100+ Free Databricks Context Engineer Associate Practice Questions

Designing instructions and system prompts

Author durable system prompts with role and scope framing, explicit output schemas, curated few-shot examples, grounding and refusal rules, and instruction precedence so the agent behaves consistently and resists override attempts.

Configuring retrieval systems with Mosaic AI Vector Search

Create Delta Sync and Direct Vector Access indexes, choose managed or self-managed embeddings, and tune chunking, top-k, similarity thresholds, hybrid keyword-similarity search, reranking, and metadata filtering for high-quality RAG.

Memory architecture with Lakebase and MLflow

Persist short-term state per thread_id and long-term memory per user_id using Lakebase serverless Postgres, checkpoint LangGraph agents, retrieve only relevant memories, and audit assembled context with MLflow Tracing.

Agent integration with tools via MCP and Unity Catalog functions

Connect agents to tools, resources, and data through the Model Context Protocol and managed MCP servers for AI Search, Genie, Databricks SQL, and Unity Catalog functions, all governed by Unity Catalog and the Unity AI Gateway.

Context-window management

Bound context growth with a cascade of tool-output compression, sliding-window trimming, and last-resort LLM summarization (compaction), plus token budgeting and structured note-taking to avoid context rot in long conversations.

Governance of context with Unity Catalog

Use Unity Catalog metadata, tags, column masking, row filters, lineage, and policy to enforce data quality and PII handling so only trusted, authorized data ever enters the agent's context.

Multi-agent and long-horizon workflows

Isolate context per specialist agent, orchestrate supervisor and worker agents, pass concise summarized handoffs, coordinate durable shared state in Lakebase, and validate outputs to prevent error amplification across long tasks.

Empirical evaluation of context engineering

Measure agent quality with MLflow scorers and LLM-as-a-judge for correctness, groundedness, relevance, and safety; use trajectory evaluation, cost and latency metrics, fixed datasets, and production monitoring to verify each change improves the agent.