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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters |
| Topic 2: Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement |
| Topic 3: Context Management & Reliability | 15% | - Context window optimization and prioritization - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Token budget management and cost control |
| Topic 4: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Tool distribution and permission controls |
| Topic 5: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - System prompt design and persona alignment |
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NEW QUESTION # 154
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 extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30-day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?
Answer: C
Explanation:
The document contains multiple factually valid values whose applicability depends on chronology and legal context. Collapsing those values into a single scalar field discards essential provenance. Option B corrects the data model by representing each term as a structured record containing the extracted value, source location, document or amendment identifier, and effective date.
Anthropic's Structured Outputs feature is designed for data-extraction use cases in which nested objects and arrays must conform to a defined JSON Schema. ( https://platform.claude.com/docs/en/build-with-claude
/structured-outputs ) Anthropic also recommends grounding factual outputs in direct source material and making claims auditable through supporting evidence. ( https://docs.anthropic.com/en/docs/test-and-evaluate
/strengthen-guardrails/reduce-hallucinations ) A provenance-aware schema applies both principles: it retains the original clause and the amendment instead of forcing Claude to resolve a potentially complex legal precedence question during extraction.
Option A is destructive because removing superseded text prevents auditing and may eliminate terms still relevant to earlier periods. Option C oversimplifies amendment logic; the newest document is not automatically controlling for every date, jurisdiction, or clause. Option D identifies risk but does not improve the extracted representation and unnecessarily sends all amendment cases to manual review.
After extraction, deterministic business logic can select the value effective on a requested date while retaining the complete contractual history.
Official references/topics: Structured Outputs; Nested Schema Design; Provenance and Source Grounding; Temporal Data Modeling.
NEW QUESTION # 155
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.
Your extraction system uses tool use with a JSON schema containing 12 fields and detailed descriptions, totaling approximately 2,500 tokens for the complete tool definition. Processing documents under 150,000 tokens yields 98% accuracy. For documents between 175,000 and 190,000 tokens, accuracy drops to 71%, with information from the final third consistently missed. The model's context window is 200,000 tokens.
What is the most likely cause?
Answer: B
Explanation:
Option D identifies the dominant capacity problem. Anthropic states that everything sent in a request counts toward the context window : the system prompt, messages, documents, tool results, tool definitions, and the output Claude generates. A 175,000-190,000-token document is therefore not the complete input. Adding a
2,500-token tool schema, system instructions, wrappers, and the reserved output budget can push a nominally sub-200,000-token document close to or beyond the effective limit. Anthropic also warns that more context is not automatically better and that accuracy and recall can degrade as token count grows, a behavior described as context rot. That combination explains why short documents remain accurate while long documents lose information. Option A predicts a problem independent of document length, contradicting the measurements.
Option B is an unsupported generalization and does not account for tool-definition overhead. Option C recognizes long-context degradation but ignores the concrete evidence that hidden prompt components consume the same window. The correct response is to count tokens for the complete request before submission, reserve output capacity, and chunk or retrieve relevant sections when the total approaches the model's supported window.
NEW QUESTION # 156
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: C
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 # 157
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 must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?
Answer: B
Explanation:
A tool definition converts the desired extraction structure into an explicit machine-readable contract. Claude returns the event information inside a tool_use block, with the tool arguments corresponding to the properties defined by the tool's input_schema . Anthropic specifies that custom tool parameters are described using JSON Schema, allowing the application to extract the structured arguments directly rather than attempting to recover JSON from ordinary prose. For current implementations, adding strict: true to the tool definition provides guaranteed conformance of tool-call inputs to the declared schema. ( https://docs.anthropic.com/en
/docs/agents-and-tools/tool-use/implement-tool-use )
Options A, B, and D remain prompt-based formatting techniques. They may improve the probability of valid JSON, but none creates the same schema-enforced interface. Prefilling an opening brace constrains the beginning of the response without guaranteeing valid field names, required properties, or data types. Retry logic detects failures only after generation and adds latency. Detailed formatting instructions can still produce malformed or structurally incorrect output.
Anthropic now also provides Structured Outputs for direct, schema-validated JSON responses. Within the options presented, however, a schema-defined tool is the only approach that establishes an explicit structured- output boundary rather than relying primarily on text-generation compliance. ( https://docs.anthropic.com/en
/docs/test-and-evaluate/strengthen-guardrails/increase-consistency )
Official references/topics: Tool Definitions, JSON Schema Input Contracts, Strict Tool Use, Structured Outputs.
NEW QUESTION # 158
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 submits two requests:
* Request A: "Rename the getUserData function to fetchUserProfile everywhere it's used."
* Request B: "Improve error handling throughout the data processing module-add try/catch blocks, meaningful error messages, and ensure failures don't silently corrupt data." For which request does specifying an explicit multi-phase workflow (such as analyze # propose # implement with review) most improve outcome quality?
Answer: B
Explanation:
Request B benefits substantially more from an explicit multi-phase workflow because it is broad, judgment- intensive, and likely to affect multiple execution paths. Improving error handling requires the agent to identify current failure modes, determine which exceptions should be caught, preserve useful diagnostic information, prevent partial state corruption, and verify that the resulting behavior remains consistent across the module.
An analyze # propose # implement # review sequence separates those concerns and creates checkpoints before consequential edits are made.
Request A is comparatively deterministic. The function and target name are already defined, and the task primarily requires locating references, applying the rename, and running tests or static analysis. Anthropic's Claude Code guidance states that planning is most useful when the approach is uncertain, the work spans multiple files, or the developer is unfamiliar with the affected code. For a small change that can be described as a straightforward diff, direct implementation is usually preferable. ( https://code.claude.com/docs/en/best- practices ) Anthropic also characterizes prompt chaining as appropriate when a task can be divided into fixed stages and additional latency is justified by increased accuracy. Request B fits that pattern because analysis and review materially reduce the risk of introducing swallowed exceptions or inconsistent recovery behavior. (
https://www.anthropic.com/research/building-effective-agents )
Official references/topics: Prompt Chaining; Plan Before Editing; Multi-Phase Coding Workflows; Verification and Review.
NEW QUESTION # 159
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