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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Developer productivity workflows - Claude Code usage and configuration |
| Topic 2: Agentic Architecture & Orchestration | 27% | - Selecting appropriate Claude architectures - Designing agentic systems and workflows - Agent coordination and orchestration patterns |
| Topic 3: Context Management & Reliability | 15% | - Evaluation and reliability strategies - Production deployment considerations - Managing context windows and information flow |
| Topic 4: Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Topic 5: Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration |
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NEW QUESTION # 54
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?
Answer: B
Explanation:
Temporal metadata lets the synthesis agent distinguish measurements from different time periods and interpret 18% in 2021 and 35% in 2024 as a trend rather than a contradiction.
NEW QUESTION # 55
A developer uses Claude Code to refactor a function during a development session. Before committing, the developer asks the same Claude session to review the code for issues. Later, a separate automated CI review catches several bugs that the same-session review missed. What best explains this discrepancy?
Answer: A
Explanation:
The key difference is reviewer independence. In the original session, Claude retains the reasoning, assumptions, implementation decisions, and intermediate conclusions that led to the refactor. That context can anchor the subsequent review toward defending or confirming the chosen approach instead of evaluating the resulting code independently. The separate CI review begins with fresh context and can assess the implementation solely against the code and review criteria.
Anthropic's Claude Code best-practices documentation explicitly states that fresh context improves code review because Claude is not biased toward code it has just written. It recommends a writer/reviewer pattern using separate sessions or a review subagent with an isolated context.
NEW QUESTION # 56
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?
Answer: C
Explanation:
Continue the agentic loop when stop_reason is tool_use, execute the requested tool, and return its result to Claude. Exit when Claude returns a terminal reason such as end_turn; other reasons like max_tokens require separate handling.
NEW QUESTION # 57
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.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got `2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?
Answer: D
Explanation:
An unchanged retry repeats the same task specification and therefore commonly reproduces the same invalid interpretation. The validator has generated precise corrective information--quantity requires a float, but the model returned the range string 2 to 3. Supplying that error in a follow-up turn converts a generic retry into an iterative repair operation.
Anthropic identifies iterative refinement as a method for detecting and correcting inconsistencies by feeding an earlier output back into a subsequent request with targeted instructions. Option A applies that pattern directly. Claude receives the invalid output, the exact Pydantic error, and an instruction to return a schema-compliant correction. The application should cap retries, retain the original source, and escalate cases that cannot be represented without information loss.
NEW QUESTION # 58
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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues:
(1) compound phrases like "Python and SQL" are sometimes kept as one
entry, sometimes split;
(2) implied but unstated skills occasionally appear in extractions;
(3) similar documents produce wildly different array lengths (5-10 vs
40+ entries).
Your prompt currently says "Extract all skills mentioned."
What's the most effective improvement?
Answer: B
Explanation:
The failure is caused by an underspecified extraction policy rather than an inadequate JSON structure. "Extract all skills mentioned" does not define whether coordinated phrases should be split, whether inferred capabilities qualify, or what level of specificity constitutes one skill. Few- shot examples can demonstrate all three decisions using concrete inputs and expected outputs.
Anthropic identifies examples as one of the most effective ways to improve output accuracy and consistency because they communicate behavior more precisely than abstract instructions alone.
Representative examples can show that "Python and SQL" becomes two entries, that an unstated skill must not be inferred from a job title, and that broad descriptions should not be decomposed into dozens of speculative micro-skills.
NEW QUESTION # 59
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