepts and components of Mosaic AI Vector Search

4.9: Identify batch inference workloads and apply ai_query() appropriately

4.10: Configure vector search for a particular solution based on number of embeddings, update frequency, latency, and cost requirements.

4.11: Configure a persistent datastore to store and retrieve intermediate memory or structured information.

4.12: Apply CI/CD best practices such as updating a Vector Search index, promoting prompts across environments, and testing individual components of an agent.

4.13: Integrate managed, external, and custom MCP servers based on a given application requirements

4.14: Apply prompt version control and manage prompt lifecycle

4.15: Develop an appropriate interactive user facing interface for an agent usage scenario (Apps, Slack, Teams, etc.)

5.1: Use masking techniques as guard rails to meet a performance objective

5.2: Select guardrail techniques to protect against malicious user inputs to a Gen AI application

5.3: Use legal/licensing requirements for data sources to avoid legal risk

5.4: Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application

6.1: Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics

6.2: Select key metrics to monitor for a specific LLM deployment scenario

6.3: Evaluate agent performance using MLflow scoring and tracing

6.4: Use inference logging to assess deployed RAG application performance

6.5: Use Databricks features to control LLM costs

6.6: Use inference tables and Agent Monitoring to track a live LLM endpoint

6.7: Identify evaluation judges that require ground truth

6.8: Use AI Gateway (Inference Tables, Usage Tables, and rate limiting) to track an LLM or agent deployed via Agent Framework.

6.9: Use Databricks custom Scorers for evaluating agents and LLMs

6.10: Incorporate SME feedback to improve agent performance