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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Claude Code Configuration & Workflows	20%	- Claude Code 1. Configuration and project setup 2. Development workflows 3. Agent skills 4. Code generation and automation
Topic 3: Prompt Engineering & Structured Output	20%	- Prompt design 1. Few-shot prompting 2. Output validation 3. Structured output and JSON schemas 4. Prompt engineering techniques
Topic 4: Tool Design & MCP Integration	18%	- Tool integration 1. Resource and server integration 2. Tool interface design 3. Model Context Protocol (MCP) 4. Tool selection and safety
Topic 5: Context Management & Reliability	15%	- Context handling 1. Cost and performance optimization 2. Reliability and evaluation 3. Context window management 4. Memory strategies

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Quiz 2026 Anthropic CCAR-F: First-grade Claude Certified Architect - Foundations Practice Braindumps

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q128-Q133):

NEW QUESTION # 128
The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

A. The verification tool is clearing the agent's internal state after each successful validation step.
B. The prompt lacks instructions telling Claude to remember information across multiple exchanges.
C. The conversation history isn't being passed in subsequent API requests.
D. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.

Answer: C

Explanation:
Claude relies on the conversation history included in each API request to maintain continuity across turns. If earlier messages are not passed forward, the agent will not have access to previously provided details and may ask for information again as though the exchange never occurred.

NEW QUESTION # 129
The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?

A. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts
B. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
C. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent
D. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes

Answer: A

Explanation:
Purpose-specific tools with clear input and output contracts remove ambiguity about what kind of analysis is expected. Separate extraction, summarization, and verification tools make the agent choose the correct operation and produce outputs in the format downstream agents can reliably use.

NEW QUESTION # 130
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.
Your productivity agent connects to three MCP servers: an issue tracker with search_issues , get_issue , and create_comment ; a documentation wiki with search_docs , get_page , and list_spaces ; and a database explorer with run_query , get_schema , and list_databases . For cross-system questions such as, "Which database tables are affected by the authentication refactor in PROJ-1234?", the agent makes eight to ten sequential exploratory calls, lacks visibility into each server's available content, and exhausts context before completing complex investigations.
What architectural change best leverages MCP capabilities to address these problems?

A. Add a prepare_investigation tool to each server that accepts a natural-language question and returns relevant summaries.
B. Consolidate all three systems into one unified MCP server with cross-referencing capabilities.
C. Expose each server's content catalog as MCP resources, including issue summaries, documentation hierarchies, and database schemas.
D. Add an orchestrator that routes each question to one server based on keywords.

Answer: C

Explanation:
Option C uses MCP resources for their intended purpose: exposing contextual data that applications and agents can discover and read without treating every lookup as an action-oriented tool invocation. Anthropic's Claude Code MCP documentation states that Claude Code automatically provides mechanisms to list and read resources exposed by connected MCP servers. Resources may contain text, JSON, structured data, or other server-provided content. The official MCP server concepts likewise explain that resources expose information from files, APIs, and databases through identifiable, discoverable URIs. Publishing issue summaries, the wiki hierarchy, and database schemas gives the agent an initial map of available evidence. It can identify relevant systems and retrieve only the necessary records before making targeted tool calls. Option A creates another opaque, high-level tool whose output quality depends on each server interpreting the investigation correctly.
Option B produces tight coupling and increases operational complexity. Option D prevents the cross-system investigation required by the question. Resource catalogs improve discoverability, reduce blind exploration, and preserve tools for searches, queries, and mutations that genuinely require execution.

NEW QUESTION # 131
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.
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers dismiss approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project' s conventions as persistent context during every review?

A. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap,"
"large class," or "deprecated import" before results reach developers.
B. Configure the review to analyze only the changed lines in the diff without the surrounding file context, reducing the amount of code the model evaluates.
C. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
D. Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.

Answer: D

Explanation:
Option A supplies the missing project context before Claude evaluates the code. Anthropic's CLAUDE.md documentation defines project-level files as persistent, version-controlled instructions for coding standards, architecture, workflows, and conventions. The described exceptions are stable repository facts: force- unwrapping is permitted in tests, coordinator classes follow an intentional pattern, and an internally maintained dependency remains approved despite its public deprecation status. Recording these facts concisely allows every review session to interpret the patterns correctly.
Option B removes surrounding evidence and would make architectural and cross-file judgments less reliable.
Option C hides findings after generation and can suppress genuine bugs containing the same keywords.
Option D adds noise to source files and distributes policy across individual call sites instead of documenting it centrally. The CLAUDE.md entries should be specific and scoped-for example, limiting the force- unwrapping exception to test paths rather than approving it globally. Anthropic notes that shorter, precise instructions are followed more consistently, so the file should state the accepted convention, its scope, and any conditions that would still make the pattern reportable.

NEW QUESTION # 132
A developer includes multiple unrelated tasks inside one extremely long prompt. What is the MOST likely outcome?

A. Increased prompt clarity.
B. Improved reasoning.
C. Faster inference.
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 # 133
......

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