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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integration | 19% | - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools |
| Topic 2: Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Implement guardrails and safety controls - Address ethical AI considerations and bias mitigation |
| Topic 3: Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars |
| Topic 4: Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution |
| Topic 5: Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Implement iterative improvement pipelines - Optimize performance, prompting, and model selection |
| Topic 6: Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases |
| Topic 7: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs |
>> CCAR-P Test Sample Questions <<
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NEW QUESTION # 21
You are evaluating retrieval-strategy claims used by a peer team.
For each claim, select yes if the statement is generally accurate. Otherwise, select no.
Answer:
Explanation:
Explanation:
Yes, Yes, Yes, No, No
Dense vector retrieval represents semantic similarity, making it suitable for matching paraphrases and conceptually related language even when the query and source do not share identical words. Sparse lexical retrieval, including BM25-style matching, retains strong sensitivity to exact tokens and is therefore valuable for identifiers, product codes, technical names, and uncommon terminology.
Structured query retrieval is appropriate when the required operation depends on explicit fields, predicates, joins, counts, grouping, or aggregation over a relational schema. In that situation, generating or invoking a constrained database query is more precise than approximating the operation through semantic similarity.
Hybrid retrieval is not identical to dense retrieval. It combines semantic and lexical candidate sets, typically followed by rank fusion or reranking. Anthropic's contextual-retrieval guidance explains that semantic search captures meaning and paraphrases, while BM25 captures exact terminology; combining them improves coverage. Contextual Retrieval, Contextual Retrieval Cookbook Random sampling is not a relevance strategy. It provides no systematic relationship between the query and selected evidence, producing unstable coverage and preventable hallucination risk. Production Q & A requires deterministic or evaluated relevance mechanisms, access filters, suitable indexes, and measurable retrieval metrics such as recall at k.
Study Guide references/topics: Dense retrieval; sparse retrieval; structured queries; hybrid search; rank fusion; retrieval evaluation; production RAG design.
NEW QUESTION # 22
You are rolling out a standardized Claude Code configuration to an engineering team and must complete the planning steps before piloting the configuration.
Which two steps must be completed BEFORE piloting the configuration with a small group of engineers?
(Select two.)
Each correct answer presents part of the solution.
Answer: A,E
Explanation:
Before a pilot begins, the architect must understand the workflows being enabled, the associated security boundaries, and the correct ownership of each configuration decision. Option D establishes this foundation.
Organization-wide, non-overridable controls belong in managed configuration, whereas repository-specific MCP servers, shared permissions, and team subagents generally belong in project scope.
Option A then converts those decisions into an initial project baseline that the pilot group can use. The baseline should identify approved MCP endpoints, minimum permission rules, denied operations, subagent responsibilities, configuration ownership, and observable success criteria. Anthropic distinguishes managed, user, project, and local scopes according to who they affect and whether they are shared with the team. Claude Code Configuration Scopes Options B and C are broad-rollout activities and should not occur before pilot evidence is available. Option E is explicitly post-pilot: findings must first be collected before the configuration can be iterated and stabilized.
The correct lifecycle is discovery, boundary definition, baseline design, limited pilot, evidence review, baseline refinement, documentation and training, followed by controlled expansion with a support and rollback process.
Study Guide references/topics: Claude Code rollout planning; scope ownership; MCP baselines; permission design; pilot strategy; operational enablement.
NEW QUESTION # 23
A healthcare organization is evaluating two Claude-powered AI architectures for a clinical documentation assistant. Architecture X produces higher output quality scores but costs $0.18 per documentation session and averages 4.2 seconds per response. Architecture Y scores slightly lower on quality metrics but costs $0.09 per session and averages 2.1 seconds per response. The stated SLA requires responses under 3 seconds, and the annual volume is projected at 2 million documentation sessions.
Which evaluation approach correctly applies business value pillar analysis to this decision?
Answer: B
Explanation:
Architecture X fails a stated production gate: its 4.2-second average exceeds the required three-second response SLA. It should therefore not remain a qualifying architecture unless it is redesigned and retested before selection. A promise to optimize after deployment is not evidence of compliance.
Architecture Y meets the latency requirement at 2.1 seconds. Its annual inference cost is $180,000 at two million sessions, compared with $360,000 for Architecture X-a difference of $180,000 annually. However, cost alone does not establish suitability. The organization must determine whether Y's lower quality score causes material omissions, correction effort, clinician dissatisfaction, documentation risk, or reduced workflow productivity.
Option C correctly treats the SLA as a threshold and then analyzes the remaining architecture across cost, efficiency, quality, and productivity. Option A ignores clinical quality. Option B disregards an explicit SLA failure. Option D may eventually be viable, but it introduces routing complexity and leaves unanswered how complex cases are detected, whether X's latency becomes acceptable for them, and how both paths are evaluated.
The final choice should be gated by clinical-quality evaluation, human review requirements, safety metrics, operational latency, and total annual cost.
Study Guide references/topics: Business-value pillars; performance gates; solution cost; workflow efficiency; quality trade-offs; healthcare architecture evaluation.
NEW QUESTION # 24
You are identifying the highest-impact optimization for a deployment whose token cost is dominated by a long, repeated system prompt and a large retrieved context per request.
Which optimization most directly targets the dominant cost driver?
Answer: B
Explanation:
Option C addresses both components responsible for the excessive cost. Anthropic prompt caching allows stable, repeatedly submitted prompt material-such as system instructions, tool definitions, and reusable background information-to be placed in a consistent prefix. After that prefix is written to the cache, qualifying subsequent requests can reuse it at the lower cache-read cost instead of repeatedly processing the same content at the standard input-token rate.
Retrieval must be optimized separately. Supplying an entire document collection or excessively deep search results increases cost, consumes context capacity, and may reduce answer quality by surrounding the relevant evidence with distracting material. Retrieval should select the smallest set of authoritative passages that provides sufficient evidence for the current query. This typically requires relevance scoring, deduplication, metadata filtering, reranking, and explicit token-budget limits.
Increasing retrieval depth or expanding the repeated system prompt directly worsens the identified cost driver.
Moving every request to a heavier model changes the unit economics but does not correct inefficient context construction. The recommended optimization therefore combines prefix caching with query-specific context pruning, followed by evaluation to confirm that reduced context does not lower task accuracy.
Study Guide references/topics: [Prompt caching](https://docs.anthropic.com/en/docs/build-with-claude
/prompt-caching); [effective context engineering] (https://www.anthropic.com/engineering/effective-context- engineering-for-ai-agents); retrieval precision; token-cost analysis; cache-prefix stability.
NEW QUESTION # 25
You are configuring tool permissions for a Claude-based assistant. The assistant's defined responsibilities require read access to a knowledge base and write access to a draft queue, and nothing else.
Which scoping design best applies least privilege?
Answer: B
Explanation:
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.
NEW QUESTION # 26
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