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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Tool Design & MCP Integration	18%	- Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Tool distribution and permission controls
Topic 2: Claude Code Configuration & Workflows	20%	- CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions
Topic 3: Prompt Engineering & Structured Output	20%	- System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement
Topic 4: Context Management & Reliability	15%	- Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies - Context window optimization and prioritization
Topic 5: Agentic Architecture & Orchestration	27%	- Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling - Session state management and workflow enforcement

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q82-Q87):

NEW QUESTION # 82
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?

A. Add the instruction: "Only report findings you are highly confident are genuine problems."
B. Ask Claude to rate each finding's confidence from 1 to 10 and include only findings rated 8 or higher.
C. Define explicit reporting criteria that distinguish reportable bugs and security issues from minor style preferences and accepted local patterns.
D. Add a secondary classification model that filters findings according to predicted developer acceptance.

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 # 83
After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?

A. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
B. The subagents need to share a single API connection to enable automatic context sharing between invocations.
C. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.
D. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.

Answer: D

Explanation:
The synthesis agent relies on the coordinator to provide relevant findings from prior subagents. If the coordinator fails to include these outputs in the synthesis prompt, the agent receives no actionable context and cannot produce a meaningful summary or analysis.

NEW QUESTION # 84
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer asks the agent to understand how the caching layer works before adding a new cache-invalidation trigger. Initial Grep searches show that caching logic spans 15 files containing decorators, middleware, and service classes-approximately 8,000 lines in total.
What is the most effective next step for building understanding while managing context constraints?

A. Use Glob to find files matching common caching patterns such as *cache*.py or caching/ , read the largest files first, and inspect smaller files afterward.
B. Use Read to load all 15 files sequentially and build a complete understanding of the caching implementation.
C. Use Grep to search for invalidate and expire , and then read only the matching line ranges with minimal surrounding context.
D. Analyze imports and class hierarchies to identify the base cache class, read that file to understand its interface, and then trace the specific invalidation implementations.

Answer: D

Explanation:
Option A establishes the caching architecture before consuming thousands of implementation lines. The base class or protocol reveals the supported operations, lifecycle, cache-key rules, and invalidation contract.
Imports, subclasses, registrations, and call sites can then direct the agent toward only those concrete implementations relevant to the proposed trigger. Anthropic's effective context-engineering guidance explains that context is finite and that recall degrades as irrelevant tokens accumulate. It identifies Glob and Grep as mechanisms for retrieving code just in time rather than loading an entire repository upfront. Option B assumes file size or naming determines architectural importance; a small interface may define the system while a large service file contains incidental caching calls. Option C loads approximately 8,000 lines before establishing which portions matter. Option D is overly lexical and can miss invalidation implemented through event publication, key-version changes, inherited methods, or generically named hooks. Dependency-guided exploration provides a compact conceptual model first and expands the investigation only when concrete relationships show that additional files are relevant.

NEW QUESTION # 85
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 monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output.
How should you modify the system to run these subagents concurrently?

A. Add instructions explaining the performance benefits of parallel execution and requesting that the coordinator invoke both subagents simultaneously.
B. Create an asynchronous orchestration layer that starts separate coordinator-subagent pairs in parallel and aggregates their results.
C. Structure the coordinator to emit both Agent tool calls--formerly Task tool calls--in a single response rather than across separate conversation turns.
D. Switch both subagents to a Haiku-tier model instead of Sonnet to reduce their individual execution times.

Answer: C

Explanation:
Option B expresses both independent delegations in the same orchestration turn, allowing the runtime to execute them concurrently and return their results together. The current Claude Agent SDK calls the subagent-spawning capability the Agent tool; Task is its former name. Anthropic's SDK subagent documentation explicitly identifies parallel analysis as a primary subagent use case. Claude tool responses can also contain multiple tool_use blocks, enabling independent calls to be handled as one parallel group rather than as serial model turns.

NEW QUESTION # 86
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.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?

A. Require subagents to include publication or data collection dates in their structured outputs.
B. Configure the web search agent to only return results from the past 6 months.
C. Add a conflict resolution agent that automatically discards older data when newer data exists for the same metric.
D. Instruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.

Answer: A

Explanation:
Temporal metadata lets the synthesis agent distinguish measurements from different time periods and interpret 18% in 2021 and 35% in 2024 as a trend rather than a contradiction.

NEW QUESTION # 87
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