CCARF Practice Paper
Paper 5
Study-guide scenarios · 54 questions. Use flashcard mode for instant feedback or exam mode to simulate the real test.
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Questions in Paper 5
Q1D1 Agentic Architecture & Orchestration — [Scenario: Multi-agent Research System] A document analysis agent discovers that two credible sources contain directly contradictory statist…
Q2D1 Agentic Architecture & Orchestration — [Scenario: Multi-agent Research System] The web-search and document-analysis agents have completed their tasks and returned results to the c…
Q3D5 Context Management & Reliability — [Scenario: Multi-agent Research System] A document analysis subagent frequently fails when processing PDF files: some have corrupted section…
Q4D1 Agentic Architecture & Orchestration — [Scenario: Multi-agent Research System] After running the system on “AI impact on creative industries,” you observe that every subagent comp…
Q5D5 Context Management & Reliability — [Scenario: Multi-agent Research System] The web-search subagent returns results for only 3 of 5 requested source categories (competitor site…
Q6D5 Context Management & Reliability — [Scenario: Multi-agent Research System] The document analysis subagent encounters a corrupted PDF file that it cannot parse. When designing …
Q7D2 Tool Design & MCP Integration — [Scenario: Multi-agent Research System] Production logs show a persistent pattern: requests like “analyze the uploaded quarterly report” are…
Q8D1 Agentic Architecture & Orchestration — [Scenario: Multi-agent Research System] A colleague proposes that the document analysis agent should send its results directly to the synthe…
Q9D2 Tool Design & MCP Integration — [Scenario: Multi-agent Research System] In your system design, you gave the document analysis agent access to a general-purpose tool `fetch_…
Q10D1 Agentic Architecture & Orchestration — [Scenario: Multi-agent Research System] While researching a broad topic, you observe that the web-search agent and the document analysis age…
Q11D5 Context Management & Reliability — [Scenario: Multi-agent Research System] Production monitoring shows inconsistent synthesis quality. When aggregated results are ~75K tokens,…
Q12D5 Context Management & Reliability — [Scenario: Multi-agent Research System] In testing, the combined output of the web-search agent (85K tokens including page content) and the …
Q13D1 Agentic Architecture & Orchestration — [Scenario: Claude Code for Continuous Integration] Your team uses Claude Code for generating code suggestions, but you notice a pattern: non…
Q14D4 Prompt Engineering & Structured Output — [Scenario: Claude Code for Continuous Integration] Your code review component is iterative: Claude analyzes the changed file, then may reque…
Q15D3 Claude Code Configuration & Workflows — [Scenario: Claude Code for Continuous Integration] Your CI/CD system runs three Claude-based analyses: (1) fast style checks on every PR tha…
Q16D4 Prompt Engineering & Structured Output — [Scenario: Claude Code for Continuous Integration] Your automated reviews find real issues, but developers report the feedback is not action…
Q17D3 Claude Code Configuration & Workflows — [Scenario: Claude Code for Continuous Integration] Your CI pipeline includes two Claude-based code review modes: a pre-merge-commit hook tha…
Q18D4 Prompt Engineering & Structured Output — [Scenario: Claude Code for Continuous Integration] Your automated review analyzes comments and docstrings. The current prompt instructs Clau…
Q19D4 Prompt Engineering & Structured Output — [Scenario: Claude Code for Continuous Integration] Your automated review generates test-case suggestions for each PR. Reviewing a PR that ad…
Q20D4 Prompt Engineering & Structured Output — [Scenario: Claude Code for Continuous Integration] After an initial automated review identifies 12 findings, a developer pushes new commits …
Q21D3 Claude Code Configuration & Workflows — [Scenario: Claude Code for Continuous Integration] Your pipeline script runs `claude "Analyze this pull request for security issues"`, but t…
Q22D4 Prompt Engineering & Structured Output — [Scenario: Claude Code for Continuous Integration] A pull request changes 14 files in an inventory tracking module. A single-pass review tha…
Q23D4 Prompt Engineering & Structured Output — [Scenario: Claude Code for Continuous Integration] Your automated code review averages 15 findings per pull request, and developers report a…
Q24D3 Claude Code Configuration & Workflows — [Scenario: Claude Code for Continuous Integration] Your team wants to reduce API costs for automated analysis. Currently, synchronous Claude…
Q25D4 Prompt Engineering & Structured Output — [Scenario: Code Generation with Claude Code] You asked Claude Code to implement a function that transforms API responses into an internal no…
Q26D3 Claude Code Configuration & Workflows — [Scenario: Code Generation with Claude Code] You need to add Slack as a new notification channel. The existing codebase has clear, establish…
Q27D3 Claude Code Configuration & Workflows — [Scenario: Code Generation with Claude Code] Your CLAUDE.md file has grown to 400+ lines containing coding standards, testing conventions, a…
Q28D3 Claude Code Configuration & Workflows — [Scenario: Code Generation with Claude Code] You’re tasked with restructuring your team’s monolithic application into microservices. This im…
Q29D3 Claude Code Configuration & Workflows — [Scenario: Code Generation with Claude Code] You find that including 2–3 full endpoint implementation examples as context significantly impr…
Q30D3 Claude Code Configuration & Workflows — [Scenario: Code Generation with Claude Code] Your team created a `/migration` skill that generates database migration files. It takes the mi…
Q31D3 Claude Code Configuration & Workflows — [Scenario: Code Generation with Claude Code] You want to create a custom slash command `/review` that runs your team’s standard code review …
Q32D3 Claude Code Configuration & Workflows — [Scenario: Code Generation with Claude Code] You create a custom skill `/explore-alternatives` that your team uses to brainstorm and evaluat…
Q33D5 Context Management & Reliability — [Scenario: Code Generation with Claude Code] You’re adding error-handling wrappers around external API calls across a 120-file codebase. The…
Q34D4 Prompt Engineering & Structured Output — [Scenario: Customer Support Agent] Your agent handles single-issue requests with 94% accuracy (e.g., “I need a refund for order #1234”). But…
Q35D1 Agentic Architecture & Orchestration — [Scenario: Customer Support Agent] After calling `get_customer` and `lookup_order`, the agent has all available system data but still faces …
Q36D4 Prompt Engineering & Structured Output — [Scenario: Customer Support Agent] Production metrics show that when resolving complex billing disputes or multi-order returns, customer sat…
Q37D1 Agentic Architecture & Orchestration — [Scenario: Customer Support Agent] Production metrics show your agent averages 4+ API loops per resolution. Analysis reveals Claude often re…
Q38D2 Tool Design & MCP Integration — [Scenario: Customer Support Agent] Production logs show a consistent pattern: when customers include the word “account” in their message (e.…
Q39D4 Prompt Engineering & Structured Output — [Scenario: Customer Support Agent] Production logs show the agent sometimes chooses `get_customer` when `lookup_order` would be more appropr…
Q40D2 Tool Design & MCP Integration — [Scenario: Conversational AI Architecture Patterns] Your `remove_team_member` tool uses a `dry_run: boolean` parameter for previewing impact…
Q41D2 Tool Design & MCP Integration — [Scenario: Conversational AI Architecture Patterns] Production monitoring shows your `search_catalog` tool fails 12% of the time: 8% are net…
Q42D4 Prompt Engineering & Structured Output — [Scenario: Conversational AI Architecture Patterns] Over several turns discussing investment strategy, a user stated "I have a very low risk…
Q43D5 Context Management & Reliability — [Scenario: Conversational AI Architecture Patterns] Users refine playlist preferences over multiple conversation turns. Two messages after a…
Q44D5 Context Management & Reliability — [Scenario: Conversational AI Architecture Patterns] After a 40-minute cooking session, the conversation reaches 78,000 tokens. History inclu…
Q45D5 Context Management & Reliability — [Scenario: Conversational AI Architecture Patterns] Users report that during extended conversations the assistant loses track of earlier top…
Q46D5 Context Management & Reliability — [Scenario: Conversational AI Architecture Patterns] Users report that latency increases and costs rise when conversations exceed 50 turns. W…
Q47D5 Context Management & Reliability — [Scenario: Conversational AI Architecture Patterns] After three months of weekly sessions, conversation history grows to 85,000 tokens. When…
Q48D4 Prompt Engineering & Structured Output — [Scenario: Conversational AI Architecture Patterns] During QA testing, Claude follows system prompt guidelines for the first 10–15 turns, bu…
Q49D4 Prompt Engineering & Structured Output — [Scenario: Conversational AI Architecture Patterns] Your AI tutor has a 2,800-token system prompt defining teaching methodology and adaptati…
Q50D4 Prompt Engineering & Structured Output — [Scenario: Conversational AI Architecture Patterns] Your assistant must maintain an enthusiastic tone, explain its reasoning, and ask clarif…
Q51D4 Prompt Engineering & Structured Output — [Scenario: Conversational AI Architecture Patterns] Users report repetitive response openings like "Certainly!" and "I'd be happy to help!" …
Q52D5 Context Management & Reliability — [Scenario: Conversational AI Architecture Patterns] A webhook notifies your system that a user's package has shipped while the user is activ…
Q53D4 Prompt Engineering & Structured Output — [Scenario: Conversational AI Architecture Patterns] Users frequently send requests like "Book a venue for the party." The assistant asks 4+ …
Q54D4 Prompt Engineering & Structured Output — [Scenario: Conversational AI Architecture Patterns] Your assistant uses a contractor-persona system prompt. Early turns follow the rules, bu…