# CCAR-P Practice: Foundations Extended | Your AI Department > 30 free CCAR-P practice questions on the five CCAR-F foundations domains (extended to harder scenarios), with study notes and explanations. Source: https://youraidept.com/network/ccar-p-practice-questions/the-five-ccar-f-foundations-domains CCAR-P domain drill # CCAR-P practice questions: The five CCAR-F foundations domains. 30 original questions on The five CCAR-F foundations domains, which the Claude Certified Architect – Professional exam extends to harder scenarios. No official weights are published yet. Study notes first, then the questions with an explanation for each. When this domain feels solid, take [the timed CCAR-P practice exam](https://youraidept.com/network/ccar-p-practice-exam) and check the domain breakdown. ## What to know about The five CCAR-F foundations domains - Everything on the CCAR-F blueprint, extended to harder scenarios: multi-agent systems with real failure modes, tool ecosystems rather than single tools, and context strategies for long-running work. See the CCAR-F page for the Foundations material. ## The five CCAR-F foundations domains questions All domains (30) The five CCAR-F foundations domains (30) The five CCAR-F foundations domains A multi-agent system fails in cascades when one worker misbehaves. What architectural controls apply? - Redundant workers per task with majority voting, so a single misbehaving worker is outvoted by the others - Timeouts, bounded retries, circuit breakers per worker, and a coordinator that can stop the run - Stricter output schemas and a shorter system prompt per worker, reducing the chance that any worker misbehaves - A larger context window for the coordinator so it can hold every worker transcript and spot problems itself Check answer ### Show answer Answer: Timeouts, bounded retries, circuit breakers per worker, and a coordinator that can stop the run. Limits and a coordinator that can halt the run are what contain cascading failures. Voting adds workers and cost without a way to stop a run that is going wrong, and prevention through stricter schemas does not contain the failures that still occur. The five CCAR-F foundations domains Prompts are edited by several engineers and quality varies release to release. What fixes the process? - Freeze the prompts for a quarter at a time and batch all edits into a single tested release - Move the variable parts of each prompt into a config service so engineers edit settings rather than prose - Version prompts in source control with review, and run the eval set in CI on every change - Assign one owner per prompt who approves edits by hand and keeps a changelog in the team wiki Check answer ### Show answer Answer: Version prompts in source control with review, and run the eval set in CI on every change. Treat prompts like code: versioned, reviewed, and tested automatically. A single human approver adds a bottleneck without measurement, and a config service changes where edits happen without testing their effect. The five CCAR-F foundations domains A multi-agent system has a planner, three workers, and a verifier. The verifier rejects 40% of worker output. Where do you look first? - The planner's decomposition, adding a second verifier pass so borderline output gets a tie-breaking judgment - The verifier's rubric, since a strict rubric applied to reasonable output is the more common cause of a high rejection rate in practice - The worker model tier, since a stronger model usually cuts the rejection rate faster than editing prompts does - The worker briefs and acceptance criteria, since a high rejection rate usually means the task or the criteria are unclear Check answer ### Show answer Answer: The worker briefs and acceptance criteria, since a high rejection rate usually means the task or the criteria are unclear. High rejection usually points to unclear briefs or criteria, so read those before changing anything else. Loosening the verifier or adding a tie-breaker treats the symptom, and a bigger worker model spends money before the task is even well defined. The five CCAR-F foundations domains Your agent's tool surface has grown to 60 tools and accuracy is falling. Best structural fix? - Group tools behind fewer well-scoped interfaces or route to specialist subagents with small tool sets - Rewrite all 60 descriptions with examples and add a tool-selection few-shot block to the system prompt - Move to a larger model tier, since tool selection accuracy scales with capability at this tool count - Order the tool list by call frequency so the most used tools appear first in the model's context Check answer ### Show answer Answer: Group tools behind fewer well-scoped interfaces or route to specialist subagents with small tool sets. Large flat tool sets degrade selection, so partition them. Better descriptions help at the margin but leave 60 candidates competing for every call, and ordering by frequency does nothing for the rare tools that cause most of the errors. The five CCAR-F foundations domains A long-running agent must survive process restarts. What is required? - Idempotent tools, so that re-running the task from the first turn after a restart produces the same result - A larger context window so the full history fits when the process reloads the last transcript - Durable state: the plan, progress, and pending tool results persisted outside the process - A supervisor that restarts the process and re-sends the original request with a note to continue where it left off Check answer ### Show answer Answer: Durable state: the plan, progress, and pending tool results persisted outside the process. Durability lives outside the process: persist the plan, progress, and pending results so a restart resumes rather than restarts. Idempotent tools make a full re-run safe but still throw away all the work, and a note to continue gives the new process nothing to continue from. The five CCAR-F foundations domains Which design keeps a coordinator's context from growing without bound over hundreds of subtasks? - Each subagent appends its full transcript so the coordinator can re-check work without re-running it - Subagents return compact results; the coordinator keeps a structured ledger and compacts periodically - Restart the coordinator on a fixed cadence and hand the new instance the last user message plus the task list - Stream subagent output straight to the user and keep the coordinator stateless between subtasks Check answer ### Show answer Answer: Subagents return compact results; the coordinator keeps a structured ledger and compacts periodically. Compact returns plus a ledger keep the coordinator's context proportional to the work that matters. Scheduled restarts lose progress each time, and a stateless coordinator cannot sequence or reconcile hundreds of dependent subtasks. The five CCAR-F foundations domains You need a prompt that behaves the same across model upgrades. What practice helps most? - Few-shot examples drawn from the previous model's best outputs so the new model imitates them - A detailed persona and tone section, since style stability is what users notice most across model versions - Explicit, testable instructions plus a golden eval set that runs on every model change - Pin the model version in config and treat any upgrade as a new project with a freshly written prompt Check answer ### Show answer Answer: Explicit, testable instructions plus a golden eval set that runs on every model change. Specificity plus evals: explicit instructions transfer, and the eval set tells you where they did not. Examples from the old model anchor the new one to old behavior including its mistakes, and rewriting from scratch per upgrade guarantees drift. The five CCAR-F foundations domains An MCP server exposes tools with side effects to several agents. What must the server enforce itself? - Authorization per caller and idempotency on side-effecting operations - Structured logging of every call with the caller's agent id so misuse can be traced after the fact - Rate limits per client and a global concurrency cap so no single agent can exhaust the backend - Tool descriptions that state the side effects clearly so calling agents gate them appropriately Check answer ### Show answer Answer: Authorization per caller and idempotency on side-effecting operations. Servers cannot rely on callers: authorization and idempotency have to be enforced where the side effect happens. Logging and rate limits are worth having but detect or throttle misuse rather than prevent it, and descriptions only work if every caller honors them. The five CCAR-F foundations domains Which caching strategy suits a system with a large stable policy document and per-user variable content? - Skip caching, since per-user variation means most requests miss and the cache write premium is wasted - Cache the policy document as the prefix and put per-user content after the breakpoint - Cache each user's full conversation as its own prefix so that repeat visits hit the cache - Place the user content first so the model sees it before the policy, then cache the policy block Check answer ### Show answer Answer: Cache the policy document as the prefix and put per-user content after the breakpoint. Stable prefix cached, variable suffix uncached. Putting user content first breaks the shared prefix for every request, and per-user caches only pay off for the rare user who returns within the cache lifetime. The five CCAR-F foundations domains An agent's plan step produces good plans but execution drifts. What is the architectural response? - Increase the planning depth so the plan covers more of the edge cases the executor might wander into during long runs - Add a retry at the end of execution that reruns the task when the output fails validation - Drop the separate planning step and let a single stronger model plan and execute in one pass - Make the plan an explicit artifact that execution checks against, with a review step when it deviates Check answer ### Show answer Answer: Make the plan an explicit artifact that execution checks against, with a review step when it deviates. Plans are useful when execution is held to them, so make the plan a checked artifact. A deeper plan does not stop the executor from ignoring it, and an end-of-run retry discovers the drift only after all the cost has been spent. The five CCAR-F foundations domains Which of these is the right place for a hard spending limit on an agent? - The system prompt, as a stated token budget the model tracks and stops at on its own - The runtime: a budget enforced by the loop or a hook that stops the run - The tool descriptions, with a cost annotation so the model prefers cheaper calls - The billing dashboard, with an alert to the on-call engineer at 80% of budget Check answer ### Show answer Answer: The runtime: a budget enforced by the loop or a hook that stops the run. Enforce budgets in code, where they cannot be forgotten or reasoned around. A budget in the prompt is a request the model may not honor, and a dashboard alert arrives after the money is spent. The five CCAR-F foundations domains A system uses one prompt for five customer segments and quality varies by segment. Approach? - Add few-shot examples from the weakest segment until its scores match the others - Raise the model tier for all segments, since the variance reflects a capability limit - Expand the single prompt with a section per segment so the model applies the right guidance - Route by segment to specialized prompts and evaluate each route separately Check answer ### Show answer Answer: Route by segment to specialized prompts and evaluate each route separately. Routing plus per-route evals lets each segment be tuned and measured on its own. Segment sections in one prompt still compete for attention and are evaluated as a blend, and examples from the weakest segment can shift quality on the others. The five CCAR-F foundations domains What is the main risk of letting subagents spawn their own subagents without limits? - Duplicate work, since sibling subagents cannot see each other's results without a shared store - Loss of detail, since each level summarizes upward and precision degrades with depth - Rate limit exhaustion at the API layer, which the provider handles with backoff in any case - Unbounded cost and untraceable behavior, so depth and fan-out need caps Check answer ### Show answer Answer: Unbounded cost and untraceable behavior, so depth and fan-out need caps. Cap recursion and fan-out: without limits, cost and behavior both become impossible to bound or trace. Duplicate work and summarization loss are real but bounded problems; unbounded spawning has no ceiling at all. The five CCAR-F foundations domains A retrieval-augmented agent returns confident answers from stale documents. Fix? - A system prompt instruction telling the model to hedge when it is unsure whether a source is current - A larger retrieval window so that newer documents are more likely to appear among the results - Freshness metadata in the index, ranking or filtering by recency, and surfacing dates in the answer - A reranker tuned on relevance so the most on-topic chunk wins regardless of its age Check answer ### Show answer Answer: Freshness metadata in the index, ranking or filtering by recency, and surfacing dates in the answer. Freshness is a retrieval property: index the dates, rank on them, and show them. A relevance reranker will happily pick an on-topic but outdated chunk, and a hedging instruction cannot tell the model which sources are stale. The five CCAR-F foundations domains Which is the strongest reason to keep tool results out of the system prompt? - System prompts have a hard length cap that tool results would exceed on most real tasks - Tool results in the system prompt are billed at a higher rate than the same tokens in messages - The system prompt is for instructions; data belongs in turns so it stays data and can be managed - Prompt caching only applies to the system prompt, so adding data there invalidates the cache Check answer ### Show answer Answer: The system prompt is for instructions; data belongs in turns so it stays data and can be managed. Instructions and data stay separate: data in turns can be truncated, compacted, and treated as untrusted. Caching applies to any stable prefix, not only the system prompt, and there is no separate billing rate or special cap for system prompt tokens. The five CCAR-F foundations domains When should an architect prefer a deterministic function over a model call inside a workflow? - Only for arithmetic and date math, where model error rates are known to be high - When the step is on the critical path, keeping model calls for background work - Only when latency matters, since a model call on a small tier is comparable in cost - Whenever the logic is fully specifiable; code is cheaper, faster, and testable Check answer ### Show answer Answer: Whenever the logic is fully specifiable; code is cheaper, faster, and testable. Do not spend a model on what code can do: anything fully specifiable belongs in code regardless of where it sits in the workflow. Restricting this to math or to the critical path leaves plenty of deterministic logic paying model cost and variance. The five CCAR-F foundations domains A Claude Code deployment across a 200-engineer org needs consistent behavior. Which lever scales? - Each engineer's user-level settings and memory, tuned during an onboarding workshop - An internal wiki with recommended prompts and a Slack channel for questions - A central prompt-engineering team that reviews a sample of each engineer's session transcripts weekly - Checked-in CLAUDE.md, shared skills, hooks, and project-scoped MCP config Check answer ### Show answer Answer: Checked-in CLAUDE.md, shared skills, hooks, and project-scoped MCP config. Configuration in the repo scales: every engineer gets the same instructions, skills, hooks, and MCP servers on checkout. Wikis and workshops depend on people remembering, and per-user settings drift the moment someone edits theirs. The five CCAR-F foundations domains What should an evaluator in an evaluator-optimizer loop output? - A single numeric score, keeping the evaluator cheap and its output easy to threshold - A pass or fail flag plus the full reasoning trace so that humans can audit the decision later without re-running it - A corrected version of the output, so the loop converges in one step instead of several - A structured verdict against explicit criteria with specific feedback the generator can act on Check answer ### Show answer Answer: A structured verdict against explicit criteria with specific feedback the generator can act on. Actionable, structured feedback is what lets the generator improve. A bare score gives it nothing to act on, and a rewritten output turns the evaluator into a second generator with no independent check. The five CCAR-F foundations domains Which context strategy suits an agent that reads thousands of tickets a day? - One agent per ticket with no shared state, relying on the ticket system for any aggregation - Embed all tickets and let the agent retrieve as needed with no persistent working state - Load the full day's tickets into one long-context call so the model can spot cross-ticket patterns - Process in batches with per-batch summaries and a rolling state document Check answer ### Show answer Answer: Process in batches with per-batch summaries and a rolling state document. Batch, summarize, roll state: bounded context per call plus a carried summary. One giant call is expensive and degrades with length, and per-ticket agents with no shared state cannot see patterns across tickets at all. The five CCAR-F foundations domains A team asks whether to expose a tool as one call with many options or several narrow tools. Guidance? - Prefer one tool with an options schema, since fewer tools keep the definitions block small - Mirror the backend REST endpoints one to one so that the mapping between each tool and its API call is obvious to maintainers - Prefer many tools in every case, since each extra tool adds a few tokens and selection is cheap - Prefer narrow, clearly described tools; combine only when the model routinely needs them together Check answer ### Show answer Answer: Prefer narrow, clearly described tools; combine only when the model routinely needs them together. Narrow tools select better and are easier to describe and test. One tool with many modes hides the choice inside a schema the model fills in less reliably, and mirroring REST endpoints optimizes for the API's shape rather than the model's. The five CCAR-F foundations domains A production agent's accuracy dropped after a tool provider changed its response format. What should have caught it? - A larger eval set run before each of your own releases, covering more of the tool paths - Contract tests on tool responses plus a scheduled eval that alerts on drift - Pinning the provider's API version in the client so that format changes could not reach production - Schema validation in the tool wrapper that coerces unexpected fields to safe defaults Check answer ### Show answer Answer: Contract tests on tool responses plus a scheduled eval that alerts on drift. External dependencies change on their own schedule; contract tests and scheduled evals detect it before users do. Pre-release evals only run when you ship, so a change on the provider's side slips through, and silent coercion hides the break instead of surfacing it. The five CCAR-F foundations domains Which design keeps a multi-tenant agent from leaking one customer's context into another's? - Per-tenant context, memory, and tool credentials, with tenant id enforced in every tool call - A system prompt instruction that names the current tenant and forbids any reference to others - Random session ids per conversation so that contexts cannot be guessed across tenants - A shared memory store partitioned by a tenant tag that the model includes in each query Check answer ### Show answer Answer: Per-tenant context, memory, and tool credentials, with tenant id enforced in every tool call. Isolation is enforced in the runtime and tools, not requested in prose. A tenant tag the model supplies is the strongest distractor, but it relies on the model getting the tag right on every query, which is exactly the failure you are guarding against. The five CCAR-F foundations domains A coordinator agent spends most of its tokens re-reading subagent transcripts. Fix? - A larger context window on the coordinator so that full transcripts fit without truncation or summarization - Prompt caching on each transcript so that repeated reads cost less per token - Subagents return structured summaries; the coordinator keeps a ledger instead of transcripts - Fewer, larger subagents so there are fewer transcripts for the coordinator to read Check answer ### Show answer Answer: Subagents return structured summaries; the coordinator keeps a ledger instead of transcripts. Structured returns keep the coordinator lean and its decisions grounded in what matters. Caching lowers the price of re-reading but not the context consumed, and fewer subagents just concentrates the same transcript volume. The five CCAR-F foundations domains How should an architect decide between extended thinking and a second verification call? - By task type: extended thinking for reasoning-heavy tasks, verification for extraction and formatting work - Extended thinking by default, since it keeps the workflow to one call and one bill - A verification call by default, since a second independent pass catches more errors - By measuring: run both on the eval set and compare accuracy, cost, and latency for that task Check answer ### Show answer Answer: By measuring: run both on the eval set and compare accuracy, cost, and latency for that task. Empirical comparison on the task decides; both techniques have cases where they win. Rules of thumb by task type are a starting hypothesis, not a decision, and either default leaves the cheaper or more accurate option untested. The five CCAR-F foundations domains Which practice makes a prompt library maintainable across a large team? - A single senior owner for the whole library who personally approves every change - Ownership per prompt, versioning, an eval per prompt, and a change process - A shared document where any engineer can edit, with a weekly review of the accumulated diff - A freeze on all prompts outside a monthly release window, with edits batched Check answer ### Show answer Answer: Ownership per prompt, versioning, an eval per prompt, and a change process. Prompts are production assets; manage them as such, with an owner, history, and a test per prompt. A single approver becomes the bottleneck and still has no eval to check against, and a freeze slows change without making it safer. The five CCAR-F foundations domains What is the failure mode of caching aggressively in an agent with per-user personalization? - Cached prefixes go stale, so users keep seeing policy text from before the last deployment - The cache lifetime expires in the middle of a conversation, forcing a full uncached re-read of the prefix on the very next turn - Personal content placed before the breakpoint defeats the cache, or a shared prefix built wrong leaks user data - Cache writes cost more than reads, so low-repeat traffic pays a premium for no benefit Check answer ### Show answer Answer: Personal content placed before the breakpoint defeats the cache, or a shared prefix built wrong leaks user data. Cache boundaries must respect data ownership and stability: personal data before the breakpoint kills the hit rate, and a shared prefix assembled from user data can leak across users. A cache expiring mid-conversation costs money but exposes nothing. The five CCAR-F foundations domains A client wants the agent to remember everything forever. Your design answer? - Append every transcript to a vector index and retrieve the top matches into each prompt - Store durable facts and decisions in a memory store with retention rules; load what a task needs - Decline memory entirely and require the user to restate context at the start of each session - Keep full transcripts in the prompt and rely on a long-context model tier to hold them Check answer ### Show answer Answer: Store durable facts and decisions in a memory store with retention rules; load what a task needs. Selective, governed memory beats hoarding: facts and decisions with retention rules, loaded on demand. Indexing every transcript keeps everything forever with no governance, which is the request restated rather than a design. The five CCAR-F foundations domains An MCP server is shared by several teams' agents and performance degrades under load. First architectural move? - Add a second server instance and split the teams by name across the two - Rate limits and per-client quotas on the server, plus caching of read-heavy resources - Reduce the number of tools exposed so each call carries less schema overhead - Switch transports from HTTP to stdio so that each agent gets its own dedicated server process Check answer ### Show answer Answer: Rate limits and per-client quotas on the server, plus caching of read-heavy resources. Shared services need quotas and caching so one heavy client cannot degrade the rest. A second instance doubles capacity once without addressing the fairness problem, and a per-agent stdio process shifts the load onto whatever backend the server calls. The five CCAR-F foundations domains Which signal tells you a workflow step should become a model call rather than code? - It is slow in code, and a small model tier would return an answer faster - It runs on every request, so a model call's consistency matters more than its cost - Its inputs are unstructured or ambiguous and rules keep failing on edge cases - It is the step users complain about most, regardless of what the complaints say Check answer ### Show answer Answer: Its inputs are unstructured or ambiguous and rules keep failing on edge cases. Ambiguity is where models earn their cost. A step that runs on every request is the last place to add model latency and variance, and user complaints about a deterministic step usually mean a bug rather than a need for judgment. The five CCAR-F foundations domains How do you keep a 60-tool integration surface manageable for an agent? - Merge related tools into a few generic ones with a mode parameter that selects the behavior - Sort the tool list by usage and drop the bottom 40, since rarely used tools add noise - Give the model all 60 with a routing table in the system prompt that maps each user intent to the right tool - Partition tools by task into specialist subagents or dynamic tool loading, each with a small relevant set Check answer ### Show answer Answer: Partition tools by task into specialist subagents or dynamic tool loading, each with a small relevant set. Small, relevant tool sets per context is what keeps selection accurate. Generic tools with a mode parameter move the choice into an argument the model fills in less reliably, and dropping the rare tools removes capability rather than managing it. ## Next - [Timed CCAR-P practice exam](https://youraidept.com/network/ccar-p-practice-exam): 63 questions in 120 minutes, scored against the 720 pass mark. - [All CCAR-P practice questions](https://youraidept.com/network/ccar-p-practice-questions) with a domain filter. - [The CCAR-P exam page](https://youraidept.com/network/ccar-p): format, weights, registration, study path. ### Other CCAR-P domains - [Governance, safety, and risk management (17 questions)](https://youraidept.com/network/ccar-p-practice-questions/governance-safety-and-risk-management) - [Stakeholder communication and lifecycle management (16 questions)](https://youraidept.com/network/ccar-p-practice-questions/stakeholder-communication-and-lifecycle-management) ## FAQ ### How much of the CCAR-P exam is the five CCAR-F foundations domains? + No official weights are published for CCAR-P yet. Prep guides report the five CCAR-F foundations domains as the Foundations material, extended to harder scenarios. The reported structure is on the CCAR-P exam page. ### Are these real CCAR-P exam questions? + No. They are 30 original scenario questions written from the blueprint for this domain. Reproducing exam content would violate the candidate agreement. ### How should I study the five CCAR-F foundations domains for CCAR-P? + Read the domain notes on this page, answer the questions with explanations hidden, then take the timed CCAR-P practice exam and check the per-domain breakdown for this domain. ## Who wrote this ### [Matei Olaru](https://youraidept.com/about/matei-olaru) CEO, Your AI Department Former public-company CEO, corporate attorney, and World Bank consultant. Runs the YAID Network and the partner relationship with Anthropic. ### [Michael Greenberg](https://youraidept.com/about/michael-greenberg) AI Product Lead, Your AI Department AI at Google, Meta, and Samsung. Six patents. Leads member support and the certification prep guidance. Your AI Department is a registered Claude Partner Network company. Our members sit these exams under our partner standing. Facts on this page were last checked on October 15, 2026. ## Ready to sit CCAR-P for real? Registration needs a Claude Partner Network organization. [YAID Network](https://youraidept.com/network) members register under our partner standing. $149 per quarter, exam fee separate. [Apply to join YAID ↗](https://youraidept.com/network/apply?ref=drill)[How registration works →](https://youraidept.com/network/register-for-claude-certification)