A context engineer is developing a Databricks agent for a retail inventory management system. The system prompt and tool descriptions consume 1,800 tokens of a 4,096-token context window, and the engineer reserves 1,000 tokens for the user turn and model response. The remaining tokens are used for few-shot examples, each costing approximately 250 tokens, so the engineer includes exactly five examples.
The agent uses three tools: inventory_lookup, reorder_check, and supplier_lead_time.
Six candidate examples are available:
Example 1: A warehouse manager asks whether a depleted SKU needs reordering; the agent calls inventory_lookup, confirms depletion, calls reorder_check, and returns a binary reorder decision.
Example 2: A procurement officer asks when a low-stock item can be replenished; the agent calls reorder_check, calls supplier_lead_time, and returns a replenishment recommendation.
Example 3: A store associate asks for the current stock count of a single product; the agent calls inventory_lookup and returns a scalar quantity.
Example 4: A store associate asks for the current stock count of a single product; the agent calls inventory_lookup and returns a structured status object containing the quantity and a restocking flag.
Example 5: A store associate asks whether a forecasted shortfall requires immediate action; the agent calls inventory_lookup, compares against minimums, calls reorder_check, and returns a binary reorder decision.
Example 6: A logistics coordinator asks how long a specific supplier takes to fulfill an order; the agent calls supplier_lead_time and returns a scalar lead-time value.
Which example should the engineer omit to preserve the token budget while maximizing behavioral coverage?

• A. Example 6, because supplier_lead_time is already invoked in Example 2, and a standalone lead-time lookup does not add new tools to the demonstrated call space.
• B. Example 1, because the same tools are exercised across Examples 2 and 5, making its specific decision path redundant.
• C. Example 3, because another example in the set covers the same user role and tool path, but the output of the other example is richer.
• D. Example 5, because its forecasting logic duplicates the threshold-check pattern already established in Example 1, and its user role (store associate) appears elsewhere in the set.

A context engineer is examining an MLflow trace for a Databricks compliance agent mid-workflow on a quarterly regulatory review for a regional bank. The agent's design pattern is to retrieve regulatory clauses, extract structured obligations with regional metadata and source citations, validate each obligation through the rules engine, and synthesize the audit memo from the structured intermediate plus the validation outcomes.
The context window is nearing the compaction threshold, and compaction will fire before the workflow is complete.
The trace includes three executed spans and one queued span:
Span 1 (RETRIEVER): Retrieved 22 regulatory clause excerpts from the compliance vector index. Output: 22 documents (8,400 tokens) of raw clause text with metadata for source URI and section ID.
Span 2 (LLM/CHAT_MODEL): Produced a structured intermediate (410 tokens) containing three extracted compliance obligations, each annotated with a regional applicability tag describing how the bank's state-level regulations modify the federal requirement and a source citation pointing back to the originating clause.
Span 3 (TOOL): Ran the three obligations through the rules engine and returned a structured pass/fail report (280 tokens) with one outcome entry per obligation. Each entry contains the obligation ID, the pass/fail value, and a brief explanation of the rule that determined the outcome.
Span 4 (LLM/CHAT_MODEL, queued): Will draft the compliance memo for the bank's audit team.
Which content from the executed spans can the compactor safely summarize without affecting span 4's execution?

• A. All three executed spans, because the default compaction summary captures the agent's state and findings, and span 4 will draft the memo from the digest, which preserves the agent's analysis at the level needed for synthesis.
• B. The 22 raw clause excerpts from span 1, because their content has been transferred forward by span 2 into the structured intermediate (with regional applicability tags and source citations preserved) that the memo synthesis works from.
• C. The structured intermediate output from span 2, because span 1's raw regulatory clauses are the canonical source preserved verbatim for the memo's direct source quotations and audit traceability, while span 2's extracted obligations are intermediate analysis, the rules engine has already factored into the pass/fail report.
• D. The 22 raw clause excerpts from span 1 and the structured intermediate output from span 2, because everything that has been processed by a downstream operation is redundant in the active context; the pass/fail report from span 3 is the only output still doing work for the queued span.

A context engineer runs a finance Q & A agent on Databricks. The agent invokes a Genie space backed by tables in the finance.silver schema, and Genie chooses which table to query for each user questions by reading Unity Catalog table and column metadata. Two tables exist in the schema: revenue and revenue_gross.
MLflow evaluation runs show the Correctness scorer flagging frequent failures in which Genie queries revenue_gross when the user asks about net revenue, and queries revenue when the user asks about gross figures. Both tables are in active production use and must remain available to the Genie space, and the same disambiguation should be usable by other Genie spaces and BI consumers building against the same schema.
Which intervention resolves this failure?

• A. Apply a Unity Catalog certified-status tag to revenue and a deprecated-status tag to revenue_gross so that downstream consumers treat revenue as the authoritative source.
• B. Apply a custom Unity Catalog tag categorizing one table as the primary revenue source and the other as a secondary view, so Genie's tool selection prefers the primary.
• C. Add a Genie space instruction at the space level stating which table to use for net-revenue questions and which to use for gross-revenue questions, so Genie applies the rule when routing each user question.
• D. Add COMMENT text on both tables and their columns in Unity Catalog that clearly distinguishes the two tables - what each represents, the units, and the basis, so Genie reads the disambiguating descriptions when routing each user question.

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?

• A. A system prompt scoping the agent as an automotive service assistant; explicit tool-use instructions mapping service_history to VIN-based operating profile lookups and symptom_sop_map to symptom- to-procedure-code resolution; and the Databricks AI Search index for retrieving narrative OEM service instructions.
• B. A system prompt scoping the agent as an automotive service assistant; explicit tool-use instructions mapping vehicle_profiles to VIN-based operating profile lookups; and the Databricks AI Search index for both procedure code resolution and narrative OEM service instructions, relying on semantic search to surface structured codes from manual text.
• C. A system prompt scoping the agent as an automotive service assistant; tool-use instructions mapping vehicle_profiles to VIN-based operating profile lookups and symptom_sop_map to symptom-to- procedure-code resolution; and the Databricks AI Search index for retrieving narrative OEM service instructions.
• D. A system prompt scoping the agent as an automotive service assistant; a generic instruction to query available structured Delta tables for any structured lookup based on provided identifiers; and the Databricks AI Search index for retrieving narrative OEM service instructions.