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A context engineer is developing a multi-agent research pipeline on Databricks. A coordinator agent divides an incoming scientific literature query into two parallel subtasks: a citation-retrieval agent and a methodology- analysis agent. Both agents independently receive only the original user query, produce their outputs, and return them to the coordinator, which then invokes a synthesis agent. The synthesis agent produces a final report that contradicts the citation-retrieval findings because it relies solely on the methodology-analysis output and has no visibility into retrieved citations.
Which two context propagation changes would prevent the conflicting outputs in the synthesis agent?
Choose two

• A. Update the synthesis agent's input schema to accept a structured context object containing both citation- retrieval results and methodology-analysis results as named fields.
• B. Replace the parallel dispatch pattern with a sequential chain so the methodology-analysis agent runs first, and the citation-retrieval agent runs afterward, without changing what context is forwarded to the synthesis agent.
• C. Configure the coordinator agent to merge outputs from the citation-retrieval and methodology-analysis agents into a single context payload before invoking the synthesis agent.
• D. Broadcast every agent's full raw interaction history, including intermediate prompts and tool calls, directly to the synthesis agent, allowing the LLM to self-structure citation results from methodology analysis.
• E. Configure the coordinator to forward only a high-level methodology summary into the synthesis agent's context while omitting the underlying citation-retrieval data, to keep the context window small.

A context engineer is designing an MCP server for a Databricks agent that connects to a catalog of 30 tools.
The server uses a progressive disclosure architecture to manage a strict token budget. At startup, the agent loads only the tool names and one-sentence descriptions. After receiving a user request to query a specific Unity Catalog table, the agent evaluates the catalog, identifies one tool as the likely match, and needs to move to the next stage of the architecture.
What should the agent do in the inspect layer?

• A. Load the full schemas for all 30 available tools so it can cross-validate the selected tool's parameters against similar tools before constructing the call.
• B. Call the selected tool using parameter values inferred from the short description, since the catalog layer has already confirmed the tool identity.
• C. Fetch the full schemas and documentation for the single identified tool, then use that retrieved schema to construct a valid tool call.
• D. Retrieve an enriched summary for the selected tool that includes usage examples and defers loading parameter types until the execution layer.

A context engineer is developing a Databricks legal research agent for an international financial services organization. The agent uses a DELTA_SYNC Databricks AI Search index containing regulatory compliance documents for the United States, United Kingdom, and European Union.
Each document chunk includes the following metadata:
jurisdiction: The regulatory jurisdiction to which the document applies.
document_status: Whether the document is active, superseded, or archived.
effective_date: The date on which the regulatory provision became effective.
document_id: The unique identifier of the source document.
The agent uses semantic similarity search with num_results=10 to retrieve regulatory evidence.
During MLflow evaluation, the engineer discovers that questions about current UK financial reporting obligations frequently retrieve superseded UK regulations and highly relevant US regulatory documents. The resulting answers are technically plausible but cite requirements that do not apply to the user ' s jurisdiction or requested reporting period.
The embedding model produces accurate semantic similarity scores, and increasing num_results does not improve regulatory correctness.
The engineer must preserve semantic retrieval while ensuring that only regulations applicable to the requested jurisdiction and reporting date are considered.
Which retrieval configuration should the context engineer implement?

• A. Increase num_results from 10 to 50 and apply a reranker so that the most semantically relevant regulatory documents appear first, regardless of their original jurisdiction.
• B. Apply metadata filters for the requested jurisdiction, active regulatory status, and applicable effective- date conditions as part of the AI Search query, then rank the eligible documents by semantic relevance.
• C. Retrieve all semantically relevant documents first and instruct the LLM through the system prompt to ignore regulations belonging to other jurisdictions or historical reporting periods.
• D. Replace semantic retrieval with full-text search so that exact jurisdiction names and regulatory terminology determine which documents are returned.

A data analyst is creating a Genie space for a hospital operations team that needs to analyze emergency department wait times, patient discharge rates, and nurse-to-patient ratios. The engineer has added the relevant tables, written general instructions describing the hospital operations domain, and configured sample questions. Stakeholders require any metric involving nurse-to-patient ratios to use the official staffing calculation defined by the compliance team, but Genie should still answer free-form questions about discharge rates and wait times using ad hoc SQL when no trusted asset covers the question.
Which approach should the engineer use to meet both requirements?

• A. Register example SQL queries as trusted assets for nurse-to-patient ratios, discharge rates, and wait time metrics so that every operational response earns the "Trusted" label and stakeholders can rely on all outputs.
• B. Add the nurse-to-patient ratio query as a sample question paired with the expected answer so analysts see the correct format and Genie uses the sample as a template when generating ratio queries.
• C. Register an example SQL query containing the compliance-approved nurse-to-patient ratio calculation as a trusted asset, and leave discharge rate and wait time queries unregistered so Genie generates ad hoc SQL for those questions.
• D. Add the compliance-approved nurse-to-patient ratio calculation to the general instructions as a text description so Genie applies the correct formula when generating ad hoc SQL for staffing questions.

A context engineer is developing a RAG pipeline for a pharmaceutical knowledge base. Document chunks from clinical trial summaries are stored in a Unity Catalog-governed Delta table with precomputed embedding vectors. Pure vector similarity search retrieves topically related chunks effectively but misses chunks that depend on precise drug compound names and dosage identifiers in the search string, because those tokens are underrepresented in the embedding space. The engineer wants to recover those exact-term chunks while preserving the semantic retrieval that already works, without standing up a separate keyword index or adding a downstream merge step.
Which configuration should the engineer use to close this retrieval gap?

• A. Query the index using approximate nearest neighbors (ANN), then filter the returned chunks to those containing the exact drug and dosage terms, resulting in a single ranked result set.
• B. Query the index using full-text search, then leverage the Databricks reranker with approximate nearest neighbors (ANN) to return a single hybrid ranking.
• C. Query the index with hybrid search, using approximate nearest neighbors (ANN) and full-text search in one call and returning a single fused ranking.
• D. Query the index using a filter with a LIKE operator in conjunction with approximate nearest neighbors (ANN) to return a single ranking while recovering the specific exact-term chunks being ignored in the original search.