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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q183-Q188):

NEW QUESTION # 183
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. Reviews are posted asynchronously and do not block pull-request creation. Developers report that reviews consistently miss bugs involving cross-file interactions-for example, a pull request renames a function's parameters, but the review does not flag callers in other files that still use the old parameter names. Post-release analysis shows that cross-file bugs account for 35% of production incidents from reviewed pull requests. What is the most effective change to your review design?

A. Run parallel review passes for each changed file with its direct dependents included, and then aggregate and deduplicate the findings through a final summarization call.
B. Use static analysis to build a dependency graph of changed code, and then expand the prompt to include every file within two dependency hops of any changed file.
C. Redesign the review as a turn-limited agentic task in which the model can read files and search the codebase through tools, following references to verify cross-file findings.
D. Add chain-of-thought instructions asking the model to list all external references in the diff and then reason step by step about how each change might affect callers in other files.

Answer: C

Explanation:
The failure is caused by missing evidence, not insufficient reasoning over the supplied evidence. A static prompt containing only changed files cannot reliably identify callers, configuration dependencies, generated interfaces, or indirect relationships located elsewhere in the repository. Asking Claude to reason more deeply cannot recover code that it was never given.
Option A converts the review into a bounded agentic loop. Claude can search for symbol references, read relevant callers, inspect type definitions, and follow newly discovered dependencies before validating a potential defect. Anthropic describes the Claude Code agentic loop as gathering context, taking action, verifying results, and repeating based on tool feedback. A turn limit preserves predictable cost and execution time.
Option C improves coverage but imposes an arbitrary two-hop boundary and depends on the accuracy of a precomputed graph, which may omit dynamic imports, reflection, generated code, configuration references, or language-specific call relationships. Option D creates duplicated context and aggregation complexity while still limiting each reviewer to predetermined dependents. Option B changes the reasoning instructions but supplies no mechanism for checking unchanged files. Tool-enabled, just-in-time retrieval is therefore the most adaptable and reliable architecture for cross-file review.

NEW QUESTION # 184
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.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?

A. Use Glob with a pattern such as **/eval* to locate files, and then read each matching file.
B. Read the project's main entry file and follow import statements to trace where eval() might be used.
C. Use Bash to run ls -R | grep eval and search the recursively listed filenames.
D. Use Grep to search for the regular-expression pattern eval\( across all files in the codebase.

Answer: D

Explanation:
Option B uses the tool designed to search file contents. Anthropic's Claude Code tools reference distinguishes Grep from Glob: Grep searches lines inside files, whereas Glob matches filenames and paths. Grep is built on ripgrep and accepts regular-expression patterns, so the opening parenthesis should be escaped as eval\( when the intention is to match the literal function call. The search can return matching files, line numbers, and surrounding context without loading every file into the model's context window. Option A searches filenames resembling eval , not source files whose contents invoke the function. Option C follows only code reachable from a selected entry point and can miss test utilities, dynamically loaded modules, scripts, and dormant vulnerable code. Option D combines a recursive filename listing with text filtering; it still searches names rather than file contents. After Grep identifies matches, the agent should use Read on the relevant ranges to distinguish actual executable calls from comments, strings, or safe wrappers. Grep therefore provides the most complete and context-efficient initial security scan.

NEW QUESTION # 185
After the web-search agent finds 25 sources containing 120,000 tokens of raw content, the document-analysis agent extracts 15,000 tokens of key insights, and the synthesis agent produces a coherent 3,000-token narrative draft, the coordinator must pass context to the report-generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.
B. Pass the complete accumulated context from all prior agents.
C. Pass the synthesis draft together with a structured source index that maps key claims to their source URLs and relevant excerpts.
D. Pass only the synthesis draft and use a separate post-processing pipeline to match claims to sources and insert citations after report generation.

Answer: C

Explanation:
Option B gives the report generator the two forms of information it actually needs: a compact narrative containing the synthesized conclusions and precise evidence records supporting those conclusions. The source index should map claim identifiers to URLs, document locations, excerpts, and other provenance fields so citations can be generated and verified without reloading 120,000 tokens of raw material.
Anthropic's context-engineering guidance recommends distilling extensive subagent activity into high-signal summaries while preserving critical context and dependencies. Its research architecture similarly treats subagents as intelligent filters that compress large evidence collections before returning relevant findings to the coordinator.
Option A lacks sufficient location-level evidence for reliable citation verification. Option C postpones attribution until after drafting, when claim wording may no longer map cleanly to the original evidence.
Option D maximizes theoretical completeness but introduces extensive irrelevant material, higher token costs, slower processing, and greater context pollution. The synthesis draft plus a structured claim-to-source index maintains evidentiary completeness where it matters while eliminating intermediate searches, duplicate passages, and other low-value context.

NEW QUESTION # 186
A retail organization observes that Claude occasionally gives different wording when summarizing identical customer reviews. The summaries remain accurate. What is the MOST likely explanation?

A. Large language models are probabilistic.
B. The API returned cached responses.
C. Claude is malfunctioning.
D. Retrieval failed.

Answer: A

Explanation:
Claude generates text probabilistically, meaning multiple valid outputs may exist for the same prompt. Small wording differences are expected even when factual content remains consistent.
Lower temperature can improve consistency if deterministic outputs are preferred.

NEW QUESTION # 187
A company wants Claude to summarize thousands of support tickets efficiently. Which design scales BEST?

A. Send all tickets in one request.
B. Process tickets individually and combine summaries.
C. Remove system prompts.
D. Increase temperature.

Answer: B

Explanation:
Hierarchical summarization is an effective strategy for large datasets. Claude summarizes smaller batches first, then combines intermediate summaries into higher-level reports, improving scalability while maintaining important information.

NEW QUESTION # 188
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