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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Claude Code Configuration & Workflows	20%	- Claude Code 1. Code generation and automation 2. Development workflows 3. Configuration and project setup 4. Agent skills
Prompt Engineering & Structured Output	20%	- Prompt design 1. Prompt engineering techniques 2. Output validation 3. Few-shot prompting 4. Structured output and JSON schemas
Context Management & Reliability	15%	- Context handling 1. Memory strategies 2. Reliability and evaluation 3. Context window management 4. Cost and performance optimization
Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Single-agent and multi-agent architectures 2. Planning and execution strategies 3. Workflow design 4. Agent orchestration
Tool Design & MCP Integration	18%	- Tool integration 1. Tool selection and safety 2. Tool interface design 3. Resource and server integration 4. Model Context Protocol (MCP)

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CCAR-F aktueller Test, Test VCE-Dumps für Claude Certified Architect - Foundations

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Anthropic Claude Certified Architect - Foundations CCAR-F Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
When researching "renewable-energy adoption," the web-search agent returns recent statistics showing 35% adoption in 2024, while the document-analysis agent extracts an 18% adoption figure from an internal 2021 report. The synthesis agent incorrectly treats the figures as contradictory instead of recognizing that they may show growth over time. What change would best enable the synthesis agent to interpret such temporal differences correctly?

A. Require subagents to include publication dates and data-collection periods in their structured outputs.
B. Configure the web-search agent to return only results published during the previous six months.
C. Add a conflict-resolution agent that automatically discards older data whenever a newer value exists for the same metric.
D. Instruct the synthesis agent to treat the newest value as authoritative and place all older findings in a separate historical section.

Antwort: A

Begründung:
The two statistics cannot be compared correctly without temporal metadata. Option A preserves when each value was measured, not merely when its document was retrieved. The structured record should ideally distinguish publication date, observation period, geographic scope, population, methodology, unit, and metric definition. The synthesis agent can then determine whether the figures represent a trend, conflicting measurements of the same period, or incomparable populations.
Anthropic's multi-agent research guidance emphasizes specifying output formats and source requirements so downstream agents receive the context required for accurate synthesis. Its structured-output capability enables these provenance fields to be required and schema validated.
Option B removes valuable historical evidence and still does not guarantee that returned sources describe the same measurement period. Option C assumes newer automatically means more reliable and could discard an authoritative historical baseline. Option D preserves older material but imposes an interpretation without examining whether methodology, geography, or definitions changed. Explicit temporal and methodological metadata allows the synthesis agent to state a defensible conclusion such as apparent growth from 18% in
2021 to 35% in 2024 while preserving any comparability caveats.

22. Frage
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 has three requirements for Claude Code's behavior in your project:
* Claude must never modify files in the db/migrations/ directory.
* Claude should prefer your custom logging module over console.log .
* All TypeScript files must be auto-formatted with Prettier after every edit.
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1.
How should you restructure these requirements across Claude Code's configuration mechanisms?

A. Configure hooks for all three: a PreToolUse hook script that blocks Edit calls targeting db/migrations/ , a PreToolUse hook script that adds logging convention context before edits, and a PostToolUse hook that runs Prettier after TypeScript edits.
B. Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook to run Prettier after TypeScript edits.
C. Move all three requirements into .claude/rules/ as path-scoped rules: one targeting db/migrations/** that forbids editing those files, and others targeting **/*.ts for the logging convention and formatting instruction.
D. Rewrite all three requirements in CLAUDE.md using stronger directive language and add few-shot examples that demonstrate Claude refusing to edit migration files and running Prettier after edits.

Antwort: B

Begründung:
Each requirement belongs in the mechanism matching its enforcement semantics. The migration restriction is a hard safety boundary, so it should be implemented through permissions.deny , not merely expressed as contextual guidance. Anthropic explicitly states that CLAUDE.md content is treated as context rather than enforced configuration. Permission rules use the Tool(specifier) form, evaluate deny rules before ask or allow rules, and support path-based Edit(...) restrictions. ( https://code.claude.com/docs/en/memory ) The custom logging convention is a persistent project preference, making CLAUDE.md the appropriate location. It should guide Claude's code-generation decisions but does not require deterministic interception.
Prettier formatting, by contrast, is deterministic automation. Anthropic documents the exact pattern of using a PostToolUse hook with an Edit|Write matcher to run Prettier automatically after file modifications. (
https://code.claude.com/docs/en/hooks-guide )
Option A incorrectly treats path-scoped rules as a security control; rules remain instructions. Option B overuses hooks for a semantic coding preference that belongs in CLAUDE.md. Option C relies entirely on stronger prompting, which cannot guarantee that protected files remain untouched. Option D correctly separates prohibition, preference, and automatic enforcement.
Official references/topics: Permission rules, CLAUDE.md semantics, PostToolUse formatting hooks.

23. Frage
Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

A. Increase temperature.
B. Add grounding through trusted retrieval.
C. Remove examples.
D. Shorten responses only.

Antwort: B

Begründung:
Grounding responses with trusted enterprise documents helps Claude generate answers supported by authoritative sources. Retrieval significantly reduces unsupported statements while improving factual consistency across knowledge-intensive applications.

24. Frage
You are building a structured data-extraction system using Claude. The system extracts information from unstructured documents, validates output against JSON schemas, and integrates the results with downstream systems.
Monitoring reveals that specifications sometimes appear inconsistently within source documents.
For example, a summary section might state "Battery: 4000 mAh," while the detailed specifications table states "Battery: 4200 mAh." Your current schema contains a single battery_capacity field.
This inconsistency occurs in approximately 15% of documents, and historical analysis confirms that the detailed specifications table is accurate 90% of the time.
What is the most effective approach?

A. Add extraction instructions specifying that values from the detailed specifications table take precedence when conflicting values exist, while retaining the single-value schema.
B. Reject every extraction containing conflicting values and require the source document to be corrected before processing continues.
C. Add a conflict_detected Boolean field and route every affected document for manual review.
D. Change the field to an array that captures every discovered value and its source location, leaving downstream systems to apply precedence rules.

Antwort: A

Begründung:
Option C converts an empirically validated source hierarchy into an explicit extraction rule. The downstream contract requires one battery_capacity value, and historical analysis establishes that the detailed specifications table is substantially more reliable than the summary section. Claude should therefore be instructed to inspect all occurrences, detect conflicts, and select the detailed- table value when the two locations disagree.
Anthropic's prompting guidance emphasizes clear, direct instructions, relevant context, and explicit decision rules when order or completeness matters. Providing the reason for the precedence rule also helps the model generalize it to comparable specification conflicts.

25. Frage
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?

A. Retry the extraction up to three times when validation fails, accepting the first result that passes validation.
B. When validation fails, make a follow-up request containing the document, extraction, and validation errors so the model can correct the result.
C. Add stricter schema constraints with detailed field descriptions to prevent the model from initially generating invalid values.
D. Implement post-processing logic that automatically corrects common errors, such as recalculating totals from line items when sums do not match.

Antwort: B

Begründung:
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.

26. Frage
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