Important Anthropic CCAR-P Exam Questions

Answer: C

Explanation:

Project-scoped configuration is the appropriate mechanism for repository-specific settings that must remain consistent across collaborators. Claude Code recognizes `.claude/settings.json` as the shared project settings file and `.mcp.json` as the project-level MCP server configuration. When these files are committed to source control, every engineer receives the same definitions through the repository's normal clone, pull, review, and versioning workflow.

Anthropic's scope hierarchy distinguishes project configuration from user and local configuration. Files under the user scope apply to one engineer and are not automatically shared. Operating-system environment variables are also workstation-specific and require separate configuration management. Managed settings can be distributed centrally, but they are primarily intended for organization-wide security policies and controls that must be enforced by administrators; they are less appropriate when the configuration belongs specifically to one repository.

Shared configuration should contain server definitions and environment-variable references, but not plaintext credentials. Each engineer can resolve required secrets through an approved local secret mechanism while retaining identical server names, transports, arguments, and non-sensitive settings. Project MCP servers also remain subject to workspace trust and approval controls before execution.

Study Guide references/topics: [Claude Code configuration scopes](https://docs.anthropic.com/en/docs/claude-code/settings); project-scoped MCP servers; `.mcp.json`; repository-based operational standardization; secure secret separation.

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Answer: C

Explanation:

Option C provides the minimum coherent package required to move from architectural intent to implementable and supportable delivery. The architecture overview establishes system boundaries and component relationships. Architecture Decision Records preserve the context and trade-offs behind key choices. Component contracts define interfaces, schemas, responsibilities, failure behavior, and integration assumptions.

The evaluation framework and reference set allow the implementation team to confirm that prompts, models, retrieval components, and tool integrations continue to meet the defined quality, safety, latency, and cost thresholds. Runbooks translate operational knowledge into repeatable procedures, while the on-call playbook defines detection, escalation, containment, and recovery actions. The known-limitations register prevents unsupported behavior from being mistaken for an implementation defect and helps stakeholders manage residual risk.

Options A and B depend excessively on slides or undocumented verbal knowledge. Option D provides implementation material without explaining the system-level decisions, operating model, acceptance criteria, or limitations. Source code cannot substitute for architectural and operational documentation.

A clean transition does not mean the architect can never be consulted. It means routine implementation and support do not depend on undocumented knowledge held by a single individual.

Study Guide references/topics: Design-to-implementation handoff; ADRs; component contracts; reference evaluations; runbooks; operational playbooks; limitations management.

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Answer: D, E

Explanation:

Per-user, scope-restricted OAuth tokens directly eliminate the shared-credential weakness. Each token represents a distinct user or delegated identity, carries only the permissions required for that user's role, and can be revoked independently. This supports least privilege, limits the blast radius of credential compromise, and enables authorization decisions to be tied to a specific principal instead of an undifferentiated shared API key.

Actor attribution in tool-call logs directly resolves the second finding. Each audit event should record the authenticated user, delegated service identity, session or request identifier, tool name, authorization decision, relevant resource, timestamp, and outcome. This establishes accountability and permits incident investigators to reconstruct who initiated a consequential action.

Moving credentials into a server-side secret store is sound secret-management practice, but it does not by itself correct the shared-identity problem: a centrally stored key can still represent every user as the same principal. Schema validation protects downstream processing, while retrieval-layer RBAC protects information access; neither addresses the two specific audit findings.

These controls should be reinforced by short-lived tokens, server-side authorization enforcement, sensitive-field redaction, and tamper-resistant audit-log retention.

Study Guide references/topics: [Claude Code security](https://docs.anthropic.com/en/docs/claude-code/security); [MCP authentication and OAuth](https://docs.anthropic.com/en/docs/claude-code/mcp); least privilege; identity propagation; auditable tool execution.

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Answer: B, E

Explanation:

The strongest investigation combines configuration comparison with controlled replay. Comparing the current system prompt and output-schema settings against the last known good version identifies maintenance-related configuration drift. Relevant differences include removed formatting instructions, altered schema requirements, incompatible parser expectations, changed stop sequences, or a disabled structured-output mechanism.

Replaying known premaintenance requests against the current configuration creates a controlled test set. Inspecting the raw response before parsing then establishes whether malformed JSON originates in model generation or is introduced by streaming assembly, transport handling, post-processing, or the downstream parser. If the raw response is valid but the pipeline rejects it, the fault lies after generation. If the raw response is malformed, the prompt, schema enforcement, stop reason, and request configuration become the primary investigation targets.

Clearing the prompt cache assumes cache corruption without evidence. Changing model tiers introduces another variable and weakens causal attribution. Raising `max_tokens` is appropriate only when raw responses show truncation and the returned stop reason indicates the token limit was reached; it is not the most direct initial test.

Study Guide references/topics: [Increasing output consistency](https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/increase-consistency); [agent evaluation methodology](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents); regression replay; last-known-good comparison; raw-output inspection.

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Answer: A, D

Explanation:

Structured discovery begins by eliciting constraints from the people who own the business outcome, operate the process, manage risk, and will use or support the deployed system. Stakeholder workshops in Option A identify measurable expectations concerning accuracy, latency, throughput, cost, privacy, compliance, escalation, and acceptable failure behavior before the solution is scoped.

Option D converts those expectations into production gates. Success criteria must be explicit enough to evaluate empirically, while failure thresholds must determine when the design requires remediation, human review, or rejection. Anthropic's evaluation guidance states that successful LLM applications begin with defined success criteria and evaluations that measure performance against them. It characterizes strong criteria as specific, measurable, achievable, and relevant. Define Success Criteria and Build Evaluations

Option B prematurely selects a model before the workload profile is known. Option C can supplement discovery, but reaction-driven prototyping cannot replace documented requirements and acceptance criteria. Option E postpones essential constraints until architectural commitments may already have been made.

The discovery output should create traceability from stakeholder objectives to requirements, evaluation metrics, architecture decisions, and deployment gates.

Study Guide references/topics: Structured discovery; stakeholder workshops; nonfunctional requirements; success criteria; failure thresholds; production readiness gates.

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