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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Token budget management and cost control - Context window optimization and prioritization - Context pruning and summarization strategies |
| Topic 2: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting - MCP tool, resource and prompt implementation |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Topic 4: Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting |
| Topic 5: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters |
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NEW QUESTION # 182
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 system has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence #90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high- confidence extractions.
Before deploying, what validation step is most critical?
Answer: D
Explanation:
An aggregate accuracy value can conceal severe performance disparities. A system may achieve 97% overall accuracy while performing poorly on a low-volume document type, a critical financial field, or a specific edge case. Automating outputs solely from the aggregate figure could therefore expose downstream systems to concentrated, high-impact errors.
Anthropic's evaluation guidance states that evaluations should be task-specific, reflect the real-world task distribution, and explicitly include edge cases. It also emphasizes multidimensional success criteria rather than reliance on a single global metric. ( https://docs.anthropic.com/en/docs/build-with-claude/develop-tests ) Option A applies those principles by stratifying performance according to document type and field. This reveals whether confidence is calibrated consistently and whether the proposed automation threshold remains safe for every operationally significant segment.
Option B is useful only after segment-level performance has been understood. Selecting a global threshold cannot correct a subgroup where confidence is systematically overstated. Option C is necessary governance work, but it treats the overall 97% result as though errors were uniformly distributed. Option D places unvalidated outputs into downstream systems and depends on passive error reporting, which may fail to detect silent corruption.
The correct deployment gate is therefore segmented validation, followed by threshold selection, downstream acceptance criteria, and a controlled pilot.
Official references/topics: Define Success Criteria; Task-Specific Evaluations; Edge-Case Coverage; Reliability Segmentation.
NEW QUESTION # 183
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 system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article-for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?
Answer: A
Explanation:
The schema already supports the correct representation of missing evidence: null . The remaining defect is behavioral. Claude must be explicitly instructed that absence of information is a valid outcome and that values may be populated only when directly supported by the supplied article.
Anthropic's hallucination-reduction guidance recommends explicitly permitting Claude to express uncertainty, grounding outputs in the source, and retracting claims that lack supporting evidence. It also recommends restricting the model to the provided documents rather than allowing unsupported external knowledge. ( https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option C translates those controls directly into the extraction contract: when no source evidence exists, the model returns null .
Option A may improve baseline performance but does not remove the architectural ambiguity that encourages completion of missing fields. Option B is counterproductive because non-nullable required fields force the model to provide values even when the article contains none. Structured validation would confirm that the response is syntactically valid while accepting fabricated content. Option D adds cost, latency, and another probabilistic model decision; a second model can repeat or endorse the original hallucination.
A robust implementation should supplement the instruction with source spans or quotations for populated fields and automated checks where feasible. Nevertheless, among the options, explicit null behavior is the direct and most effective correction.
Official references/topics: Reduce Hallucinations; External-Knowledge Restriction; Nullable JSON Schema Fields; Grounded Extraction.
NEW QUESTION # 184
Your test-generation process produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that verify only that functions do not throw exceptions, tests that duplicate existing coverage, or tests that ignore your team's fixture conventions. How should you reduce the rate of low-value tests being generated in the first place?
Answer: B
Explanation:
The failures reflect missing project-specific knowledge: Claude does not know which fixtures are preferred, what the existing suite already covers, or what the team considers meaningful behaviour. Option C provides this information as persistent project context before test generation begins. This changes generation quality at the source instead of filtering weak tests after spending tokens to produce them.
Anthropic's CLAUDE.md documentation recommends storing shared build and test commands, coding standards, architectural decisions, conventions, and common workflows in a project CLAUDE.md. Testing guidance can define required behavioural assertions, fixture selection rules, duplication checks, naming conventions, and representative examples of acceptable and unacceptable tests.
Option A avoids difficult directories rather than improving the model's understanding and sacrifices potentially useful automation. Option B may remove some weak tests, but it doubles model work and asks another probabilistic call to infer quality criteria that should have been stated explicitly. Option D treats line coverage as a quality metric even though a trivial test can increase coverage without validating meaningful behaviour, while a valuable regression test may cover already executed lines with better assertions. Persistent, concrete testing standards therefore offer the most direct and scalable improvement.
NEW QUESTION # 185
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 CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day using the synchronous API. Reviews are non-blocking-developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API for its 50% cost reduction.
What factor most determines whether batch processing is appropriate for this use case?
Answer: C
Explanation:
Option C identifies the fundamental trade-off introduced by batch processing: lower cost in exchange for asynchronous completion and potentially substantial latency. Anthropic states that most Message Batches complete within one hour, but results may become available only when all requests finish or after 24 hours, whichever occurs first. Therefore, the workflow must remain useful even if security findings arrive considerably later than they would through synchronous requests.
The reviews are explicitly non-blocking, so batch processing can be suitable if developers can still act on delayed findings through follow-up commits. Option B describes an implementation requirement because batch results may be returned out of submission order, but Anthropic provides a deterministic solution through each request's unique custom_id. Option A incorrectly focuses on near-instant feedback, which batch processing does not provide. Option D is not decisive because batches support independent Messages API requests containing system prompts, tool use, and multi-turn conversation content. The primary decision is therefore whether the maximum practical feedback delay is acceptable to the development workflow.
Anthropic Message Batches documentation
NEW QUESTION # 186
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: C
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.
Option B may postpone the failure but provides no durable guarantee for still-larger pull requests, and aggressively shortening descriptions may eliminate necessary evidence. Option C abandons machine- validated structure without reducing the amount of generated content. Option D deliberately suppresses medium- or low-severity findings and repeats an oversized request rather than addressing its scope.
Partitioning establishes predictable output bounds, supports targeted retries, retains every required finding category, and prevents a single truncated response from invalidating the complete review.
NEW QUESTION # 187
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