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| Section | Weight | Objectives |
|---|---|---|
| System Prompt and Instruction Design | 9% | - Designing effective system prompts and instructions - Prompt structuring techniques and best practices - Aligning agent behavior with intended goals |
| Knowledge Retrieval and Genie Configuration | 20% | - Managing retrieval quality and avoiding pollution - Databricks Genie configuration and usage - Retrieval strategies: static vs dynamic - Configuring Databricks AI Search for relevant context retrieval |
| Context Compression and Compaction | 11% | - Selective information retention techniques - Trimming and compaction strategies - Context window constraints and limitations |
| Multi-Agent and Long-Horizon Task Design | 13% | - Evaluation and iteration of context engineering decisions - Context coordination in multi-agent systems - Governance: Unity Catalog integration, data quality, and access control - Managing context across extended workflows |
| Tool Design, MCP, and Agent Context | 13% | - Integrating agents with external tools and data sources - Model Context Protocol (MCP) implementation - Tool calling context management |
| 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 |
| Foundations of Context Engineering | 16% | - Context lifecycle and data flow - Core concepts of context engineering for AI agents - Role of context in agent reliability and performance |
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NEW QUESTION # 30
An AI engineer is auditing a Databricks compliance agent that has handled 65 conversation turns for a quarterly financial audit. The context window is at 92% capacity, and several audit findings remain open. The engineer wants to recover space by clearing specific raw tool outputs from the message history without compacting the conversation, and is reviewing four tool_result blocks. They also want to minimize the risk of losing information the agent still relies on.
The history contains:
A ledger reconciliation output at turn 7 returning 8,800 tokens of raw transaction rows from a read-only general ledger. The agent calculated aggregated taxable and non-taxable totals from these rows and passed them to the tax classification call at turn 10. The agent's later replies and tool calls do not quote any underlying transaction rows.
A tax classification output at turn 10 returning 5,200 tokens of per-line bucket breakdowns across 312 transaction lines. The agent extracted the bucket totals and passed them to the compliance flag call at turn 13.
The agent's later replies and tool calls do not quote any per-line bucket assignments.
A compliance flag output at turn 13 returning 95 tokens with three flag codes and one-sentence explanations.
The agent quotes specific flag codes and explanations from this output in turns 24, 38, and 61, incorporating them into the audit findings it is currently drafting.
A vendor master lookup output at turn 19 returning 3,400 tokens of supplier records and tax IDs from a read- only vendor master database. The agent quotes a vendor name and tax ID from this output in turns 41 and 57.
Which tool outputs should the engineer clear?
Answer: B
Explanation:
Selective removal of obsolete tool outputs is an effective context-management technique when their relevant information has already been incorporated into downstream results.
The ledger reconciliation output contains 8,800 tokens of raw transaction data. The agent has already extracted the required aggregates and passed them to the tax classification operation.
Similarly, the tax classification output contains 5,200 tokens of detailed per-line assignments, but subsequent processing uses the extracted totals rather than the original line-level records.
Option A removes these two large intermediate outputs, recovering approximately 14,000 tokens while preserving the downstream compliance results.
The compliance flag output must remain accessible because the agent repeatedly references its codes and explanations in active audit findings.
The vendor master output also contains information referenced in subsequent analysis. Removing it without preserving the necessary evidence could compromise audit traceability.
Option B retains unnecessary historical data. Option C removes potentially necessary vendor evidence.
Option D preserves an upstream output that the scenario explicitly indicates is no longer referenced.
Databricks MLflow Tracing records tool inputs, outputs, and execution relationships. Engineers can examine these traces before clearing context to determine which outputs remain operationally necessary.
Raw evidence should remain available in governed storage or trace records even when removed from the model ' s working context.
Official Databricks reference: Span concepts, Tracing overview, and Debug and analyze your app with tracing.
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NEW QUESTION # 31
A context engineer is investigating why a Genie space serving the sales operations team gives inconsistent answers to revenue questions. When business users ask the same question across different sessions, for example, "What was Q3 EMEA revenue?", Genie returns different figures. The space is configured over a Unity Catalog managed table of transaction-level data.
On reviewing the space, the engineer finds:
The underlying table has comprehensive table and column comments.
The space's text instructions describe how revenue should be computed, including currency conversion against a daily FX-rate table, accrual-timing rules, and intercompany-elimination logic.
The space includes several sample questions covering quarterly and regional revenue patterns.
In the query history, Genie composes a new SQL statement for the revenue calculation in each session, and the FX-conversion and elimination steps differ from run to run.
Which change will most reliably stabilize the space's answers to this team's revenue questions?
Answer: B
Explanation:
A parameterized Unity Catalog SQL function provides the strongest mechanism for stabilizing complex, business-critical calculations across Genie sessions.
The underlying issue is that Genie independently generates SQL for each question, resulting in variations in currency conversion, accrual timing, and intercompany elimination.
Although comprehensive metadata and instructions improve query generation, they do not guarantee that a complex financial calculation will be reproduced identically on every request.
Option A encapsulates the approved revenue-calculation logic within a reusable SQL function.
The function can accept parameters representing fiscal periods, regions, or other approved filters while maintaining the prescribed accounting transformations.
Databricks Genie supports Unity Catalog SQL functions as trusted assets. Such functions are particularly appropriate for complex calculations that should not be independently reconstructed or modified during question answering.
Option B provides a reference query, but Genie may still generate variations when adapting that query to new requests. Option C offers further natural-language guidance without enforcing calculation consistency. Option D does not provide the same explicit executable boundary as a registered SQL function.
Access to the function must also be governed through the appropriate Unity Catalog permissions, including EXECUTE .
This architecture centralizes financial logic, improves maintainability, and reduces the likelihood of inconsistent revenue figures across sessions.
Official Databricks reference: Tune Genie Agent quality - Trusted Assets and How Genie Uses SQL Functions.
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NEW QUESTION # 32
A context engineer is developing a Databricks agent for a financial research platform that answers analyst queries using a vector search index over earnings reports. The agent uses a 180-token system prompt, a 250- token conversation history window, and a RAG pipeline that retrieves 8 document chunks at runtime. Each chunk contains 550 tokens, contributing 4,400 tokens of retrieved context. The model's context window is
8,192 tokens, leaving just 3,362 tokens for the analyst query and model response. Profiling shows that the retrieved chunks consistently contain repetitive regulatory boilerplate and duplicated table headers that are rarely cited in the model's final answers.
Which two changes should the context engineer make to reduce the token footprint of retrieved context without discarding relevant financial content?
Choose two
Answer: C,D
Explanation:
The optimal approach combines retrieval-result reduction with improved document preprocessing, directly addressing the two factors responsible for excessive context consumption.
Option A reduces the number of retrieved chunks from eight to three. At 550 tokens per chunk, this decreases the retrieved context from 4,400 tokens to 1,650 tokens, representing a 62.5% reduction.
Applying a reranker helps identify which retrieved passages are most relevant to the analyst ' s question, improving precision when fewer chunks are included in the generation prompt.
Option C targets unnecessary information within individual chunks. Semantic chunking preserves meaningful financial passages, while removing duplicated headers and repetitive regulatory text improves information density.
With eight chunks averaging 150-200 tokens, retrieval would contribute approximately 1,200-1,600 tokens rather than 4,400.
Option B can increase result diversity through maximal marginal relevance, but maintaining the same number and size of chunks does not directly reduce token consumption. Option D improves retrieval representations without necessarily reducing injected context. Option E increases rather than reduces context usage.
Databricks recommends optimizing retrieval counts, reranking, chunk boundaries, and source-document preparation. Both selected changes should be evaluated for answer correctness and evidence recall, ensuring that compression does not eliminate relevant financial disclosures.
Official Databricks reference: Databricks - AI Search retrieval quality guide, Build an unstructured data pipeline for RAG, and Improve RAG application quality.
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NEW QUESTION # 33
A context engineer is developing an agent to support legal teams reviewing multi-part pharmaceutical trial protocols. Each protocol document includes a narrative background section, nested dosage tables with footnote qualifiers, cross-referenced section identifiers, and figure captions describing study design diagrams.
The agent must answer precise questions such as which protocol section governs a specific dosage threshold and what footnote conditions apply to that threshold.
Available data sources include:
A PDF binary store in Unity Catalog Volumes.
A Delta table of plain-text chunks extracted from the protocols, each tagged with page number and document identifier, indexed in a Databricks AI Search index.
A Delta table of protocol structure metadata at the page level, containing table summaries, figure captions, and section hierarchy, accessible through Unity Catalog functions.
Which context elements should the context engineer include to answer structural and footnote-level questions accurately?
Answer: B
Explanation:
The optimal solution combines semantic retrieval with targeted structural metadata enrichment, preserving both retrieval efficiency and document-context accuracy.
Pharmaceutical trial protocols contain information whose meaning depends on document organization. A dosage threshold may appear within a nested table, while its governing conditions are defined through footnotes, section references, or associated captions.
Plain-text vector retrieval can identify semantically relevant passages but may not adequately preserve relationships between tables, section headings, and footnote qualifiers.
Option D introduces a two-stage retrieval approach.
First, Databricks AI Search identifies relevant protocol chunks using semantic similarity. The returned document identifiers and page numbers establish the scope for additional retrieval.
Second, a Unity Catalog function retrieves the corresponding structural metadata, including table summaries, section hierarchy, and figure context. The agent can then interpret the retrieved passage within its original document structure.
Option A unnecessarily loads the entire metadata collection, increasing token usage. Option B introduces potentially expensive runtime document extraction despite the availability of preprocessed resources. Option C ignores structural information essential to the task.
Databricks recommends preserving structural metadata during document preprocessing and using it to improve retrieval grounding.
For strict footnote accuracy, the indexed content must also preserve the actual footnote text or provide a targeted source-document lookup.
Official Databricks reference: Build an unstructured data pipeline for RAG and Intelligent document processing.
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NEW QUESTION # 34
A context engineer is examining MLflow experiment tracking results comparing two system prompt configurations for a Databricks agent designed to assist with contract review. Configuration P uses a role declaration, a full task instruction, and a dedicated edge-case handling section that explains in prose how the agent should respond to ambiguous or overlapping clause types, averaging 205 tokens per call and 81% clause-classification accuracy. Configuration Q retains the role declaration, removes the prose edge-case handling section, shortens the task instruction to a concise directive, and adds three few-shot examples, each showing how the agent should classify a distinct edge-case clause type, averaging 340 tokens per call and
93% clause-classification accuracy. The token budget increases by 135 tokens per call between Configuration P and Configuration Q. Latency rises from 290 ms to 380 ms.
Which conclusion is supported by these experiment tracking results?
Answer: D
Explanation:
Configuration Q demonstrates a substantial improvement in classification performance, increasing accuracy from 81% to 93%, representing a gain of 12 percentage points.
The principal instructional change is the introduction of three few-shot examples demonstrating how ambiguous and overlapping contract clauses should be classified.
Few-shot examples provide concrete demonstrations of expected behavior. They are particularly valuable when a task contains boundary cases that are difficult to communicate through abstract prose instructions alone.
Databricks guidance recommends incorporating representative examples, particularly for query types on which an application performs poorly. Consequently, the edge-case examples are the strongest plausible explanation for the observed improvement.
The additional 135 tokens and 90 milliseconds should be assessed against deployment quality requirements.
For legal contract classification, the accuracy improvement may justify these costs, but the business trade-off cannot be established from accuracy alone.
There is an important experimental limitation: Configuration Q simultaneously changes the instruction length, removes explanatory prose, and adds examples. Therefore, the results do not establish that few-shot examples independently caused the improvement.
A controlled MLflow evaluation varying these components separately would be necessary to confirm causality.
Official Databricks reference: Databricks - Evaluate and compare prompt versions, Manage your agent ' s prompts, and Improve RAG chain quality.
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NEW QUESTION # 35
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