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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q45-Q50):

NEW QUESTION # 45
Which statement BEST describes Retrieval-Augmented Generation?

A. It changes token pricing.
B. It permanently retrains Claude.
C. It supplies relevant external knowledge during inference.
D. It compresses model weights.

Answer: C

Explanation:
RAG retrieves relevant documents at inference time and includes them within the prompt. The model itself is unchanged, allowing organizations to incorporate current enterprise knowledge without retraining foundation models.

NEW QUESTION # 46
Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (5% of calls), and business errors ("Order exceeds
30 day return window", "Item already refunded") that are permanent (12% of calls). Monitoring shows the agent wastes 3-4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude. What's the most effective way to reduce wasted retries while improving customer-facing response quality?

A. Add a check_refund_eligibility tool that must be called before process_refund to prevent business rule violations.
B. Implement automatic retry logic at the tool level for technical errors only, passing business errors to Claude without retries.
C. Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.
D. Return structured error responses with retriable: false for business errors and a customer-friendly explanation for Claude to use.

Answer: D

Explanation:
Structured error responses let the agent distinguish permanent business-rule failures from transient technical failures without parsing plain text. Marking business errors as non-retriable and including a customer-friendly explanation prevents wasted retry turns while helping the agent give a clear, useful response to the customer.

NEW QUESTION # 47
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.
During initial testing of the automated review pipeline, you notice that reviews on large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8?12 per run because of extensive agentic loops. Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort once it reaches both a fixed iteration count and a fixed dollar amount, enforced by Claude Code itself rather than by the surrounding job runner.
Which configuration change directly enforces both per-invocation caps?

A. Set --permission-mode dontAsk to automatically deny tool-permission requests not included in the explicitly allowed set.
B. Switch the --model flag to a smaller, less expensive model so each iteration uses fewer tokens and has a lower per-call cost.
C. Set timeout-minutes: 5 on the GitHub Actions job step and monitor per-run costs through the Anthropic Console usage dashboard.
D. Add --max-turns 10 --max-budget-usd 2.00 to the claude -p invocation to cap iterations and spending.

Answer: D

Explanation:
Option C applies the two native stopping controls required by the question. Anthropic's Claude Code CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode and --max-budget-usd as the maximum API expenditure for that invocation. When the turn limit is reached, the run exits with an error. Spending by subagents counts toward the budget cap, and current Claude Code versions stop remaining background subagents when the limit is reached. These flags therefore control the complete invocation rather than merely one model request.

NEW QUESTION # 48
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.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?

A. Read all files containing auth, login, permission, or token in their filenames or contents.
B. Launch parallel subagents to explore every service simultaneously, and then synthesize their findings into an architectural overview.
C. Read all CLAUDE.md and README files first, and then ask the engineer to identify the 10?5 most important authentication files.
D. Use Grep to locate authentication entry points, read those files, and then follow imports and function calls incrementally to map the authentication flow.

Answer: D

Explanation:
Option D builds an evidence-based architectural map without indiscriminately loading hundreds of files. Authentication entry points may include route handlers, middleware registration, token- validation functions, session constructors, or identity-provider callbacks. After Grep identifies those anchors, targeted Read operations can establish their responsibilities, while imports, call sites, and configuration references reveal the downstream authorization flow. Anthropic's large- codebase guidance recommends scoping Claude to the portion of a repository touched by the task because unrelated instructions and file reads consume tokens and degrade performance. Its common workflows guidance similarly recommends beginning broadly and narrowing into specific components.

NEW QUESTION # 49
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. 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.
B. 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.
C. 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.
D. 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.

Answer: B

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.

NEW QUESTION # 50
......

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