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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: Context Management & Reliability | 15% | - Production deployment considerations - Managing context windows and information flow - Evaluation and reliability strategies |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration |
| Topic 5: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability |
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NEW QUESTION # 163
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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?
Answer: D
Explanation:
Option B demonstrates the decision boundary Claude must learn. Carefully selected examples can show structurally similar code producing different outcomes based on project context--for example, an approved authentication wrapper versus an unsafe direct call, or a deliberate performance trade- off versus an accidental quadratic operation. These contrasts help Claude apply the underlying judgment to new code rather than merely memorizing prohibited phrases.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable methods for improving output accuracy and consistency. It recommends several examples that mirror the real use case and cover important edge conditions.
NEW QUESTION # 164
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation--for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field. Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?
Answer: D
Explanation:
Option A uses the limited review capacity as a risk-ranking mechanism rather than spending it uniformly. JSON Schema validation can prove that an output has the expected types and structure, but it cannot detect a semantically misplaced value such as a duration stored as an ingredient quantity. Field-level confidence can identify the exact parts of a record that warrant inspection, provided those scores are calibrated against labeled examples rather than trusted at face value. The labeled validation set must mirror the production distribution and include edge cases; Anthropic's evaluation guidance explicitly recommends task-specific evaluations, representative distributions, measurable criteria, and automated grading where possible.
Calibration then converts raw confidence into an empirically tested estimate of error risk, allowing reviewers to inspect the lowest- confidence records or fields first.
NEW QUESTION # 165
The synthesis agent receives summarized findings from the web search and document analysis agents, then passes a consolidated summary to the report generator. During testing, you discover the generated reports make factual claims without proper citations - the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What's the most effective approach to ensure proper source attribution in the final reports?
Answer: D
Explanation:
Structured outputs that keep summaries separate from source metadata preserve attribution throughout the agent workflow. This allows the synthesis and report generation stages to carry forward URLs, document names, page numbers, and other citation details without losing the link between claims and their original sources.
NEW QUESTION # 166
Which prompt revision is MOST likely to improve output quality?
Answer: A
Explanation:
High-quality prompts clearly specify the objective, necessary background information, operational constraints, and desired response format. Claude performs best when expectations are explicit and well structured.
NEW QUESTION # 167
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 used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?
Answer: D
Explanation:
Claude Code supports resuming a specific saved session by either its identifier or its assigned name. Because the engineer knows the target session is named auth-deep-dive , the appropriate command is:
claude --resume auth-deep-dive
Anthropic's CLI reference explicitly states that --resume can resume a session by ID or name and gives named-session usage in the same form. Resuming restores the relevant conversation history and accumulated context, including the prior analysis and files discussed during the investigation. ( https://docs.anthropic.com
/en/docs/claude-code/cli-reference )
Option B is incorrect because --continue resumes the most recent session in the current directory. The engineer has conducted three subsequent sessions, so it may open an unrelated investigation. Option A uses a flag that is not the documented Claude Code mechanism for selecting a prior session; --resume itself accepts the session identifier when an ID is used. Option C discards two hours of established context and unnecessarily repeats codebase exploration.
Named sessions are particularly useful when engineers alternate among multiple projects or parallel investigations. Assigning descriptive names allows the correct thread to be retrieved deterministically rather than depending on chronological recency.
Official references/topics: Claude Code Session Persistence, Named Sessions, --resume , --continue .
NEW QUESTION # 168
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