CCA Professional · Exam Scenarios & Domain Practice

All 7 domains | 63 questions | 120 minutes

This lesson walks through one representative scenario per domain, applies the trap pattern library, and explains how to reason through each question format.

How to Read a Professional Question

The Professional exam uses three question formats:

Single-answer (most questions): One correct answer. Wrong answers are carefully constructed to sound correct. They typically:

• Use the right vocabulary while drawing the wrong conclusion
• Select the right architecture pattern for the wrong reason
• Use a correct mechanism at the wrong layer
• Have a control that is named but not owned or evidenced

Multi-select ("which TWO"): Two correct answers. Wrong answers are plausible additions. Do not stop at one that sounds right — find both.

Scenario-matching: Five scenarios, one answer each from a small set of options. The options often seem interchangeable. The distinction is almost always about:

• Ownership structure (who controls what)
• Reusability requirement (one app vs many)
• Step knowledge (known in advance vs emergent)

Domain 1 — Solution Design & Architecture

Sample Scenario

A logistics company wants Claude to process inbound freight quotes. Every request follows the same steps: extract shipment details from an email, validate against a rate card, and generate a quote document. Requirements are stable and the steps never vary.

What the scenario signals: stable, repeating, known steps → fixed workflow.

The distractors:

• "Multi-agent with a supervisor" → trap 11 (complexity justified by no valid driver)
• "Autonomous agent with tool access to plan its own approach" → wrong because the steps are fully known; autonomy adds overhead for no gain
• "Single monolithic prompt with all instructions and the full rate card" → sacrifices per-step control and validation

The answer: Fixed workflow — gives predictability, per-step auditability, and easy debugging.

Domain 1 Decision Framework

1. Does the task have exactly one step? → Single augmented call
2. Are all steps known, stable, and identical per request? → Fixed workflow
3. Does each step's output determine the next step? → Autonomous agent
4. Do sub-tasks need different tools/context AND can they run independently? → Multi-agent

If the question mentions "audit trail" or "exception halts further processing" → the supervisor/orchestrator pattern within multi-agent (not peer-to-peer handoffs, which cannot guarantee ordering or halting).

Domain 2 — Claude Models, Prompting & Context Engineering

Sample Scenario

A system prompt contains a critical rule ("never quote internal pricing") buried in the middle of 12,000 tokens of product context. The rule is followed inconsistently. The team has already reworded it twice without improvement.

What the scenario signals: positional attention problem, not a wording problem. Two rewrites that didn't help rule out the wording hypothesis.

The distractors:

• "Repeat the rule verbatim after every paragraph" → bloats the prompt, dilutes attention further
• "Lower temperature to zero" → affects sampling randomness, not rule salience
• "Switch rule to uppercase" → superstition, not a supported mechanism

The answer: Move the critical rule to the beginning or end of the prompt, structurally separated from reference content.

Domain 2 Decision Framework

• Critical rule not being followed → positional attention problem → move to start or end
• Format compliance inconsistent despite instructions → use few-shot exemplars
• CoT applied uniformly → remove from simple extraction tasks, keep on multi-step reasoning
• Cache hit rate near zero despite stable prompt → dynamic values at position zero; fix by moving them after static content
• Mid-collection recall degradation + high cost → retrieve relevant sections, not full corpus

Domain 3 — Integration

Sample Scenario

A Claude-based assistant queries an HR system on behalf of employees. It authenticates using a single service account with organisation-wide read access. The system prompt instructs the model to only return data belonging to the requesting employee.

What the scenario signals: trap 12 — an instruction doing a security layer's job. The model has access to all employee data and is told not to return it. That is not authorization.

The distractors:

• "Service accounts cannot be used with AI systems under compliance frameworks" → not a real rule
• "Add write access for the audit log" → expands the blast radius further
• "Authenticate directly using each employee's password" → a credential anti-pattern

The answer: The core problem is authorization enforced by prompt instruction rather than by the access-control layer. The fix is per-user scoped credentials or pass-through authentication so the model can only retrieve what it is authorized to retrieve.

Domain 3 Decision Framework

• Many AI apps need the same system integration → MCP server
• One deterministic pipeline calls the model once → direct API integration
• Two orgs' agents must coordinate without exposing internal systems → agent-to-agent protocol
• Tool-selection accuracy declining as tools were added → audit/remove unused tools + progressive discovery
• Authorization via system prompt → wrong, always; must be structural access control
• RAG returns clause fragments without context → structure-aware chunking with cross-reference metadata

Domain 4 — Evaluation, Testing & Optimization

Sample Scenario

A marketing-content assistant must be evaluated for tone and brand alignment across thousands of outputs per week. Human review of every output is infeasible.

What the scenario signals: subjective quality at scale → LLM-as-judge methodology.

The distractors:

• "Exact string matching against a library of approved copy" → can't score novel copy
• "Skip tone evaluation, since it is subjective" → abandons a measurable requirement
• "Full human review, accepting the throughput cost" → infeasible as stated

The answer: LLM-as-judge rubric scored against brand guidelines, periodically calibrated against human expert ratings. The calibration step is what makes the judge trustworthy.

Domain 4 Decision Framework

• Output checkable mechanically → code-based eval
• Output requires interpretation → model-based (LLM judge): different model, constrained verdicts, calibrated
• "Good enough to launch?" → task-specific metrics with agreed thresholds, not anecdotes or general benchmarks
• Golden dataset → real queries + constructed edge cases (not synthetic only, not prompt examples)
• Offline eval improved by 6 points → run A/B test on fraction of real traffic before full rollout
• Leading indicator of quality degradation → retrieval relevance scores and "no grounded answer" rates, not support complaints

Domain 5 — Governance, Safety & Risk

Sample Scenario

A document-triage system routes 100% of flagged documents to a human reviewer. The team considers this the cautious approach. The reviewer approves nearly everything after a brief glance.

What the scenario signals: trap 10 — everything reviewed = nothing reviewed. 100% volume with no triage has produced consent fatigue.

The distractors:

• "The review gate is correctly designed; increase the reviewer's throughput" → wrong diagnosis
• "Remove the human review gate since it isn't working" → right observation, wrong fix
• "The system needs a higher confidence threshold before flagging" → partially right framing but misses the routing rule

The answer: Route to human review when low-confidence AND (irreversible OR high-cost). Route by stakes, not by volume. Redesign the gate to let high-confidence, reversible items pass without review.

Domain 5 Decision Framework

• Control named in design doc but no owner and no evidence → named-but-unowned = unenforced (trap 7)
• System relies on system-prompt instruction for data access control → trap 12, always wrong
• 100% routed to human review → trap 10, consent fatigue; fix with confidence + stakes routing rule
• Guardrail that errors silently → fail-open; dangerous for high-stakes data; must decide per-guardrail
• Regulatory obligation with no evidence artifact → a claim, not a control; add owner and evidence
• Indirect prompt injection → screening the user message is insufficient; screen retrieved content too

Domain 6 — Stakeholder Communication & Lifecycle

Sample Scenario

A CFO asks for a 25% cost reduction in the AI platform's monthly spend within 90 days. An architect proposes switching all workloads to the smallest model immediately. The CFO approves.

What the scenario signals: cost optimization without measurement first (trap from M2) + missing tradeoff framing (missing reversal cost from M4).

The correct architect response: Analyse token usage by workload first. Identify dominant cost drivers. Apply targeted levers with quality evaluation at each step. Present to the CFO as: gain (cost reduction path), give-up (engineering time for analysis + risk of quality degradation if downsizing is applied blindly), reversal cost (re-architecture time if a bulk downsize breaks a production workflow and must be reversed).

Domain 6 Decision Framework

• Stakeholder says "seamless" → ask what would break the experience → convert to p95 latency SLA
• Moving to design before all four discovery categories covered → incorrect; missing "must prove" creates compliance gaps
• Tradeoff presented without reversal cost → incomplete; add reversal cost
• Outcome document missing the before-metric → unrecoverable after launch; the before-metric must be captured first
• Governance table doesn't exist before launch → design it in; post-incident governance is incident management

Domain 7 — Developer Productivity & Ops

Sample Scenario

A team of 200 engineers is rolling out Claude. The platform team decides to enable access for all 200 on day one to build momentum quickly.

What the scenario signals: direct batch rollout without champion pattern → produces stalled adoption at basic chat.

The correct architect recommendation: enable one champion per team first; capture their workflow and solved configuration; seed adoption in batches from that shared baseline.

Domain 7 Decision Framework

• Skills distribution: org-provisioned (reach, no rollback) / plugin (versioned) / project Skill (repo-scoped) / API Skill (pinned)
• "We need every team to have this" → org-provisioned (but note: no native rollback)
• "We need to roll back if this breaks the finance workflow" → plugin (versioned, rollback)
• Spend posture missing → configure model defaults, allowlists, caps before the first bill
• AI-generated code reviewed by the same session that wrote it → session anchoring trap; use fresh session or human
• Human understanding dimension in verification checklist → never automated; always requires a human to verify they actually understand

Scenario-Matching Practice

The exam includes questions where you match five scenarios to four options (one option used twice, or one option not used). The distinguishing logic:

Architecture pattern matching:

• One transformation + supplied context → single augmented call
• Same steps every time → fixed workflow
• Emergent path → autonomous agent
• Different specialisations + parallel → multi-agent

Integration mechanism matching:

• Standardised reuse across many apps → MCP server
• Deterministic owned pipeline → direct API integration
• Cross-org coordination without internal exposure → agent-to-agent protocol

Skills distribution matching:

• Entire org, stable capability → org-provisioned
• Group-targeted, needs rollback → plugin
• Repo-scoped procedure → project Skill
• Programmatic, version-pinned → API Skill

Final Pre-Exam Checks

Before sitting the exam, confirm you can answer each of these without looking anything up:

1. What are the three feasibility verdicts?
2. Which layer of the safety stack catches indirect prompt injection?
3. What is the HITL routing rule, stated exactly?
4. What three fields does the compliance control register require?
5. What is the reversal cost of the architecture you're recommending in a tradeoff?
6. Which reliability control belongs at the service boundary (not the API layer)?
7. What is the one dimension of the verification checklist that can never be automated?
8. Which distribution mechanism supports native version rollback?
9. What is the difference between shadow testing and canary rollout?
10. Why is an uncalibrated LLM judge worse than no automated grade?

If any of these take more than 10 seconds, revisit the corresponding module lesson before proceeding to the exam.