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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability - Designing effective tools for Claude applications |
| Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Prompt design strategies - Improving Claude response quality and consistency |
| Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Designing agentic systems and workflows - Selecting appropriate Claude architectures |
| Context Management & Reliability | 15% | - Production deployment considerations - Evaluation and reliability strategies - Managing context windows and information flow |
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NEW QUESTION # 51
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the instruction: "Only flag critical issues that would definitely cause production failures. Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each finding tagged with metadata, and actionable.
Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?
Answer: D
Explanation:
Option B removes the prompt-level suppression responsible for the false negatives while preserving machine-readable metadata. Anthropic's current code-review prompting guidance warns that instructions such as "only report high-severity issues" or "be conservative" may be followed literally:
Claude can identify genuine defects during analysis but omit them from its output. Anthropic recommends requesting all findings and applying filtering separately.
Confidence and severity fields allow downstream code to apply adjustable thresholds without forcing the model to discard evidence during generation. A schema can require fields such as file, line, description, severity, confidence, evidence, and recommended action; Anthropic's Structured Outputs documentation supports enforcing such a response contract.
NEW QUESTION # 52
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.
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:
The Messages API exposes stop_reason specifically so the application can determine why Claude stopped generating and what action is required next. A value of tool_use means Claude is requesting one or more tools and expects the application to execute them and return corresponding tool_result blocks. A value of end_turn means Claude has naturally completed the response. ( https://platform.claude.com/docs/en/build-with-claude
/handling-stop-reasons )
Anthropic's agent-loop tutorial implements this as a loop that continues executing tools and returning results while stop_reason remains tool_use . Once the value changes, the response is treated according to the new terminal or continuation condition. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a- tool-using-agent ) Option A is unreliable because an assistant response can contain both text and tool-use blocks. Text alone does not prove that the workflow is complete. Option B requires the application to predict that no further tool call is needed, defeating Claude's ability to adapt from tool results. Option D is an important safety limit but is not the primary completion signal; an arbitrary maximum may terminate a valid workflow prematurely.
The harness should check stop_reason , process every requested tool call, append results to the conversation, and maintain a separate maximum-turn safeguard against runaway loops.
Official references/topics: Messages API stop reasons, tool-use loops, tool-result continuation, bounded orchestration.
NEW QUESTION # 53
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You've asked Claude Code to build a PDF report generation feature. The initial implementation queries the database correctly, but the output has formatting issues: table columns are too narrow causing content truncation, dates display without proper formatting, and page break handling is incorrect. You've noticed these issues interact-changing column widths affects how dates render, and page breaks depend on content height.
What's the most effective approach for iterating toward a working solution?
Answer: D
Explanation:
The defects are coupled, so changing all three simultaneously would make it difficult to determine which modification caused an improvement or regression. Option C establishes a controlled sequence: correct the foundational column geometry, verify the resulting layout, format dates within the stabilized columns, and finally tune page breaks using the resulting content heights.
Anthropic recommends tight feedback loops and early course correction. It also advises supplying Claude with an executable or observable verification mechanism, such as a test, build result, generated fixture, or screenshot comparison. Claude can then make a focused change, inspect the output, and iterate until that specific condition is satisfied. ( https://code.claude.com/docs/en/best-practices ) Option A discards useful context from the functioning database implementation. Option B changes multiple interacting variables in one pass, making failures harder to isolate. Option D provides a useful visual target but does not replace precise technical constraints or incremental verification.
Each stage should have explicit acceptance criteria-for example, minimum column widths, expected date strings, and page-break fixtures using short and long content. Once a stage passes, its test becomes a regression guard for subsequent changes.
Official references/topics: Incremental Refinement; Tight Feedback Loops; Observable Verification; Regression Control.
NEW QUESTION # 54
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning 45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate - it's forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows. What's the most effective approach to complete this investigation?
Answer: D
Explanation:
Subagents isolate detailed exploration in separate context windows and return concise findings to the main agent. This prevents further context degradation while preserving high-level coordination across test discovery and payment-flow tracing.
NEW QUESTION # 55
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness. Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR.
Which task decomposition pattern is most appropriate for this workflow?
Answer: A
Explanation:
Prompt chaining is appropriate because the workflow consists of predictable, fixed subtasks that apply to every pull request. The system can run a focused style-compliance analysis, then a security analysis, then a documentation review, and finally synthesize the three results into a consistent report.
Anthropic defines prompt chaining as decomposing a task into a sequence of steps in which each call handles a smaller component. It is recommended when a task can be cleanly divided into fixed subtasks, trading additional latency for better focus and accuracy. Intermediate checks can also be inserted between stages to confirm that each report section meets its requirements. ( https://www.anthropic.com/engineering/building- effective-agents ) Option A concentrates all considerations into one call, making it easier for one aspect to receive inadequate attention. Option B is unnecessary because orchestrator-workers is intended for complex work where the required subtasks cannot be predicted in advance. Here, the three review dimensions are already known and remain constant. Option D solves a different problem: routing is appropriate when inputs belong to distinct categories that require different downstream processes.
Although the three analyses could potentially be parallelized for lower latency, the option that accurately represents the stated repeatable decomposition is prompt chaining with final synthesis.
Official references/topics: Prompt Chaining, Fixed Subtasks, Intermediate Validation, Workflow Selection.
NEW QUESTION # 56
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