Free Databricks Context Engineer Associate Exam Practice Questions

A context engineer is evaluating a target-landscape research agent at a pharmaceutical R&D organization. Discovery scientists ask the agent to prepare a structured landscape memo about a proposed drug target: what has been studied, who else is in the space, at what clinical phase, and with what regulatory history.

The agent runs as a single agent on Agent Bricks Custom Agents. It retrieves from a Databricks AI Search index built over PubMed, ClinicalTrials.gov, internal program memos, and FDA review documents, and produces the memo in one pass within a single context window.

Narrow queries (one target, one indication) produce accurate memos. Broader landscape queries return with competitor programs missing that the agent had named earlier in its own reasoning, quoted evidence attached to the wrong study, and cross-document fields partially filled. The MLflow trace shows window utilization rising toward the cap before drafting begins. One agent runs throughout.

What change should the AI engineer make to the agent architecture's boundaries?

A context engineer is debugging a long-running data pipeline orchestration agent that runs multi-step workflows using registered Databricks tools such as run_job, query_catalog, and validate_schema. After compaction occurs at turn 40, the agent starts re-invoking tools it successfully called earlier in the session and ignores results that downstream steps depend on. Inspection of the compacted context shows that the summarization prompt was instructed to retain only the final state of each pipeline stage. The current compaction prompt reads: ''Summarize the current pipeline state in a few sentences and include only what is necessary.''

Which revision to the compaction prompt addresses the coherence failure while keeping token cost low?

A context engineer is building an AI application that tracks every prompt sent to a large language model, the retrieved context chunks passed to it, and the final generated response. The application must capture token usage, latency metrics, and user feedback scores associated with each inference run. The engineer needs a unified system that versions prompt templates alongside model artifacts, links evaluation results to specific runs, and provides a query interface so downstream dashboards can compare performance across experiment iterations, all without building a custom logging backend.

Which Databricks product should the context engineer use to meet these requirements?

A context engineer is reviewing a Databricks agent pipeline designed for a contract review assistant. Users submit queries such as ''Can I get out of this?'' and ''What does this cover?''. The pipeline retrieves syntactically valid contract clauses through a vector search index, but generated responses consistently address the wrong contractual context. Tracing logs confirm that the vector search index, prompt assembly, and LLM generation stages all execute without errors. The pipeline stages are: input ingestion, pre-retrieval enrichment, context retrieval, prompt construction, and response synthesis.

At which pipeline stage should the context engineer intervene to ensure that retrieval operates on a grounded representation of the user's contractual request?

A context engineer is configuring a Databricks agent for an automotive service center platform. The agent must serve service center staff who submit a vehicle identification number (VIN) and a reported symptom. The agent is expected to return a summary of the vehicle's operating profile, identify the applicable SOP procedure code for the symptom, and retrieve the narrative step-by-step service instructions for that procedure. The engineer has access to these data sources:

Unity Catalog Delta table named vehicle_profiles with columns vin, engine_type, and operating_spec.

Unity Catalog Delta table named symptom_sop_map with columns symptom_code and sop_procedure_code.

Unity Catalog Delta table named service_history with columns vin, service_date, and technician_id.

Databricks AI Search index built from scanned OEM service manuals containing narrative SOP instructions and safety warnings.

Which combination of context elements correctly supports the agent's task?