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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Production deployment considerations - Evaluation and reliability strategies - Managing context windows and information flow |
| Topic 2: Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns |
| Topic 4: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability |
| Topic 5: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
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You will become accustomed to and familiar with the free demo for Anthropic CCAR-F Exam Questions. Exam self-evaluation techniques in our CCAR-F desktop-based software include randomized questions and timed tests. These tools assist you in assessing your ability and identifying areas for improvement to pass the Anthropic Claude Certified Architect - Foundations exam.
NEW QUESTION # 69
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?
Answer: B
Explanation:
Option A describes the principal reason an independent review context is valuable. The implementing session contains the assumptions, interpretations, and reasoning that produced the refactor. When asked to review its own work, Claude may continue operating within those same assumptions and therefore overlook defects caused by them. A fresh reviewer evaluates the resulting diff and stated requirements independently, without inheriting the implementation narrative.
Anthropic's Claude Code best-practices guidance explicitly recommends an adversarial review step using a fresh subagent or separate context. It explains that the reviewer should see the diff and review criteria rather than the reasoning that produced the change.
NEW QUESTION # 70
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 extraction pipeline processes invoices and extracts line items, subtotals, tax amounts, and grand totals. During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total--sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?
Answer: D
Explanation:
The pipeline must preserve source evidence while making inconsistencies explicit. Option D records the amount stated on the invoice separately from the total derived from extracted line items. A mismatch then becomes a machine-detectable validation condition rather than an invisible extraction defect.
This approach is superior because it does not silently overwrite source data or ask another model to guess which value is correct. Anthropic's evaluation guidance recommends automated, code- based grading whenever the criterion can be expressed deterministically. Arithmetic reconciliation is precisely such a criterion.
In production, the summation should preferably be calculated by application code using normalized decimal values, even though the option describes the model populating calculated_total. The essential design principle remains the same: preserve stated_total, compute an independent total, compare them, and route discrepancies for adjudication.
NEW QUESTION # 71
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.
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?
Answer: A
Explanation:
A mandatory compliance threshold must be enforced outside probabilistic model reasoning. A PreToolUse hook can inspect every attempted process_refund call before execution, compare its amount with the $500 threshold, and deny the call when the limit is exceeded. The host application can then create the human- escalation case using the validated customer and order context.
Anthropic describes hooks as deterministic controls that ensure required actions occur instead of relying on the model to choose them. Agent SDK documentation confirms that a single denying PreToolUse hook blocks the tool call, including MCP tools matched through their qualified tool names. ( https://docs.anthropic.com/en
/docs/claude-code/hooks-guide )
Options A and B improve expected behavior but cannot guarantee compliance. The stated production failure rate demonstrates that prompting alone is insufficient. Option C prevents the refund from completing, but the subsequent escalation still depends on Claude correctly interpreting the returned error and invoking the human tool. It therefore enforces the financial block but not the complete escalation requirement.
Option D enforces both sides of the policy at the orchestration boundary: deny the unauthorized action and route the case to an approved human process. The escalation action should be idempotent and auditable.
Official references/topics: PreToolUse hooks, deterministic policy enforcement, MCP tool interception, human-approval controls.
NEW QUESTION # 72
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 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. Developers report that reviews consistently miss cross-file bugs-for example, a pull request renames a function's parameters, but the review does not identify callers in unchanged files that still use the old argument order.
Evaluation shows that cross-file bugs account for 35% of production incidents originating from reviewed pull requests.
What is the most effective change to the review design?
Answer: D
Explanation:
Option C gives the reviewer access to the evidence currently missing from its prompt. An agentic review can use Grep, Glob, Read, language-server tools, and test commands to locate callers, follow imports, inspect type definitions, and verify whether a suspected compatibility issue actually exists. A turn limit controls cost while still allowing targeted exploration.
Anthropic's context-engineering guidance recommends just-in-time retrieval: agents should retain lightweight references and dynamically load the information needed for the current task instead of preloading a large fixed context. Anthropic has also reported that code-review performance improves when the necessary repositories are available for gathering complete context. Option A may load many irrelevant files while still missing reflection, generated interfaces, dependency injection, or indirect call paths beyond the arbitrary two- hop boundary. Option B cannot reason reliably about code that the model cannot inspect. Option D improves coverage but fragments repository-wide relationships and may duplicate substantial work. An agentic loop can begin with the diff, identify uncertain cross-file effects, search only relevant paths, and report findings only after locating supporting code. Anthropic context-engineering guidance , Claude Code quality report
NEW QUESTION # 73
When analyzing complex legal cases that cite multiple precedents, the document-analysis subagent processes each precedent sequentially. A landmark case citing 12 precedents takes more than three minutes to analyze completely. What is the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?
Answer: D
Explanation:
Option A parallelizes independent precedent analysis while preserving centralized control. The coordinator can partition the 12 precedents into balanced groups, provide each worker with identical extraction and citation requirements, monitor completion or failure, and aggregate the structured results before invoking synthesis.
Anthropic's multi-agent research architecture uses an orchestrator-worker pattern in which a lead agent coordinates specialized subagents operating in parallel. Parallel execution is valuable when tasks are substantially independent, as each precedent can be analyzed without waiting for the preceding case.
Maintaining the fan-out at the coordinator also produces a clear execution trace showing each assignment, status, and returned result.
Option B introduces nested delegation and makes tool usage, permissions, failures, and costs harder for the coordinator to observe. Option C compounds those problems through recursive spawning and risks excessive agent and token consumption. Option D may be appropriate for a large distributed processing platform, but it adds infrastructure without inherently improving the coordinator's reasoning-level observability or defining how results are associated with the case. Coordinator-managed parallel workers provide the required latency reduction with the simplest debuggable architecture.
NEW QUESTION # 74
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