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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Session state management and workflow enforcement |
| Topic 3: Claude Code Configuration & Workflows | 20% | - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions |
| Topic 4: Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - System prompt design and persona alignment |
| Topic 5: Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - MCP tool, resource and prompt implementation |
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NEW QUESTION # 61
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 system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2? attempts.
For which failure pattern would additional retries be LEAST effective?
Answer: B
Explanation:
Retry-with-error-feedback is effective when the required information is available and the defect concerns representation. Options A, C, and D are correctable formatting failures: the model can flatten an object into an array, remove thousands separators and emit an integer, or convert a datetime into the required date-only format. The validation message supplies enough information to revise the output.
Option B is fundamentally different. The complete co-author list is absent from the model's input and exists only in an external document. No number of retries can recover evidence that was never supplied. Repeated attempts may instead increase the probability of fabrication.
Anthropic's hallucination guidance recommends restricting responses to available documents, allowing the model to acknowledge missing information, and withholding or retracting claims that cannot be grounded in the source.
The correct response is therefore to return an explicit missing-data state, retrieve the external document through a tool, or route the record for enrichment. Retrying without changing the information boundary cannot solve the problem.
Current Anthropic Structured Outputs can eliminate many shape-related failures by guaranteeing JSON Schema conformance, but they still cannot create unavailable source facts.
NEW QUESTION # 62
Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API call using a Sonnet-tier Claude model. The release notes are not needed until the following morning, so results have approximately 12 hours of acceptable latency. Your team needs to reduce per-token API cost for this step while keeping the same model and prompts, with no change to the model tier or output quality. Which approach satisfies all these constraints?
Answer: A
Explanation:
Option C preserves each commit's existing model, prompt, and independent processing structure while applying Anthropic's reduced batch pricing. The official Message Batches documentation states that batch processing reduces input and output token costs by 50%. Requests are processed independently and asynchronously, and each response can be correlated with its original commit through a unique custom_id.
This workload is a strong batch candidate because it contains many independent requests and does not require an immediate response. Most batches complete within one hour, although processing can continue for up to
24 hours. The pipeline must therefore tolerate asynchronous completion and retrieve the results when processing finishes.
Option A changes the request architecture, increases the risk of context or output-limit failures, and makes individual retry and result association more difficult. Reducing request count does not inherently reduce per- token pricing. Option B improves throughput but concurrency does not change token rates. Option D would reduce cost but violates the explicit requirement to retain the same model tier. The Message Batches API is the only option satisfying every stated constraint.
NEW QUESTION # 63
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent-this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate).
How should you achieve guaranteed compliance?
Answer: B
Explanation:
A mandatory compliance threshold must be enforced outside probabilistic model reasoning. A PreToolUse hook can inspect every attempted process_refund call before execution, compare its amount with the $500 threshold, and deny the call when the limit is exceeded. The host application can then create the human- escalation case using the validated customer and order context.
Anthropic describes hooks as deterministic controls that ensure required actions occur instead of relying on the model to choose them. Agent SDK documentation confirms that a single denying PreToolUse hook blocks the tool call, including MCP tools matched through their qualified tool names. ( https://docs.anthropic.com/en
/docs/claude-code/hooks-guide )
Options A and B improve expected behavior but cannot guarantee compliance. The stated production failure rate demonstrates that prompting alone is insufficient. Option C prevents the refund from completing, but the subsequent escalation still depends on Claude correctly interpreting the returned error and invoking the human tool. It therefore enforces the financial block but not the complete escalation requirement.
Option D enforces both sides of the policy at the orchestration boundary: deny the unauthorized action and route the case to an approved human process. The escalation action should be idempotent and auditable.
Official references/topics: PreToolUse hooks, deterministic policy enforcement, MCP tool interception, human-approval controls.
NEW QUESTION # 64
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 automated review generates many findings per pull request, but developer feedback shows that approximately half are dismissed as "not worth addressing." Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.
Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?
Answer: C
Explanation:
Option C defines the reviewer's actual objective instead of relying on an undefined notion of confidence. The prompt should identify reportable categories such as correctness defects, security vulnerabilities, data-loss risks, broken requirements, and meaningful performance regressions. It should also specify that cosmetic preferences, lint-enforced formatting, speculative refactoring, and intentional repository conventions must not be reported unless they cause concrete incorrect behavior.
Anthropic's Claude Code best-practices guidance recommends telling a reviewer to report gaps affecting correctness or stated requirements and to treat other observations as optional. Without such criteria, a reviewer asked to "find issues" will predictably return technically defensible but operationally low-value observations. Option A adds cost and another probabilistic decision layer before correcting the primary prompt. Option B relies on self-reported confidence, which does not necessarily align with developer value or severity. Option D can suppress genuine defects that require inference or cross-file verification because
"highly confident" is undefined. Explicit scope, evidence requirements, and examples of both reportable and excluded findings directly calibrate the review toward what developers will act upon. Claude Code best practices
NEW QUESTION # 65
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 need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file.
What is the most appropriate approach?
Answer: D
Explanation:
This change is narrow, localized, and already defined: add one conditional validation check to an existing function in a single file. A separate planning phase would introduce process overhead without resolving meaningful architectural uncertainty. Direct execution allows Claude to read the function, implement the condition, and run the relevant focused tests.
Anthropic explicitly states that plan mode adds overhead and should generally be skipped when the scope is clear and the fix is small. Planning is most valuable when the approach is uncertain, multiple files are affected, or the code is unfamiliar. Anthropic's practical rule is that when the required diff can be described in one sentence, direct implementation is appropriate. ( https://code.claude.com/docs/en/best-practices ) Option B allocates unnecessary reasoning effort to straightforward validation logic. Options C and D exaggerate the complexity of a single-function change. Broader impact analysis would be justified only if the requirement altered reservation semantics, time-zone rules, persistence behavior, or public interfaces-none of which is stated.
The implementation should still include verification. Claude should add or update tests for a future date, the current date, and a past date, then run the narrowest relevant test command. Direct execution does not mean unverified execution.
Official references/topics: Direct Execution; Plan-Mode Selection; Small Scoped Changes; Focused Verification.
NEW QUESTION # 66
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