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Pass the Anthropic Claude Certified Architect CCAR-F Questions and answers with CertsForce

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Questions # 1:

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.

A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed. Grep shows the library is imported in 45 files across several modules.

What’s the most effective approach?

Options:

A.

Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.


B.

Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.


C.

Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.


D.

Paste the migration guide’s breaking changes into your prompt and use direct execution to update all usages across the 45 files.


Expert Solution
Questions # 2:

Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the pipeline?

Options:

A.

Interleave the implementation and tests in the prompt, presenting each function immediately before its test cases.


B.

Add explicit instructions requiring Claude to enumerate every conditional branch and exception path, then verify that each path has a corresponding test assertion.


C.

Implement a two-pass pipeline in which one model call extracts all conditional branches and another cross-references them against test assertions.


D.

Include few-shot examples showing code with an uncovered branch and the corresponding review comment identifying the missing test case.


Expert Solution
Questions # 3:

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools (get_customer, lookup_order, process_refund, escalate_to_human). Your target is 80%+ first-contact resolution while knowing when to escalate.

Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team’s goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates.

Which approach achieves this guarantee?

Options:

A.

Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.


B.

Split the workflow into two sequential agent invocations—a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.


C.

Add orchestration-layer code that checks the agent’s outcome after each loop termination—if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.


D.

Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.


Expert Solution
Questions # 4:

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 automated review jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay results from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout the monorepo.

You need to reduce startup time while ensuring reviews still enforce the coding standards documented in the root-level CLAUDE.md file.

What is the most effective approach?

Options:

A.

Replace the default prompt using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only the project rules.


B.

Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to load the required project standards explicitly while skipping automatic discovery.


C.

Run in --bare mode and repeat all review criteria directly in the -p prompt for every invocation.


D.

Keep the default initialization and add --exclude-dynamic-system-prompt-sections to improve prompt-cache reuse across CI runners.


Expert Solution
Questions # 5:

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.

Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn’t exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: { " isError " : true, " content " : [{ " type " : " text " , " text " : " Operation failed " }]} . The agent cannot distinguish between error types.

What’s the most effective improvement?

Options:

A.

Enhance error responses with structured metadata—include error_category (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.


B.

Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.


C.

Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.


D.

Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.


Expert Solution
Questions # 6:

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.

After your daily batch of 10,000 documents completes, 300 documents (3%) fail with context_length_exceeded errors. The results file identifies each failure by custom_id.

What is the most cost-effective approach to process these failures?

Options:

A.

Resubmit the entire 10,000-document batch using a model tier with a larger context window.


B.

Reprocess the entire batch with prompt caching enabled to reduce the cost of retrying requests with identical system prompts.


C.

Increase the max_tokens parameter for the 300 failed documents and resubmit them in a new batch.


D.

Resubmit only the 300 failed documents after chunking them into smaller pieces, and then combine the partial extractions.


Expert Solution
Questions # 7:

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.

The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (≥85%) also contain errors—cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.

What approach is most effective?

Options:

A.

Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.


B.

Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.


C.

Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.


D.

Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.


Expert Solution
Questions # 8:

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 submits two requests:

    Request A: “Rename the getUserData function to fetchUserProfile everywhere it’s used.”

    Request B: “Improve error handling throughout the data processing module—add try/catch blocks, meaningful error messages, and ensure failures don’t silently corrupt data.”

For which request does specifying an explicit multi-phase workflow (such as analyze → propose → implement with review) most improve outcome quality?

Options:

A.

Neither request benefits significantly


B.

Request A, the function rename task


C.

Both requests benefit equally


D.

Request B, the error handling task


Expert Solution
Questions # 9:

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.

A developer uses Claude Code to refactor a function during a development session. Before committing, the developer asks the same Claude session to review the code for issues. Later, a separate automated CI review catches several bugs that the same-session review missed.

What best explains this discrepancy?

Options:

A.

Claude retains the implementation context and prior decisions in the session, making it less likely to challenge assumptions underlying its own changes.


B.

The session’s context window necessarily became full, leaving insufficient capacity for meaningful review.


C.

The CI review must have used a more specific prompt, while the developer’s review request was too general.


D.

The CI environment can access the full repository, while a local Claude Code session can access only the current file.


Expert Solution
Questions # 10:

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.

Your team’s CLAUDE.md includes a rule: “Use 4-space indentation and always run Prettier formatting.” Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting—sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis (“IMPORTANT: You MUST use Prettier formatting”) reduces violations to about 15%, but doesn’t eliminate them.

What is the most effective way to ensure all generated code is consistently formatted?

Options:

A.

Extract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.


B.

Add a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.


C.

Split the formatting rules into path-scoped .claude/rules/ files that load when Claude works on matching file types.


D.

Configure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.


Expert Solution
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