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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
| Topic 2: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability - Designing effective tools for Claude applications |
| 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% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Topic 5: Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Improving Claude response quality and consistency - Structured output generation and validation |
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NEW QUESTION # 88
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're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude. What approach will most effectively enable progressive improvement across multiple iterations?
Answer: C
Explanation:
Tests provide an executable specification and objective feedback for each iteration. Claude can implement against the suite, run it, inspect failures, and progressively refine the algorithm until correctness and performance requirements are met. Anthropic recommends running tests and fixing failures as an iterative Claude Code workflow.
NEW QUESTION # 89
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: B
Explanation:
The investigation contains several bounded research questions that can be delegated independently: locating the complete test suite, tracing payment and refund flows, identifying conditional branches, and mapping external dependencies. Each subagent can read the relevant files in its own context and return a focused summary to the coordinating agent.
Anthropic recommends subagents for codebase exploration because extensive file reading rapidly consumes the main context window. Subagents isolate that volume and return only their conclusions, preserving the main conversation for synthesis and implementation. ( https://docs.anthropic.com/en/docs/claude-code
/common-workflows ) Anthropic also describes parallel research as appropriate when separate investigation paths can proceed independently and the main agent can synthesize the results afterward. ( https://docs.
anthropic.com/en/docs/claude-code/sub-agents )
Option B sacrifices the current conversational state and requires reconstruction after /clear . Option C may reduce token usage, but isolated text matches cannot reliably reveal full execution paths, indirect calls, or test coverage relationships. Option D converts the current analysis into a single lossy summary and risks omitting details needed later.
Option A directly addresses the demonstrated context degradation while retaining a high-level coordinating thread. The subagent prompts should be narrowly scoped and require concrete outputs such as file paths, uncovered branches, call-chain evidence, and existing tests associated with each flow.
Official references/topics: Subagent Context Isolation; Parallel Research; Context Preservation; Coordinated Codebase Analysis.
NEW QUESTION # 90
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.
Testing reveals that when source documents are missing certain specifications, the model fabricates plausible- sounding values to satisfy your schema's required fields. For example, a document mentioning only dimensions receives a fabricated "weight: 2.3 kg" in the extraction output.
What schema design change most effectively addresses this hallucination behavior?
Answer: D
Explanation:
The schema is creating a structural incentive for fabrication. When a field is declared required, the output must contain a value even when the source document contains no corresponding evidence. Structured Outputs can guarantee that Claude's response conforms to a JSON Schema, but schema conformance does not establish that every generated value is factually supported. Anthropic's documentation shows that the required array determines which properties must be present; therefore, source-dependent properties that may legitimately be absent should not be included as required fields. ( https://platform.claude.com/docs/en/build- with-claude/structured-outputs ) Option B corrects the problem at the contract level. Claude can omit the unavailable property rather than inventing content merely to produce valid JSON. A nullable representation could also be used when downstream systems require a stable key set, but forcing an unsupported non-null value is architecturally unsound.
Option A still requires placeholder generation and does not resolve the mismatch between the schema and available evidence. Option C relies on model-generated confidence, which is not a substitute for grounding.
Option D is a useful secondary control, but it does not constitute the requested schema-design change.
Anthropic recommends allowing uncertainty and requiring factual claims to be grounded in the provided material. ( https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Official references/topics: Structured Outputs-JSON Schema design; Reduce Hallucinations-allowing uncertainty and grounding claims.
NEW QUESTION # 91
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: D
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 # 92
During initial testing of the automated review pipeline, you notice that reviews of large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops-Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?
Answer: C
Explanation:
Option A is the only configuration that establishes both required limits inside Claude Code. The official CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode; Claude Code exits with an error when that limit is reached. It defines --max-budget-usd as the maximum dollar expenditure on API calls before execution stops, including applicable subagent expenditure.
These controls address different failure dimensions. The turn limit prevents an investigation from continuing through excessive read-search-analyze cycles, while the budget limit stops an invocation whose expensive turns consume the monetary allowance before reaching the turn ceiling. Supplying both therefore creates an effective per-run boundary.
Option B controls how permission requests are handled; it does not limit the number of already permitted tool calls or API expenditure. Option C applies an external wall-clock timeout and merely observes cost, violating the requirement that Claude Code itself enforce both limits. Option D lowers expected cost per turn but creates no hard ceiling: the agent can still perform many iterations and exceed the intended budget. A cheaper model also does not guarantee that the review will terminate within a predictable number of turns.
NEW QUESTION # 93
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