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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design, MCP, and Agent Context | 13% | - Model Context Protocol (MCP) implementation - Integrating agents with external tools and data sources - Tool calling context management |
| Topic 2: Multi-Agent and Long-Horizon Task Design | 13% | - Context coordination in multi-agent systems - Managing context across extended workflows - Evaluation and iteration of context engineering decisions - Governance: Unity Catalog integration, data quality, and access control |
| Topic 3: Knowledge Retrieval and Genie Configuration | 20% | - Retrieval strategies: static vs dynamic - Managing retrieval quality and avoiding pollution - Databricks Genie configuration and usage - Configuring Databricks AI Search for relevant context retrieval |
| Topic 4: Memory Architecture with Lakebase and MLflow | 18% | - Building persistent memory with Lakebase - Memory types: session, short-term, and long-term persistence - Tracking and evaluating memory configurations with MLflow - State management across agent sessions |
| Topic 5: Foundations of Context Engineering | 16% | - Context lifecycle and data flow - Role of context in agent reliability and performance - Core concepts of context engineering for AI agents |
| Topic 6: System Prompt and Instruction Design | 9% | - Designing effective system prompts and instructions - Prompt structuring techniques and best practices - Aligning agent behavior with intended goals |
| Topic 7: Context Compression and Compaction | 11% | - Trimming and compaction strategies - Context window constraints and limitations - Selective information retention techniques |
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NEW QUESTION # 36
A context engineer is building a compound AI system for a hospital network with Agent Bricks Custom Agents. The system answers clinician questions about a specific patient's medication history. Patient records are stored in a Delta table that is continuously updated as new prescriptions, refills, and discontinuations are recorded, and clinicians must see the most current state of a patient's record when they submit a query.
HIPAA's minimum necessary rule requires the system to retrieve only the requesting patient's records instead of searching across the broader patient population. End-to-end latency per query must stay under two seconds.
Which retrieval strategy should the context engineer use?
Answer: D
Explanation:
Just-in-time structured retrieval is the most appropriate strategy because the application requires current patient records, deterministic filtering, and restricted access to protected health information.
The medication history resides in a continuously updated Delta table. Consequently, the retrieval mechanism should access the authoritative dataset when the clinician submits the request, rather than relying on previously generated embeddings or an independently synchronized index.
Option B equips the agent with a tool that executes a parameterized query against the Delta table, filtering by the specific patient identifier.
This approach supports exact retrieval of prescriptions, refills, and discontinuation records without performing a semantic search across unrelated patients.
Databricks supports parameterized SQL execution, while Unity Catalog provides fine-grained security controls, including row filters and column masks.
Option A introduces an indexing layer that may have synchronization latency. Options C and D depend on precomputed embeddings and cannot inherently guarantee the latest medication state.
The implementation must also validate the clinician ' s authorization independently of the supplied patient identifier and apply appropriate HIPAA controls.
The two-second latency requirement must be validated through performance testing, including query execution, compute availability, and agent processing time. Selecting just-in-time retrieval does not independently guarantee that latency.
Official Databricks reference: Statement Execution API, Row filters and column masks, and HIPAA compliance.
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NEW QUESTION # 37
A context engineer manages a Genie space for a finance team. Users repeatedly ask the same shape of question 40 for FY26 Q2?" "...for the last quarter?". The SQL pattern Genie generates is identical across these questions (same JOIN, GROUP BY, and aggregation, with only the fiscal-period filter changing), and the team requires that responses to these recurring questions be labeled Trusted in the chat UI. The engineer must register an asset that reuses the same vetted SQL pattern with the user-supplied fiscal period.
Which curation choice should the engineer make?
Answer: A
Explanation:
A parameterized example SQL query is the appropriate choice when users repeatedly request the same analytical calculation with different filter values.
The finance team ' s requests share the same SQL structure, including table joins, regional grouping, pipeline aggregation, and relevant business filters. Only the fiscal-period value changes.
Rather than requiring Genie to regenerate this SQL independently for every question, the engineer can register a validated example query containing a typed parameter, such as :fiscal_period .
Databricks Genie supports query parameters with defined data types, including String, Date, Date and Time, Decimal, and Integer.
When a question supplies a fiscal period, Genie can associate the input with the parameter while preserving the approved SQL structure.
This approach also supports trusted responses when Genie uses the exact registered parameterized query in chat mode.
Option B allows Genie to compose surrounding SQL dynamically, meaning the complete query structure is not necessarily preserved. Option C relies on generated SQL derived from textual instructions. Option D requires maintaining separate static examples and does not generalize well to additional fiscal periods.
A parameterized example therefore provides stronger consistency, simpler maintenance, and controlled reuse of a verified calculation.
The engineer should validate fiscal-period formats and ensure that relative expressions such as " last quarter " resolve to the correct accounting period.
Official Databricks reference: Databricks - Tune Genie Agent quality, particularly Add Query Parameters and Trusted Assets.
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NEW QUESTION # 38
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?
Answer: D
Explanation:
The safest compaction strategy is to summarize the raw retrieval results while preserving the structured obligations and validation outcomes required by the remaining workflow.
Span 1 contributes 8,400 tokens of regulatory clause text. However, Span 2 has already transformed the relevant information into a compact, structured representation.
This intermediate record retains the extracted compliance obligations, regional applicability conditions, and source citations required for audit memo generation.
Span 3 adds validation results identifying whether each obligation satisfies the rules engine ' s requirements.
Consequently, Span 4 can synthesize the memo using the structured obligations and their corresponding validation outcomes without requiring all 22 raw excerpts in its active context.
Option A correctly identifies the largest context component that can be summarized while preserving the information required by the queued operation.
Option B removes regional applicability information that may not be fully represented in the rules-engine output. Option C risks eliminating essential structured details. Option D incorrectly assumes that the validation report contains all information necessary for memo synthesis.
Databricks MLflow traces distinguish retrieval, model, and tool spans and capture their respective outputs.
The engineer should preserve source identifiers and retain access to the original regulatory excerpts for independent audit verification or direct quotation.
Official Databricks reference: Span concepts and Debug and analyze your app with tracing.
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NEW QUESTION # 39
A context engineer is debugging a Databricks customer support agent that manages complex billing escalations.
The system prompt instructs the agent to:
Strictly apply the company's official refund policy.
Treat entries from the "Billing-Policies" tool as authoritative.
Treat FAQ snippets and prior chat history as non-binding guidance only.
In one multi-step trace, the engineer observes the following:
Step 1: The agent calls the "Billing-Policies" tool and retrieves the official refund policy, which clearly states that chargebacks filed more than 90 days after purchase are not eligible for refunds.
Step 2: The agent calls a "Customer-FAQ-Search" tool that returns a community-authored FAQ thread. A top- voted but outdated and unofficial answer incorrectly claims that chargebacks are fully refundable regardless of when they are filed, and the agent summarizes this in the running conversation as an established fact.
Step 3: In a later turn, the customer describes a chargeback dispute from 18 months ago. The agent reasons from its own earlier summary, approves a full refund, and makes no reference to the official policy section excluding cases older than 90 days, even though that policy text remains in the context.
Step 4: In subsequent similar cases, the agent repeatedly approves refunds for old chargebacks, citing its prior summary as justification each time.
Which context failure mode describes what is occurring in this trace?
Answer: D
Explanation:
Context poisoning occurs when erroneous or untrustworthy information becomes incorporated into an agent ' s working context and subsequently influences its decisions as though it were established truth.
In this scenario, the failure originates at Step 2. The agent encounters an outdated, community-authored FAQ that contradicts the authoritative Billing-Policies tool. Rather than preserving the distinction between authoritative and non-authoritative information, the agent incorporates the incorrect refund statement into its conversation summary.
At Step 3, the agent relies on that contaminated summary to approve an ineligible refund. Step 4 establishes the defining characteristic of context poisoning: the incorrect information is repeatedly reinforced through subsequent decisions.
Context distraction would involve excessive context degrading attention. Context confusion would arise from irrelevant material being misapplied. Context clash concerns conflicting information or instructions. Although contradictory information exists here, repeated reliance on an established false statement makes poisoning the more precise diagnosis.
The appropriate remediation is to enforce source-authority rules during context construction, prevent unverified statements from being promoted into persistent summaries, and validate refund decisions against the authoritative policy.
Databricks MLflow Tracing supports this investigation by recording intermediate retrieval operations, tool responses, and model decisions, allowing engineers to identify where incorrect information entered and propagated through the workflow.
Official Databricks reference: Databricks MLflow 3 - Tracing overview and Debug and analyze your app with tracing.
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NEW QUESTION # 40
A context engineer is debugging a multi-agent pipeline on Databricks in which a routing agent, a retrieval agent, and a summarization agent work together to answer complex insurance claims queries. Each agent maintains its own session state, and the agents communicate through a shared message bus. All agents retrieve documents from the same current, up-to-date Databricks AI Search index. The routing agent classifies a claim as high-priority, but the final output contains a low-priority summary that contradicts the routing agent's classification. Claims adjusters report contradictory recommendations in final outputs, and pipeline reliability scores have dropped significantly over the past week.
Which underlying failure mode is most likely causing the conflicting decisions between agents?
Answer: A
Explanation:
The most likely failure is inconsistent context propagation across independently executing agents.
The routing agent establishes that the insurance claim is high-priority, but the summarization agent subsequently produces an incompatible low-priority assessment.
Although all three agents access the same document index, access to common source documents does not guarantee that they share the same operational state or previously established decisions.
Option B identifies the missing architectural requirement: an authoritative shared representation of claim severity and other critical workflow decisions.
The coordinator should preserve the routing agent ' s classification in a structured state object and explicitly transmit it to downstream agents.
For example, the shared payload could contain the claim identifier, authoritative priority, supporting evidence, classification timestamp, and decision provenance.
The summarization agent should treat that validated classification as an established workflow result rather than independently reassessing severity without authorization.
Option A conflicts with the stated availability of current source data and assumes retrieval differences not established by the scenario. Options C and D describe possible implementation defects, but there is insufficient evidence of event-ordering failures or context truncation.
Databricks supports multi-agent orchestration and persistent agent state. These capabilities can be combined to preserve authoritative decisions across agent boundaries.
Official Databricks reference: Build a multi-agent system on Databricks Apps, Agent state and memory, and Core concepts for agent observability.
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NEW QUESTION # 41
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