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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q48-Q53):

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, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks your agent to add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn't specified which modules to prioritize.
How should the agent decompose this open-ended task?

A. Systematically read all 200 files to create a complete function inventory before writing any tests, ensuring the testing plan accounts for every function before beginning.
B. Use Glob and Grep to map codebase structure, identify heavily-coupled modules, create a prioritized plan for high-impact areas, and revise as dependencies are discovered.
C. Start writing tests for the first module alphabetically, using test failures and imports to discover related files organically.
D. Create a fixed testing schedule upfront based on directory structure, allocating equal effort to each top- level directory regardless of code complexity or business importance.

Answer: B

Explanation:
The task is open-ended because neither the critical modules nor the required testing sequence is known in advance. The agent should first use lightweight discovery tools to map the repository, locate existing tests, identify central modules, and determine which components have high fan-in, business significance, complex branching, or extensive external dependencies. It can then produce an initial risk-based testing plan and refine it as new dependency information appears.
Anthropic distinguishes predefined workflows from agents that dynamically control their processes and tool usage. Agents are appropriate when the required steps cannot be reliably hardcoded and must adapt to environmental evidence. During execution, they should obtain ground truth through tool results and use that feedback to determine subsequent actions. ( https://www.anthropic.com/research/building-effective-agents ) Anthropic also identifies orchestrator-worker designs as suitable for complex coding and search tasks where the necessary subtasks depend on what the investigation reveals. ( https://www.anthropic.com/research
/building-effective-agents )
Option A assigns effort using directory boundaries rather than risk. Option C exhausts context before delivering value. Option D uses alphabetical order, which has no relationship to impact or coverage priority.
Option B establishes an evidence-driven decomposition: discover, prioritize, test high-impact paths, measure results, and revise the plan as dependencies and uncovered risks emerge.
Official references/topics: Dynamic Task Decomposition; Adaptive Agent Loops; Orchestrator-Workers; Risk-Based Test Planning.

NEW QUESTION # 49
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.
Engineers frequently ask the agent to cross-reference code changes with Jira tickets during reviews- checking ticket descriptions, acceptance criteria, and recent comments. This currently requires manually copying and pasting content into conversations. The team wants the agent to access this standard Jira ticket data directly.
What is the most effective approach?

A. Build a custom MCP server wrapping Jira's API with tools designed specifically for this team's code- review workflow.
B. Use the Bash tool with curl to call Jira's REST API, including authentication headers and parsing JSON responses inline.
C. Export Jira tickets to Markdown files in the repository that the agent accesses using the Read tool.
D. Integrate an existing Jira MCP server that exposes tickets, comments, and metadata through discoverable tool interfaces.

Answer: D

Explanation:
Option D uses the established integration mechanism without creating unnecessary infrastructure. Anthropic's Claude Code MCP documentation specifically recommends connecting an MCP server when users repeatedly copy information from an external system, such as an issue tracker, into conversations. Once connected, the server exposes Jira operations through named, schema-defined tools that Claude can discover and invoke directly. This allows the agent to retrieve ticket descriptions, acceptance criteria, comments, and metadata while preserving the server's authentication and access controls.
Option A exposes authentication details to shell commands and requires the agent to construct requests and parse responses repeatedly. Option B is justified only when no existing server provides the required capabilities or the workflow requires highly specialized operations. The question requires standard Jira information, so building and maintaining another server is unnecessary. Option C creates stale, manually synchronized copies that lose permissions, current comments, and authoritative ticket state. The team should therefore select a trusted Jira MCP implementation, configure appropriate read-only scopes where possible, and verify its tool descriptions and data-access boundaries before enabling it.

NEW QUESTION # 50
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. Add --max-turns 10 --max-budget-usd 2.00 to the claude -p invocation to cap iterations and spending.
B. Set timeout-minutes: 5 on the GitHub Actions job step and monitor per-run costs through the Anthropic Console usage dashboard.
C. Switch the --model flag to a smaller, less expensive model so each iteration uses fewer tokens and has a lower per-call cost.
D. Set --permission-mode dontAsk to automatically deny tool-permission requests not included in the explicitly allowed set.

Answer: A

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.
Option A lowers expected cost per iteration but establishes no hard ceiling on either iterations or total expenditure. Option B limits wall-clock execution at the CI-runner level and only observes spending retrospectively; it does not enforce a Claude Code budget. Option D controls permission behavior, not iteration count, execution time, or token spending. The pipeline should use both flags, capture the resulting nonzero exit or structured error status, and report whether a review ended normally or was terminated by a configured operational limit.

NEW QUESTION # 51
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. 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.
D. 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.

Answer: D

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 # 52
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, Glob) and integrates with Model Context Protocol (MCP) servers.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match - the file has repetitive docstrings, variable names, and structural patterns.
What's the most reliable way to complete this insertion?

A. Use Bash to append the function definition to the end of the file using heredoc syntax
B. Use Edit with an extremely long old_string capturing 30+ lines of context to guarantee uniqueness
C. Use Read to load the file, add the function at the appropriate location, then Write the updated file
D. Use Edit's replace_all parameter to target a common pattern and embed the new function in the replacement text

Answer: C

Explanation:
When Edit cannot identify a unique match, rewriting the fully read file is the most reliable way to control the exact insertion point. The other options are brittle, place the function incorrectly, or risk unintended replacements.

NEW QUESTION # 53
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