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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Improving Claude response quality and consistency - Structured output generation and validation |
| Topic 2: Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Claude Code usage and configuration - Integrating Claude Code into development processes |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Topic 4: Context Management & Reliability | 15% | - Production deployment considerations - Managing context windows and information flow - Evaluation and reliability strategies |
| Topic 5: Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
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NEW QUESTION # 96
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30-day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?
Answer: B
Explanation:
The document contains multiple factually valid values whose applicability depends on chronology and legal context. Collapsing those values into a single scalar field discards essential provenance. Option B corrects the data model by representing each term as a structured record containing the extracted value, source location, document or amendment identifier, and effective date.
Anthropic's Structured Outputs feature is designed for data-extraction use cases in which nested objects and arrays must conform to a defined JSON Schema. ( https://platform.claude.com/docs/en/build-with-claude
/structured-outputs ) Anthropic also recommends grounding factual outputs in direct source material and making claims auditable through supporting evidence. ( https://docs.anthropic.com/en/docs/test-and-evaluate
/strengthen-guardrails/reduce-hallucinations ) A provenance-aware schema applies both principles: it retains the original clause and the amendment instead of forcing Claude to resolve a potentially complex legal precedence question during extraction.
Option A is destructive because removing superseded text prevents auditing and may eliminate terms still relevant to earlier periods. Option C oversimplifies amendment logic; the newest document is not automatically controlling for every date, jurisdiction, or clause. Option D identifies risk but does not improve the extracted representation and unnecessarily sends all amendment cases to manual review.
After extraction, deterministic business logic can select the value effective on a requested date while retaining the complete contractual history.
Official references/topics: Structured Outputs; Nested Schema Design; Provenance and Source Grounding; Temporal Data Modeling.
NEW QUESTION # 97
After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?
Answer: A
Explanation:
Progressively summarizing older conversation turns condenses historical context while keeping recent interactions verbatim, reducing token load and maintaining coherence. This approach balances response speed, memory efficiency, and user experience by preserving essential context without requiring users to repeatedly provide information.
NEW QUESTION # 98
Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail. What is the most effective way to handle this?
Answer: B
Explanation:
Option A reduces the maximum output required from any single response while preserving the structured schema and complete severity range. The pipeline can partition files into coherent groups, execute bounded reviews, validate each returned array, and merge and deduplicate findings using stable fields such as file path, line number, category, and description.
Anthropic's stop-reason documentation confirms that max_tokens means generation reached the configured output limit and the response must be treated as incomplete. Structured output constraints can guarantee schema-valid generation when completion succeeds, but they cannot create unlimited output capacity. A large findings array can still exceed the available token budget.
NEW QUESTION # 99
During initial testing of the automated review pipeline, you notice that reviews of large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops--Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?
Answer: B
Explanation:
Option A is the only configuration that establishes both required limits inside Claude Code. The official CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode; Claude Code exits with an error when that limit is reached. It defines --max-budget- usd as the maximum dollar expenditure on API calls before execution stops, including applicable subagent expenditure.
These controls address different failure dimensions. The turn limit prevents an investigation from continuing through excessive read-search-analyze cycles, while the budget limit stops an invocation whose expensive turns consume the monetary allowance before reaching the turn ceiling. Supplying both therefore creates an effective per-run boundary.
NEW QUESTION # 100
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools (get_customer, lookup_order, process_refund, escalate_to_human). Your target is 80%+ first-contact resolution while knowing when to escalate.
After expanding the agent's MCP tools with delivery-specific capabilities (check_delivery_status, contact_driver, issue_credit, apply_promo_code, update_delivery_address, reschedule_delivery), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to
71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools-calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data.
Which approach structurally eliminates the semantic overlap identified in the logs as the error source?
Answer: A
Explanation:
Option B removes the ambiguity from the tool interface itself. The agent no longer needs to choose between separate functions that represent closely related business operations. Instead, a single compensation tool exposes an explicit action parameter, while the existing order lookup tool can optionally return delivery- tracking information through a clearly defined flag.
Anthropic's tool-design guidance recommends consolidating related operations into fewer, more capable tools when separate functions create unnecessary semantic overlap. Claude selects tools primarily from their names, descriptions, schemas, and the current task. When multiple tools appear capable of satisfying the same request, selection accuracy declines because the agent must infer distinctions that should have been made explicit in the interface design.
Option A adds routing complexity but leaves overlapping financial operations available within the same sub- agent. Option C reduces the number of tools loaded initially, but it does not eliminate the semantic duplication between compensation operations or overlapping lookup functions. Option D may improve behavior through examples, but it compensates for a poorly designed tool surface rather than correcting the root cause.
The consolidated schemas should use enums for supported actions, clearly define when each action applies, and document the returned fields. This creates a smaller and more deterministic agent-computer interface.
Official references/topics: Tool consolidation, semantic tool boundaries, action parameters, MCP tool- selection accuracy.
NEW QUESTION # 101
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