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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q179-Q184):

NEW QUESTION # 179
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 includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API request using a Sonnet-tier Claude model. The release notes are not needed until the following morning, providing approximately 12 hours of acceptable latency.
Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality.
Which approach satisfies all these constraints?

A. Concatenate all 200 commit messages into one Messages API request because reducing the number of requests always reduces token costs.
B. Submit the 200 requests through the Message Batches API with unique custom_id values and retrieve the results after the batch finishes.
C. Issue the 200 Messages API requests concurrently because parallel execution reduces the per- token price.
D. Replace the Sonnet-tier model with a Haiku-tier model to obtain a lower per-token price.

Answer: B

Explanation:
Option B applies Anthropic's dedicated asynchronous bulk-processing mechanism while preserving the existing model and prompt for every commit. The Message Batches API accepts independent Messages API requests, each identified by a unique custom_id, and charges both input and output usage at 50% of standard API prices. The approximately 12-hour latency allowance makes the release-notes workload well suited to batching because immediate results are unnecessary.

NEW QUESTION # 180
Your expense reimbursement agent processes employee requests using a
process_reimbursement tool. Company policy requires that reimbursements above $500 must be approved by a manager before funds are disbursed. The agent handles hundreds of requests daily, and you need the threshold enforcement to be tamper-proof regardless of how the agent is prompted. Which design ensures the $500 approval threshold cannot be bypassed?

A. The process_reimbursement tool accepts an approved_by_manager: boolean parameter. The system prompt instructs the agent to only set this to true after confirming that a manager has approved the request. A nightly audit script reviews all reimbursements where approved_by_manager was set to true.
B. Provide two tools: auto_reimburse (hard-coded limit of $500) and request_manager_approval.
Include detailed system prompt instructions telling the agent to check the amount and call the appropriate tool. Add a PostToolUse hook that logs which tool was called for auditing.
C. Implement the threshold check in a PreToolUse hook that inspects the amount parameter before process_reimbursement executes. If the amount exceeds $500, the hook modifies the tool call to add a requires_approval: true flag, which the tool checks before disbursing.
D. The process_reimbursement tool accepts amount and details, and internally enforces the threshold: amounts <$500 are auto-disbursed and the tool returns a success confirmation; amounts >$500 cause the tool to create a pending approval request and return a status indicating manager review is pending.

Answer: D

Explanation:
Enforcing the approval threshold within the tool itself makes it tamper-proof and independent of agent behavior or prompts. The tool controls disbursement and ensures manager approval is required for amounts over $500, preventing accidental or intentional bypass.

NEW QUESTION # 181
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 reducing per-review latency from 30-60 seconds to near-instantaneous delivery matters to your workflow.
B. Whether each review can be structured as a single request without multi-turn refinement.
C. Whether review feedback arriving up to 24 hours after pull-request creation remains actionable.
D. Whether your result-processing system can handle reviews arriving in a different order from the order in which they were submitted.

Answer: C

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 # 182
Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file.
What is the most effective approach?

A. Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to explicitly load your project standards while skipping all automatic discovery.
B. Replace the default prompt entirely by using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only your project rules.
C. Keep the default initialization and add --exclude-dynamic-system-prompt-sections to reduce per- machine prompt variability and improve prompt-cache hit rates across runners.
D. Run in --bare mode and specify all review criteria directly in the -p prompt argument for every CI invocation, without referencing external files.

Answer: A

Explanation:
Claude Code's --bare option is specifically designed for faster scripted execution. It skips automatic discovery of CLAUDE.md files, hooks, skills, plugins, MCP servers, and auto-memory while retaining essential built-in capabilities such as Bash, file reading, and file editing. Because --bare also prevents automatic loading of the root CLAUDE.md, the required standards must be added explicitly.
Option C accomplishes both objectives. --append-system-prompt-file ./CLAUDE.md loads the project standards into the current invocation while preserving Claude Code's default coding-agent system prompt and tool-use guidance. According to the official Claude Code CLI reference , append flags add file contents to the default system prompt, whereas replacement flags discard that default guidance.
Option A could work functionally but duplicates the standards in every command and creates configuration drift. Option B replaces the complete default prompt, removing useful coding, safety, and tool-use instructions. Option D improves prompt-cache reuse across different machines, but it does not disable hooks, plugins, skills, MCP servers, or CLAUDE.md discovery and therefore does not directly address the measured initialization delay.

NEW QUESTION # 183
During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?

A. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
B. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
C. Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
D. Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.

Answer: D

Explanation:
The failure originates from an underspecified tool contract. A precise description tells Claude where the required parameter must come from and prevents it from fabricating an identifier.
Server-side validation in D is still valuable as a safeguard, but it detects the error rather than addressing the agent's decision-making. Anthropic identifies detailed tool and parameter descriptions as the most important factor in reliable tool use.

NEW QUESTION # 184
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