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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Tool distribution and permission controls - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation |
| Topic 2: 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 3: Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context window optimization and prioritization - Token budget management and cost control - Context pruning and summarization strategies |
| Topic 4: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Agentic loop design and stop_reason handling |
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NEW QUESTION # 61
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 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 request using a Sonnet-tier Claude model. The release notes are not needed until the following morning, providing approximately 12 hours of acceptable latency.
Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality.
Which approach satisfies all these constraints?
Answer: A
Explanation:
Option B applies Anthropic's dedicated asynchronous bulk-processing mechanism while preserving the existing model and prompt for every commit. The Message Batches API accepts independent Messages API requests, each identified by a unique custom_id, and charges both input and output usage at 50% of standard API prices. The approximately 12-hour latency allowance makes the release-notes workload well suited to batching because immediate results are unnecessary.
Option A may reduce wall-clock completion time, but concurrency does not alter the API's per-token price.
Option C changes the task structure and risks exceeding context or output limits, mixing commit-level classifications, complicating retries, and making it harder to associate errors with individual commits. A single large request also does not automatically consume fewer tokens because the model must still process all commit content. Option D violates the requirement to retain the same model tier and output-quality profile.
Batch results may arrive in an order different from submission order, so the pipeline must associate every response with its original commit through custom_id. This provides lower cost without changing the individual review prompts. Anthropic Message Batches documentation
NEW QUESTION # 62
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 pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?
Answer: A
Explanation:
Option C is the strongest choice among the listed approaches because it moves structured data generation from unconstrained response text into a schema-governed interface. Prompt-only JSON instructions, regular- expression extraction, and corrective retries are probabilistic recovery mechanisms: they may reduce failures, but none guarantees that the first response satisfies the contract. Anthropic now provides two schema- constrained mechanisms: JSON Structured Outputs through output_config.format and strict tool use through a tool definition with strict: true. Strict tool use uses grammar-constrained sampling so the tool input conforms to the declared JSON Schema, including required properties and data types. Therefore, the production implementation represented by option C should define the extraction tool's input_schema and enable strict mode. The application reads the tool_use block as the extracted record; it does not need to scrape prose or search for braces. Option A remains vulnerable to preambles or malformed output. Option B can select a JSON-looking substring but cannot establish schema validity. Option D adds latency and cost and can still fail repeatedly. The schema-constrained tool contract is consequently the most reliable architecture available in these options.
NEW QUESTION # 63
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 invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation-for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?
Answer: D
Explanation:
Option B creates a targeted correction loop using information the first extraction did not have: the validator's precise failure report. Anthropic's prompting guidance describes prompt chaining as appropriate when an application must inspect intermediate output or enforce a specific pipeline, with self-correction following the pattern generate, review against criteria, and refine. Here, deterministic validators identify arithmetic mismatches and invalid vendor-ID formats. Returning the source document, original extraction, and explicit errors lets Claude revise only the defective fields while preserving valid data. The corrected record must then be validated again before acceptance, with a bounded retry count and human-review fallback. Option A is unsafe because automatically recalculating a total may overwrite the amount actually printed on the invoice; a mismatch can originate in an OCR or line-item extraction error. Option C repeats the same extraction without explaining what failed, wasting calls and relying on chance. Option D may prevent some format violations, but JSON Schema cannot express every cross-field arithmetic relationship or external vendor-registry rule. A validator-guided second pass therefore reduces manual review without silently changing source facts, while keeping deterministic code-not the model-as the final acceptance authority.
NEW QUESTION # 64
The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?
Answer: C
Explanation:
In a coordinated multi-agent pipeline, the coordinator mediates information flow. It collects outputs from the web search agent and includes the relevant findings in the prompt when invoking the document analysis agent, ensuring proper sequencing and context without tightly coupling the subagents.
NEW QUESTION # 65
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?
Answer: A
Explanation:
Continue the agentic loop when stop_reason is tool_use, execute the requested tool, and return its result to Claude. Exit when Claude returns a terminal reason such as end_turn; other reasons like max_tokens require separate handling.
NEW QUESTION # 66
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