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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Configuration & Workflows	20%	- Integrating Claude Code into development processes - Claude Code usage and configuration - Developer productivity workflows
Topic 2: Context Management & Reliability	15%	- Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations
Topic 3: Prompt Engineering & Structured Output	20%	- Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies
Topic 4: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability
Topic 5: Agentic Architecture & Orchestration	27%	- Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q88-Q93):

NEW QUESTION # 88
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. 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.
B. 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.
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. 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.

Answer: A

Explanation:
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.

NEW QUESTION # 89
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 pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file. Unchanged files are not included. Developers report that reviews consistently miss cross-file bugs--for example, a pull request renames a function's parameters, but the review does not identify callers in unchanged files that still use the old argument order.
Evaluation shows that cross-file bugs account for 35% of production incidents originating from reviewed pull requests.
What is the most effective change to the review design?

A. Build a static dependency graph and include every file located within two dependency hops of a changed file.
B. Run separate review passes for each changed file with its direct dependants, and then aggregate and deduplicate the findings through a final consolidation pass.
C. Add instructions asking the model to list external references and reason step by step about how each change could affect unseen callers.
D. Redesign the review as a turn-limited agentic task that can read files and search the repository, following references to verify cross-file findings.

Answer: D

Explanation:
Option C gives the reviewer access to the evidence currently missing from its prompt. An agentic review can use Grep, Glob, Read, language-server tools, and test commands to locate callers, follow imports, inspect type definitions, and verify whether a suspected compatibility issue actually exists. A turn limit controls cost while still allowing targeted exploration.
Anthropic's context-engineering guidance recommends just-in-time retrieval: agents should retain lightweight references and dynamically load the information needed for the current task instead of preloading a large fixed context. Anthropic has also reported that code-review performance improves when the necessary repositories are available for gathering complete context.

NEW QUESTION # 90
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 extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

A. Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.
B. Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
C. Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.
D. Redesign the schema so amended fields capture multiple values, each with source location and effective date.

Answer: D

Explanation:
The document contains multiple factually valid values whose applicability depends on chronology and legal context. Collapsing those values into a single scalar field discards essential provenance. Option B corrects the data model by representing each term as a structured record containing the extracted value, source location, document or amendment identifier, and effective date.
Anthropic's Structured Outputs feature is designed for data-extraction use cases in which nested objects and arrays must conform to a defined JSON Schema. Anthropic also recommends grounding factual outputs in direct source material and making claims auditable through supporting evidence. A provenance-aware schema applies both principles: it retains the original clause and the amendment instead of forcing Claude to resolve a potentially complex legal precedence question during extraction.

NEW QUESTION # 91
An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?

A. Temperature
B. Prompt length
C. Max tokens
D. Context window

Answer: A

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 # 92
After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?

A. Add instructions to your system prompt to "be conservative," "only flag definite issues," and
"consider that some patterns may be intentional."
B. Implement post-processing that uses keyword matching to filter out findings containing terms such as "convention," "context-dependent," or "trade-off."
C. Include few-shot examples in your prompt showing annotated code snippets that distinguish acceptable patterns from genuine issues in each category.
D. Create a comprehensive written specification of all patterns that should not be flagged, and then include the full documentation in the system prompt.

Answer: C

Explanation:
Option B teaches Claude the actual decision boundaries behind the team's judgments.
Contrasting examples can demonstrate why one apparent security pattern is acceptable within the organization's deployment controls while a superficially similar implementation is genuinely unsafe. The same technique can distinguish established style conventions from deviations and intentional performance trade-offs from accidental inefficiencies.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable ways to improve Claude's accuracy and consistency. Its official prompting guidance recommends examples that closely mirror the production task while covering important variations and edge cases. This encourages generalization based on demonstrated criteria instead of memorization of prohibited wording.

NEW QUESTION # 93
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