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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration |
| Topic 2: Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Topic 3: Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications |
| Topic 4: Context Management & Reliability | 15% | - Production deployment considerations - Evaluation and reliability strategies - Managing context windows and information flow |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Selecting appropriate Claude architectures - Designing agentic systems and workflows - Agent coordination and orchestration patterns |
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NEW QUESTION # 133
Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?
Answer: D
Explanation:
The problem is an undefined relevance threshold. Claude is finding technically valid observations, but the prompt does not clearly distinguish actionable defects from acceptable conventions and low-value style preferences. Option A establishes explicit positive and negative reporting criteria, allowing the model to apply the team's actual definition of a useful finding during generation.
Anthropic's prompt-engineering guidance emphasizes clear, specific instructions and well-defined success criteria. The managed Code Review documentation similarly recommends defining skipped categories, generated paths, severity rules, and evidence requirements. Reporting "bugs affecting correctness or security" while excluding "formatting handled by CI and documented local conventions" is substantially more precise than asking the model to be generally conservative.
NEW QUESTION # 134
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 the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~8,000 lines total). What's the most effective next step for building understanding while managing context constraints?
Answer: D
Explanation:
This top-down approach establishes the caching abstraction first, then follows only the relevant implementations. It builds architectural understanding without loading all 8,000 lines or relying on narrow keyword matches that may omit important control flow. Anthropic recommends scoping exploration to task-relevant code to avoid filling the context window with unnecessary file reads.
NEW QUESTION # 135
Your post_content tool requires user confirmation before publishing. The current workflow displays "Ready to post to social media. Confirm?" and analytics show users approve 98% of requests within 2 seconds. Post-mortems reveal incidents where posts went to wrong accounts, were scheduled for wrong times, or contained errors - all confirmed by users without catching the mistakes. How should you redesign the confirmation workflow?
Answer: D
Explanation:
A confirmation step must present the specific action details the user is approving. Showing the full post content, target account, scheduled time, and platform helps users detect errors before execution, making confirmation meaningful rather than a generic approval click.
NEW QUESTION # 136
What is the PRIMARY purpose of few-shot prompting?
Answer: C
Explanation:
Few-shot prompting provides representative examples that illustrate the desired task and response style. Claude learns from these examples within the prompt, improving consistency without requiring model retraining.
NEW QUESTION # 137
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.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (85%) also contain errors--cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?
Answer: D
Explanation:
Stratified random sampling provides both an ongoing quality-control mechanism and an unbiased measurement framework. By reviewing a fixed proportion of high-confidence outputs across meaningful strata--such as document type, field, source format, and business risk--the organization can estimate the residual error rate, compare performance across releases, and discover failure patterns that were not anticipated when existing rules were designed.
Anthropic recommends measurable success criteria and evaluations that mirror the real-world task distribution, including edge cases. Evaluation volume and repeatability are important because improvements must be demonstrated empirically rather than inferred from isolated examples. A weekly sampling program creates a stable benchmark and allows confidence intervals, trend analysis, regression detection, and error-taxonomy updates.
NEW QUESTION # 138
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