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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering & Structured Output	20%	- Prompt design 1. Prompt engineering techniques 2. Structured output and JSON schemas 3. Few-shot prompting 4. Output validation
Topic 2: Tool Design & MCP Integration	18%	- Tool integration 1. Model Context Protocol (MCP) 2. Resource and server integration 3. Tool interface design 4. Tool selection and safety
Topic 3: Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Planning and execution strategies 2. Agent orchestration 3. Single-agent and multi-agent architectures 4. Workflow design
Topic 4: Claude Code Configuration & Workflows	20%	- Claude Code 1. Code generation and automation 2. Development workflows 3. Agent skills 4. Configuration and project setup
Topic 5: Context Management & Reliability	15%	- Context handling 1. Reliability and evaluation 2. Memory strategies 3. Context window management 4. Cost and performance optimization

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q53-Q58):

NEW QUESTION # 53
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.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

A. Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
B. Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
C. Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
D. Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.

Answer: B

Explanation:
Because the requirements are incomplete and production caching involves hidden design choices, an interview helps surface invalidation, consistency, failure handling, and other trade-offs before implementation. Anthropic recommends this approach for larger features when important requirements may not yet be identified.

NEW QUESTION # 54
Your get_portfolio_value tool returns the total value of a user's investment portfolio. You're deciding between returning a structured JSON object with explicit fields versus returning the information as a formatted text string. What is the primary advantage of using structured output with defined fields?

A. Structured JSON is processed deterministically by the model, significantly improving accuracy when extracting values.
B. JSON schemas automatically validate that the underlying API returned correct data before the agent processes it.
C. Structured JSON consumes significantly fewer tokens than natural language, substantially reducing API costs.
D. The agent can reliably extract specific values without parsing free-form text, reducing errors in subsequent operations.

Answer: D

Explanation:
Structured output with defined fields allows the agent to directly access specific values without needing to interpret or parse natural language text, reducing errors in downstream processing and improving reliability for subsequent operations.

NEW QUESTION # 55
After deploying the automated review, you notice high precision but low recall-real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to "only report high- confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?

A. Expand the context to include related tests, recent Git history, and the module's dependency graph so Claude has richer evidence for judging severity.
B. Remove the conservative instructions and have Claude report every potential issue, then apply a programmatic filter that deduplicates findings and suppresses historically noisy categories.
C. Split the review into a finding stage whose objective is comprehensive coverage-reporting every potential issue with confidence and severity metadata-and a separate stage that verifies and thresholds those findings.
D. Add detailed few-shot examples demonstrating bug categories Claude should flag-race conditions, null dereferences, and error-handling gaps-while retaining the high-confidence filtering instruction.

Answer: C

Explanation:
The prompt's conservative reporting policy is directly causing the low recall. Claude may discover a legitimate race condition during analysis but suppress it because it cannot satisfy the instruction to report only issues about which it is certain. Option C separates two objectives that should not be conflated: broad defect discovery and strict acceptance filtering.
Anthropic's current code-review prompting guidance explicitly recommends reporting every issue, including uncertain or lower-severity findings, assigning confidence and severity metadata, and allowing a separate verification stage to filter them. This maximizes recall while retaining control over developer-facing noise.
Option A leaves the suppression policy intact, so additional examples cannot guarantee that discovered problems will be reported. Option B improves recall but relies on historical category-level suppression, which can discard genuine findings that happen to belong to noisy categories. Option D can improve analysis quality but does not remove the instruction responsible for withholding findings. A dedicated finding stage followed by an independently configurable verification or thresholding stage produces measurable recall and precision controls without forcing one model call to optimize competing objectives.

NEW QUESTION # 56
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.
A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed.
Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?

A. Paste the migration guide's breaking changes into your prompt and use direct execution to update all usages across the 45 files.
B. Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.
C. Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.
D. Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.

Answer: B

Explanation:
This migration is a high-impact, cross-module change with several independent breaking changes. Before editing, Claude must determine how the callback-based API is currently used, where the restructured User fields propagate, whether deprecated methods are wrapped or re- exported, and which downstream modules depend on the affected behavior. Plan mode is therefore the correct starting point because it allows Claude to inspect the repository, identify affected code paths, and produce an implementation strategy without modifying source files.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the change affects multiple files, or the developer is unfamiliar with the impacted code. Direct execution is better reserved for small, clearly scoped changes that can be described as a simple diff.

NEW QUESTION # 57
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. Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
B. Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.
C. Redesign the schema so amended fields capture multiple values, each with source location and effective date.
D. Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.

Answer: C

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 # 58
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