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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Prompt design strategies - Structured output generation and validation |
| Topic 2: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Integrating Claude Code into development processes - Developer productivity workflows |
| Topic 3: Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns |
| Topic 5: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability |
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NEW QUESTION # 139
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: D
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 # 140
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement, losing important nuance, while other reports use excessive qualifications and become unhelpful. The web-search agent returns, "Industry analysts estimate a $50 billion market size, although methodologies vary." The document-analysis agent returns, "A peer-reviewed study estimates $35 billion, with a ±$7 billion 95% confidence interval." The coordinator either selects one estimate arbitrarily or produces a vague $35-$50 billion range.
What systematic approach best addresses this?
Answer: D
Explanation:
Option D preserves the evidence instead of manufacturing certainty. The two estimates are not directly interchangeable: one is an industry estimate with unspecified methodology, while the other is a peer-reviewed estimate with an explicit confidence interval. Converting both into model-generated confidence scores and calculating a weighted average would create a new figure that neither source reported and that may have no statistical validity. Anthropic's hallucination-reduction guidance recommends making claims auditable through quotations, citations, and supporting evidence rather than presenting unsupported synthesis as fact. Its Citations documentation similarly emphasizes retaining the exact source passages supporting individual claims. Filtering uncertain findings, option B, would remove decision-relevant information. Requiring two- source corroboration, option C, could also discard credible evidence concerning emerging or specialized subjects. The synthesis agent should report the estimates separately, explain their methodological differences, identify which findings are strongly supported or disputed, and state what evidence would resolve the disagreement. This produces calibrated, useful reporting without arbitrary selection, excessive hedging, or false precision.
NEW QUESTION # 141
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.
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers dismiss approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project' s conventions as persistent context during every review?
Answer: C
Explanation:
Option A supplies the missing project context before Claude evaluates the code. Anthropic's CLAUDE.md documentation defines project-level files as persistent, version-controlled instructions for coding standards, architecture, workflows, and conventions. The described exceptions are stable repository facts: force- unwrapping is permitted in tests, coordinator classes follow an intentional pattern, and an internally maintained dependency remains approved despite its public deprecation status. Recording these facts concisely allows every review session to interpret the patterns correctly.
Option B removes surrounding evidence and would make architectural and cross-file judgments less reliable.
Option C hides findings after generation and can suppress genuine bugs containing the same keywords.
Option D adds noise to source files and distributes policy across individual call sites instead of documenting it centrally. The CLAUDE.md entries should be specific and scoped-for example, limiting the force- unwrapping exception to test paths rather than approving it globally. Anthropic notes that shorter, precise instructions are followed more consistently, so the file should state the accepted convention, its scope, and any conditions that would still make the pattern reportable.
NEW QUESTION # 142
Your expense reimbursement agent processes employee requests using a
process_reimbursement tool. Company policy requires that reimbursements above $500 must be approved by a manager before funds are disbursed. The agent handles hundreds of requests daily, and you need the threshold enforcement to be tamper-proof regardless of how the agent is prompted. Which design ensures the $500 approval threshold cannot be bypassed?
Answer: D
Explanation:
Enforcing the approval threshold within the tool itself makes it tamper-proof and independent of agent behavior or prompts. The tool controls disbursement and ensures manager approval is required for amounts over $500, preventing accidental or intentional bypass.
NEW QUESTION # 143
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 processes two document types: standard monthly reports, which are archived after processing, and urgent exception reports, which must trigger business alerts within 30 minutes of receipt.
Both use the same JSON schema. You want to minimize API costs while meeting the latency requirements.
How should you architect the processing pipeline?
Answer: B
Explanation:
Option B correctly separates asynchronous bulk work from latency-sensitive operational work. Anthropic describes the Message Batches API as a cost-effective asynchronous service for high-volume requests that do not require immediate responses. Batch requests are processed independently, normally finish within an hour, may remain pending for up to 24 hours, and are billed at 50 percent of standard API prices. Those characteristics fit standard monthly reports because they are archived after processing and have no immediate business deadline. Urgent exception reports, however, must trigger an alert within 30 minutes. The batch service cannot guarantee that deadline, even if many batches finish quickly; a workload should not use typical completion time as a service-level guarantee. Routing exceptions through the synchronous Messages API provides the real-time response path needed for alerting, while the shared JSON schema keeps downstream records consistent across both routes. Option A knowingly permits delayed alerts. Option C introduces an hourly submission delay before processing even begins. Option D meets latency requirements but wastes the batch discount on non-urgent reports. A routing layer based on document priority therefore satisfies both the cost objective and the business deadline without changing the extraction contract.
NEW QUESTION # 144
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