Databricks GenAI Engineer flashcards
Databricks GenAI Engineer · Build recall with a focused review session.
32 cards. Read the term and the definition here. The deck at the top of this page is optional.
- Foundation Model APIs
- Databricks-hosted endpoints providing pay-per-token access to models such as Llama, DBRX, and MPT, callable through an OpenAI-compatible API within the workspace.
- ChatDatabricks LangChain class
- A LangChain chat model wrapper from the databricks-langchain package that sends requests to a Databricks Foundation Model or serving endpoint by name.
- Model Serving endpoint
- A Databricks resource that hosts models or foundation model APIs behind a REST API, supporting autoscaling, concurrency, and optional GPU hardware.
- Provisioned throughput
- A serving mode that reserves model capacity for consistent latency and throughput, billed per hour rather than per token.
- RAG pipeline
- Retrieval-augmented generation: embed and index documents, retrieve relevant chunks for a query, then inject them into the prompt sent to an LLM.
- Agent Framework
- Mosaic AI Agent Framework for building, deploying, and evaluating LLM agents with tool-calling and retrieval, integrated with MLflow tracing.
- OpenAI-compatible client
- Databricks serving endpoints expose /chat/completions; point the OpenAI SDK base_url to the workspace serving endpoint and authenticate with a Databricks token.
- Delta Sync index
- A Vector Search index that automatically syncs embeddings from a Delta table, keeping the index current as source data changes.
- Direct Vector Access
- A Vector Search index type where vectors are inserted and updated manually via API rather than synced from Delta.
- Chunking
- Splitting documents into smaller passages before embedding; chunk size and overlap trade retrieval precision against context completeness.
- Embedding model
- A model converting text to dense vectors; the same model must be used for indexing and query time to keep vector spaces aligned.
- ai_parse_document
- Databricks SQL AI function that extracts text and structure from PDFs, images, and documents for downstream RAG ingestion.
- Change Data Capture
- Reading Delta table change feeds to incrementally update embeddings instead of reprocessing the entire corpus.
- Vector Search index metadata
- Additional Delta columns stored alongside vectors, filterable at query time to restrict retrieval by attributes like source or date.
- MLflow evaluate
- An MLflow API that runs an evaluation dataset through a model or agent and computes scorers such as correctness, relevance, and groundedness.
- LLM-as-judge
- An evaluation technique where a language model scores outputs against criteria or reference answers using a rubric prompt.
- Groundedness scorer
- A metric measuring whether an answer is supported by retrieved context, flagging hallucinations in RAG responses.
- Foundation Model Fine-tuning
- Databricks supervised fine-tuning of supported models using instruction-response pairs, producing a new model version registered in Unity Catalog.
- Evaluation dataset
- A structured collection of inputs and optional expectations used to reproducibly score model or agent behavior over time.
- Review App
- A Databricks labeling interface where domain experts review traces and provide feedback used to improve and evaluate agents.
- Inference tables
- Delta tables that log requests and responses for a serving endpoint, enabling monitoring, debugging, and quality analysis.
- Scale to zero
- A serving endpoint setting that scales replicas down to zero when idle, reducing cost at the expense of cold-start latency.
- Traffic splitting
- Routing a percentage of endpoint requests to different served model versions to support canary releases and A/B tests.
- Lakehouse Monitoring
- Databricks profiling and drift detection for tables, including inference tables, with configurable metrics and refresh schedules.
- Databricks Asset Bundles
- A declarative packaging format for jobs, pipelines, and serving endpoints that supports versioned deployment across environments.
- System tables
- Account-level Delta tables such as system.serving and system.billing.usage that expose endpoint logs, usage, and cost for monitoring.
- AI Gateway
- A governance layer on serving endpoints providing usage tracking, rate limits, payload logging, and guardrails such as PII and content safety filters.
- Unity Catalog
- A unified governance layer for data, models, and functions providing fine-grained grants, lineage, and audit across the lakehouse.
- PII redaction
- Detecting and masking personal identifiers in prompts or logs using functions like ai_mask or presidio before storage or model calls.
- Audit logs
- Audit logs in system.access.audit record who accessed or modified data, models, and endpoints, supporting compliance investigations and incident response.
- Content safety filter
- A guardrail that screens model inputs and outputs for harmful categories such as hate, violence, or self-harm and blocks or flags them.
- Service principal
- A non-human identity used by applications to authenticate to Databricks, granted least-privilege access to data and endpoints.