Free Anthropic CCAR-P Practice Exam with Questions & Answers

Authentication and authorization run before retrieval so retrieval can filter by identity — Yes

An asynchronous queue absorbs bursty traffic between intake and the model-invocation layer — Yes

Tax computation is encoded directly in the system prompt rather than in code — No

Conversation logs include unredacted government identifiers to maximize tuning signal — No

Authentication and authorization must precede retrieval so unauthorized information never enters model context. An asynchronous queue is appropriate for burst absorption, backpressure, controlled concurrency, and retry management at scale. Deterministic tax computation should be implemented in validated code or a controlled calculation tool, not delegated to probabilistic prompt interpretation. Including unredacted government identifiers in conversation logs violates data-minimization principles and creates unnecessary privacy, security, and regulatory exposure. Sensitive fields should be removed, tokenized, or redacted before logging or model use unless specifically required and authorized. The resulting design separates deterministic computation, access control, scalable orchestration, and language reasoning into appropriate architectural layers.

A structured feedback session must enable informed review, traceable decisions, and accountable follow-through. Option B allows stakeholders to examine architecture diagrams, evaluation results, risk registers, or prototypes before the meeting. The focused agenda keeps discussion aligned with decisions that must be made rather than allowing the session to become an unstructured status conversation. Written decision records prevent different participants from leaving with incompatible interpretations. Assigning an owner and due date to every action converts feedback into executable delivery work. Options A, C, and D depend heavily on memory, provide inadequate preparation time, and create weak decision traceability. In-flight deployments particularly require documented feedback because accepted changes may affect scope, cost, risk, testing, and release timing.

Study Guide references/topics: Stakeholder feedback; decision records; action ownership; lifecycle alignment; communication planning.

Transaction value is only one component of risk. Even a small refund can create fraud exposure, discriminatory outcomes, contractual violations, account compromise, policy inconsistency, or cumulative financial loss. Delegation should therefore depend on the nature, detectability, reversibility, and aggregate impact of failure—not a monetary threshold alone. Option B supports risk-stratified automation: routine, well-evidenced cases may proceed automatically, while anomalous, identity-sensitive, disputed, or policy-edge cases require review or escalation. Anthropic’s trustworthy-agent principles emphasize maintaining meaningful human control rather than imposing universal review or unrestricted autonomy. Option A is unnecessarily absolute; Option C prioritizes efficiency over governance; and Option D prohibits potentially safe, controlled automation without risk analysis.

Study Guide references/topics: Delegation competency; human control; risk classification; reversibility; cumulative exposure; escalation design.

Option D defines a measurable performance characteristic rather than a business capability. The requirement specifies a percentile-based latency threshold—under 800 milliseconds at the 95th percentile—and ties that threshold to the expected workload volume. It therefore describes how well the system must operate under defined conditions.

Options A, B, and C are functional requirements. They describe actions the solution must perform: extracting fields and creating a downstream record, producing an editable draft, and assigning tickets to categories. Functional requirements define observable system behaviors or outputs.

A strong non-functional requirement must include a metric, threshold, measurement method, and operating condition. For Option D, the architect should further define whether latency is measured end-to-end or only at the model API, the test duration, concurrency, input-size distribution, cache state, timeout behavior, geographic region, and treatment of failed requests. Without those details, teams may report incompatible results while claiming to satisfy the same requirement.

Non-functional requirements for Claude solutions commonly address latency, availability, throughput, cost, privacy, security, recoverability, observability, scalability, and regulatory compliance.

Study Guide references/topics: Structured discovery; functional requirements; non-functional requirements; p95 latency; workload conditions; measurable acceptance criteria.

===============

Managed configuration is designed for organization-wide security and compliance controls that individual users and repositories must not override. Applying the hardening flag centrally establishes an administrative policy boundary and prevents engineers from disabling the control through user, project, or local settings.

Anthropic identifies managed scope as the appropriate location for security policies, non-overridable compliance requirements, and standardized configurations deployed by IT or DevOps. Managed values ordinarily take precedence over command-line arguments and every user-controlled settings scope. Claude Code Settings

Option A depends on every engineer maintaining the setting and allows the user to edit or delete it. Option B affects only pipeline execution and does not protect local, interactive, IDE, or other execution paths. Option D standardizes the setting within the repository but does not make it tamper-resistant; contributors with repository write access could alter the file, use higher-precedence local settings where permitted, or operate outside the repository configuration.

The implementation should also verify active policy delivery, monitor configuration-change events, and test that startup or execution fails safely if the managed setting is absent or invalid. Central definition without enforcement and verification would not fully satisfy the requirement.

Study Guide references/topics: Managed settings; non-overridable controls; enterprise hardening; policy enforcement; configuration governance; defense against local override.

===============

Strong decision communication must explain both how the decision was reached and how it will remain governed. Option D provides the reasoning structure: the alternatives considered and the criteria used to compare them. This allows reviewers to verify that capability, cost, latency, security, maintainability, integration complexity, and business outcomes were evaluated consistently rather than selectively.

Option A establishes lifecycle accountability. Architectural decisions depend on assumptions about requirements, models, data, regulations, usage volume, and operational constraints. Naming an owner and review cadence ensures the decision is reconsidered when those assumptions materially change.

Option B imposes a meeting mechanism rather than describing decision quality. Mandatory attendance by senior leaders may add delay without improving the clarity or correctness of the record. Option C removes important evidence and increases the chance that rejected approaches will be proposed again without addressing the original objections. Option E can improve consistency and discoverability, but visual standardization alone does not establish substantive architectural communication.

A strong decision record should therefore be evidence-based, audience-accessible, traceable to requirements, explicit about alternatives and consequences, assigned to an accountable owner, and governed by defined review triggers.

Study Guide references/topics: Architectural-decision communication; alternatives analysis; evaluation criteria; decision ownership; review cadence; changing assumptions; lifecycle governance.

Least privilege means granting the exact capabilities required for the defined responsibility—no more and no less. Option B permits the assistant to read the approved knowledge base and write only to the draft queue. Enforcing the allow-list at the orchestration layer creates a deterministic authorization boundary outside the model’s prompt and reasoning.

The rule should also be role-specific so different users or operational roles receive only the tools and data scopes they require. The knowledge-base tool should remain read-only, while the draft-queue tool should be restricted to draft creation rather than external sending, approval, or production publication.

Option A exposes every globally readable tool, creating unnecessary access to unrelated information. Option C relies on voluntary model behavior instead of enforceable authorization. A mistaken tool selection or successful prompt injection could therefore invoke an inappropriate capability. Option D minimizes permissions by eliminating the assistant’s required functionality and is not a viable least-privilege implementation.

Anthropic’s permission guidance distinguishes allow, ask, and deny rules and confirms that these controls are enforced by Claude Code rather than by model instructions. Configure Permissions

Study Guide references/topics: Least privilege; per-role authorization; orchestration-layer enforcement; capability scoping; read/write separation; tool allow-lists.

===============

The network-boundary requirement is a mandatory architectural constraint; therefore, Option D must be resolved before evaluating operational savings. Managed infrastructure can reduce responsibility for agent loops, sandboxes, session state, recovery, and orchestration, but those advantages are immaterial if financial data leaves the approved boundary. Anthropic describes Claude Managed Agents as a hosted agent harness and provides configurable data-residency controls. The architect must still validate inference location, session persistence, encryption, subprocessors, credential handling, auditability, and deletion behavior against the organization’s exact security policy. Options A, B, and C affect migration effort or economics, but they cannot override a non-negotiable data-governance requirement.

Study Guide references/topics: Architectural constraints; managed versus self-hosted agents; data boundaries; residency; security-gated decision-making.

Regression testing determines whether a change has broken behavior that previously satisfied the defined acceptance criteria. A stable reference set preserves known-good cases and enables the updated prompt to be compared with the prior baseline using consistent scoring rules. This makes Option B directly aligned with the described failure. Adversarial testing focuses on malicious or boundary inputs, such as prompt injection. Smoke testing confirms that the system remains broadly available, but it does not measure whether previously correct model behavior has degraded. Integration testing verifies contracts and interactions among components and may not detect output-quality regressions caused specifically by a prompt change. Anthropic recommends task-specific evaluations that mirror real workloads, include edge cases, and measure performance against explicit success criteria throughout prompt iteration. Anthropic: Define success criteria and build evaluations

================

A direct call to the existing stable HTTP endpoint is the simplest mechanism that satisfies the stated requirements. The interaction is stateless, requires only one tool invocation, and has a strict latency objective. Adding session management, message-bus translation, or another model-mediated agent would introduce unnecessary network hops, operational dependencies, failure modes, and processing latency.

The assistant’s tool implementation should validate input parameters, authenticate through a server-side credential mechanism, apply narrowly scoped authorization, set explicit timeouts, and validate the pricing response before returning it to Claude. Credentials must never be supplied by or exposed to the model. Appropriate logging should capture request attribution, endpoint outcome, and timing without unnecessarily recording sensitive data.

Anthropic describes the Claude API as a RESTful interface and supports custom tools in which the application executes the requested function or API operation. Claude API Overview, Tool Use Overview

MCP could be justified when standardized discovery or reuse across clients is required, but the scenario already supplies a stable API and does not establish that additional requirement.

Study Guide references/topics: Protocol selection; direct API integration; stateless calls; latency minimization; scoped credentials; avoiding unnecessary agentic complexity.

===============