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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q94-Q99):

NEW QUESTION # 94
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 system has been running for 3 weeks and human reviewers have corrected 847 extractions.
Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or
"a splash," the model either invents specific amounts or leaves fields empty--accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A. Add few-shot examples to your prompt demonstrating correct handling of informal measurements--extracting them verbatim rather than converting or omitting them.
B. Fine-tune the model on the 847 corrected extractions.
C. Update your JSON schema to add a "measurement_type" enum field (precise/informal).
D. Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.

Answer: A

Explanation:
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful, splash, and pinch exactly.
Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities.

NEW QUESTION # 95
Your CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day through the synchronous API. Reviews are non-blocking-developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API because it offers a 50% cost reduction. What factor most determines whether batch processing is appropriate for this use case?

A. Whether each review can be structured as a single request without multi-turn refinement.
B. Whether reducing per-review latency from 30-60 seconds to near-instantaneous delivery matters to your workflow.
C. Whether your result-processing system can handle reviews arriving in a different order from the order in which they were submitted.
D. Whether review feedback arriving up to 24 hours after pull-request creation remains actionable.

Answer: D

Explanation:
The decisive trade-off is whether the workflow can tolerate asynchronous completion. Anthropic's Message Batches API documentation states that batch requests receive a 50% discount and that most batches finish within one hour, but processing can continue for as long as 24 hours. Therefore, the review process is suitable for batching only if potentially delayed feedback remains useful.
Option C directly tests that operational requirement. The reviews are already non-blocking, which makes batching promising, but non-blocking does not automatically mean that feedback delivered many hours later still has value. If developers have already merged and moved to unrelated work, delayed security findings may increase remediation cost or remain unaddressed.
Option A is manageable through each request's unique custom_id, which allows results to be correlated regardless of return order. Option B is incorrect because batch requests can contain supported multi-turn conversation histories and tool-use configurations; each request is processed independently, but it need not represent a single conversational turn. Option D reverses the relevant consideration: batch processing is not intended to provide near-instantaneous responses. The real question is whether the cost saving justifies its longer and nondeterministic completion time.

NEW QUESTION # 96
Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail. What is the most effective way to handle this?

A. Switch from tool_use to prompting Claude to return findings as a Markdown list.
B. Add retry logic that detects truncated JSON and resends the request with instructions to report only critical-and high-severity findings.
C. Increase max_tokens to the model's maximum and instruct Claude to keep each finding description under 50 words.
D. Split the review into multiple API calls that each analyze a subset of the changed files, and then merge the resulting findings arrays.

Answer: D

Explanation:
Option A reduces the maximum output required from any single response while preserving the structured schema and complete severity range. The pipeline can partition files into coherent groups, execute bounded reviews, validate each returned array, and merge and deduplicate findings using stable fields such as file path, line number, category, and description.
Anthropic's stop-reason documentation confirms that max_tokens means generation reached the configured output limit and the response must be treated as incomplete. Structured output constraints can guarantee schema-valid generation when completion succeeds, but they cannot create unlimited output capacity. A large findings array can still exceed the available token budget.

NEW QUESTION # 97
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent
- this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate). How should you achieve guaranteed compliance?

A. Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
B. Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
C. Modify the refund tool to return an error with message "Amount exceeds policy limit - please escalate" when threshold is exceeded.
D. Implement a hook to intercept tool calls; when the refund process amount exceeds $500, block it and invoke human escalation.

Answer: D

Explanation:
Enforcing the policy outside of the model via a PreToolUse hook guarantees compliance. By intercepting high-value refund attempts and triggering human escalation, the system ensures the rule is applied consistently regardless of prompt instructions or agent behavior.

NEW QUESTION # 98
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. Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*"]), loading rules only when editing matching files.
B. Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.
C. Split content into subdirectory CLAUDE.md files (/terraform/CLAUDE.md,
/kubernetes/CLAUDE.md), so Claude loads directory-specific guidance.
D. Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g, "## Terraform Conventions"), improving organization and readability.

Answer: A

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 # 99
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