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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures |
| Topic 2: Context Management & Reliability | 15% | - Production deployment considerations - Managing context windows and information flow - Evaluation and reliability strategies |
| Topic 3: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Topic 4: Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
| Topic 5: Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
>> New Anthropic CCAR-F Study Notes <<
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NEW QUESTION # 144
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: A
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.
NEW QUESTION # 145
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.
The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate-it generates messages such as, "I'll ask the web-search agent to find sources on this topic"-but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors.
What is the most likely cause?
Answer: C
Explanation:
Option C matches the distinction between reasoning about delegation and executing it. Defining subagents makes their descriptions available for selection, but the coordinator must still invoke the SDK's subagent- spawning tool. Current Claude Agent SDK documentation calls this the Agent tool; Task was its earlier name and remains relevant to older SDK configurations. Anthropic's Subagents in the SDK documentation instructs developers to include Agent in allowedTools so subagent invocations are approved automatically. Without that permission, an invocation can fall through to a permission callback or be denied under a non-interactive permission mode. Option A is unlikely because the configured subagent descriptions already tell Claude when each agent should be selected, although explicit prompting can improve invocation reliability. Option B misstates context isolation: context must be included in the spawning prompt, but that issue occurs after an invocation is attempted and does not explain the absence of all subagent executions. Option D would normally produce truncation evidence or incomplete output rather than consistent verbal promises with no tool call. The configuration should therefore permit Agent, explicitly request delegation where necessary, and log subagent invocation events.
NEW QUESTION # 146
An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?
Answer: B
Explanation:
Lower temperature reduces randomness during token selection, producing more consistent and repeatable outputs. This is especially valuable in structured enterprise workflows where deterministic behavior is preferred over creative variation.
NEW QUESTION # 147
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.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?
Answer: B
Explanation:
Project-scoped MCP servers are stored in .mcp.json and can be shared through version control with the whole team. Personal local or user-scoped servers are stored in ~/.claude.json and remain private to the individual user.
NEW QUESTION # 148
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.
You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization-the synthesis agent attempts web searches, and the report generator tries to analyze documents.
What is the primary cause of this poor tool-selection behavior?
Answer: B
Explanation:
Option C identifies the architectural contradiction. A specialized subagent's role prompt says what work it should perform, but granting it every tool simultaneously signals that unrelated actions are available and potentially legitimate. The synthesis agent can therefore interpret web search as a permissible way to fill gaps, while the report generator may invoke document-analysis tools even though those responsibilities belong elsewhere. Anthropic's Agent SDK documentation describes subagents as focused workers with tailored instructions and explicitly supports restricting each subagent to specific tools; it notes that tool restrictions reduce unintended actions. There is no documented universal threshold at which choosing among 18 tools becomes unreliable, so option B invents a "four or five tools" limit. Context consumption in option A may affect efficiency, but it does not directly explain the role-specific cross-calling pattern. Option D is also incorrect because the observed behavior occurs after delegation, inside agents that already possess the tools.
The remedy is least-capability configuration: give the web agent search tools, the document agent document tools, the synthesis agent only the inputs and utilities required to combine findings, and the report agent formatting or file-output tools. Aligning role, description, and tool allowlist removes the conflicting affordances.
NEW QUESTION # 149
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