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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Prompt Engineering & Structured Output	20%	- Tool-augmented prompting 1. Tool-use driven reasoning patterns 2. Validation and retry loops - Instruction design 1. Few-shot prompting strategies 2. Deterministic output formatting (e.g., JSON)
Claude Code Configuration & Workflows	20%	- CI/CD and workflow integration 1. Command vs plan mode usage 2. Automated pipeline integration - Claude Code configuration 1. CLAUDE.md hierarchy and scoping 2. Project-level vs user-level configuration
Context Management & Reliability	15%	- Context window optimization 1. Managing long conversation degradation 2. "Lost in the middle" mitigation strategies - System reliability 1. Error propagation handling 2. Escalation strategies (fail vs retry vs human)
Tool Design & MCP Integration	18%	- Tool interface design 1. Clear tool descriptions and boundaries 2. Structured tool input/output schemas - Model Context Protocol (MCP) 1. MCP server integration patterns 2. Tool selection and routing strategies
Agentic Architecture & Orchestration	27%	- Multi-agent orchestration 1. Parallel task decomposition 2. Coordinator / sub-agent patterns - Agentic loops & lifecycle design 1. Tool use loop execution (request → tool_use → result → next step) 2. Stop reason handling and control flow

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q17-Q22):

NEW QUESTION # 17
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 monorepo contains shared coding standards in /docs/standards/ : security-rules.md (for services handling user data), testing-petterns.md (for all packages), and api-conventions.md (for API-facing services). Your 15 packages are organized by feature domain ( /packages/auth/,
/packages/billing/, /packages/notifications/ , etc.) without naming conventions indicating which handle user data or expose APIs. Package maintainers are expected to configure their own local development settings, as they understand their package's domain requirements. Currently, all package CLAUDE.md files duplicate all three standards, applying irrelevant guidance. What's the most effective approach?

A. Put all standards in the root CLAUDE.md with override instructions like "ignore security-rules.md when working in packages that don't handle user data."
B. Create a shared-stendards.md that uses @imports to combine all three standards, then have each package's CLAUDE.md import that combined file.
C. Create .claude/rules/ files for each standard with YAML frontmatter paths listing every package directory where that standard should apply.
D. Use @imports in each package's CLAUDE.md to reference only the specific standard files relevant to that package, based on the maintainer's domain knowledge.

Answer: D

Explanation:
Package maintainers know which domain-specific standards apply. Each package can import only the required shared files, avoiding duplicated content and irrelevant context while keeping the authoritative standards centralized. Claude Code supports @path/to/import in CLAUDE.md files.

NEW QUESTION # 18
How should subagents handle transient failures before propagating errors to the coordinator?

A. Silently drop the failed query and proceed with other data sources without notifying the coordinator.
B. Immediately propagate all failures to ensure the coordinator maintains full observability.
C. Implement local error recovery within the subagent and only propagate errors that cannot be resolved locally.
D. Use a PreToolUse hook to block the error from reaching the coordinator entirely.

Answer: C

Explanation:
Subagents should attempt to implement local recovery for transient failures first. If they cannot resolve the issue, they should then propagate the error to the coordinator along with what was attempted and any partial results obtained.

NEW QUESTION # 19
Your infrastructure-as-code repository includes Terraform modules (/terraform/), Kubernetes manifests (/kubernetes/), and CI/CD pipeline scripts (/pipelines/). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?

A. Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.
B. Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g, "## Terraform Conventions"), improving organization and readability.
C. Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*"]), loading rules only when editing matching files.
D. Split content into subdirectory CLAUDE.md files (/terraform/CLAUDE.md,
/kubernetes/CLAUDE.md), so Claude loads directory-specific guidance.

Answer: C

Explanation:
The paths field conditionally loads each rule only when Claude works with matching files, preventing unrelated Terraform, Kubernetes, or pipeline guidance from consuming context.
Subdirectory CLAUDE.md files are directory-based, while path-scoped rules provide precise file- pattern targeting.

NEW QUESTION # 20
Your team wants to use the Message Batches API to reduce costs by 50%. The pipeline includes a real-time blocking pre-merge code review and a nightly technical debt audit. How should you apply the Batch API?

A. Switch both to the Batch API but implement a timeout fallback to synchronous calls if it exceeds 5 minutes.
B. Use synchronous API calls for both workflows since the Batch API lacks JSON schema support.
C. Use the Batch API for both workflows to maximize cost savings.
D. Use the Batch API strictly for the nightly audit, and retain synchronous calls for the blocking pre-merge review.

Answer: D

Explanation:
The Message Batches API processes within a 24-hour window with no latency guarantees. It is perfect for latency-tolerant workloads like overnight audits, but inappropriate for blocking workflows (like pre-merge checks) where developers need immediate feedback.

NEW QUESTION # 21
During data extraction, a semantic validation check fails because the extracted line items do not sum to the total invoice amount. What is the recommended best practice for implementing the validation-retry loop?

A. Increase the max_tokens limit on the retry request to give the model more space to self-correct.
B. Append specific validation error details to the prompt, stating which field failed and the nature of the discrepancy.
C. Rely entirely on the model's self-reported confidence scores to automatically adjust the extraction.
D. Send a generic retry message such as 'There were errors in your extraction. Please try again.' to reset the model's reasoning.

Answer: B

Explanation:
Generic retry messages give the model no signal for what to fix. The recommended practice is to use specific error feedback (e.g., 'Line items total $450 doesn't match the stated subtotal $500'). This explicit feedback gives the model a clear correction target during the retry loop.

NEW QUESTION # 22
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