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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q80-Q85):

NEW QUESTION # 80
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.
Your invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation--for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?

A. Add stricter schema constraints with detailed field descriptions to prevent the model from initially generating invalid values.
B. When validation fails, make a follow-up request containing the document, extraction, and validation errors so the model can correct the result.
C. Implement post-processing logic that automatically corrects common errors, such as recalculating totals from line items when sums do not match.
D. Retry the extraction up to three times when validation fails, accepting the first result that passes validation.

Answer: B

Explanation:
Option B creates a targeted correction loop using information the first extraction did not have: the validator's precise failure report. Anthropic's prompting guidance describes prompt chaining as appropriate when an application must inspect intermediate output or enforce a specific pipeline, with self-correction following the pattern generate, review against criteria, and refine. Here, deterministic validators identify arithmetic mismatches and invalid vendor-ID formats. Returning the source document, original extraction, and explicit errors lets Claude revise only the defective fields while preserving valid data. The corrected record must then be validated again before acceptance, with a bounded retry count and human-review fallback.

NEW QUESTION # 81
Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

A. Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.
B. Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.
C. Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.
D. Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.

Answer: B

Explanation:
Structured error output gives the agent enough information to respond usefully. Including the error type, whether the issue is recoverable, and actionable guidance allows the agent to distinguish malformed tracking IDs, missing shipments, and temporary API failures, then suggest the correct next step to the user.

NEW QUESTION # 82
A developer includes multiple unrelated tasks inside one extremely long prompt. What is the MOST likely outcome?

A. Improved reasoning.
B. Faster inference.
C. Increased prompt clarity.
D. Reduced instruction clarity.

Answer: D

Explanation:
Combining many unrelated objectives in a single prompt increases ambiguity and makes it harder for Claude to identify priorities. Separating tasks into focused prompts generally produces more reliable and maintainable outputs.

NEW QUESTION # 83
Which statement BEST describes Retrieval-Augmented Generation?

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

Answer: D

NEW QUESTION # 84
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?

A. Replace the default prompt using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only the project rules.
B. Keep the default initialization and add --exclude-dynamic-system-prompt-sections to improve prompt- cache reuse across CI runners.
C. Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to load the required project standards explicitly while skipping automatic discovery.
D. Run in --bare mode and repeat all review criteria directly in the -p prompt for every invocation.

Answer: C

Explanation:
Option B removes the identified startup work while deliberately restoring the one source of project context the reviews require. Anthropic documents that --bare skips automatic discovery of hooks, skills, plugins, MCP servers, auto memory, and CLAUDE.md files. It is specifically intended for CI and scripted execution where fast, reproducible startup behavior is more important than loading every locally configured extension.
Because bare mode also skips the root CLAUDE.md, the pipeline must supply that content explicitly. -- append-system-prompt-file ./CLAUDE.md loads the standards while retaining Claude Code's default coding- agent behavior and tool guidance. Option A replaces the default system prompt but does not, by itself, establish the same minimal startup path as --bare; replacement also discards valuable default coding instructions. Option C can work technically but duplicates repository policy inside every pipeline invocation and creates configuration drift. Option D improves prompt-cache reuse by relocating machine-specific prompt sections, but it does not eliminate discovery of hooks, MCP servers, plugins, skills, and nested instructions.
Bare mode plus an explicitly appended standards file directly addresses both performance and policy requirements. Claude Code bare-mode documentation

NEW QUESTION # 85
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