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| Section | Weight | Objectives |
|---|---|---|
| Claude Models, Prompting & Context Engineering | 13% | - Guardrails - System prompts and prompt templates - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - Context window optimization |
| Governance, Safety & Risk Management | 14% | - Security and risk management - Ethical AI considerations - AI safety and guardrails - Human-in-the-loop validation - Regulatory and compliance requirements |
| Developer Productivity & Operational Enablement | 7% | - Developer enablement - AI-assisted developer workflows - Claude tooling configuration for teams - Debugging and operational issue resolution |
| Evaluation, Testing & Optimization | 16% | - Evaluation framework design - Production monitoring and optimization - Cost and performance optimization - A/B testing - System issue diagnosis - Evaluation metrics and datasets |
| Stakeholder Communication & Lifecycle Management | 14% | - Communicating architectural decisions - Architecture documentation - Discovery and requirements gathering - Stakeholder management - Service-level agreements - Solution lifecycle management |
| Solution Design & Architecture | 17% | - End-to-end architecture design - Architectural patterns
- Decomposition techniques for complex problem solving - Translating business problems into Claude-based AI solutions - Alignment with business value, cost, performance, and SLAs |
| Integration | 19% | - Enterprise system integration - Authentication and authorization analysis - RAG pipeline design
|
>> CCAR-P Quizfragen Und Antworten <<
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28. Frage
You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."
Antwort:
Begründung:
Explanation:
* Persist the validated output for downstream consumption and audit - Output Handling
* Order prompt sections so cacheable content appears before per-request content - Prompt Construction
* Summarize prior conversation history when full history is no longer needed - Input Preparation
* Validate the model's structured output against the expected schema - Output Handling
* Move stable repeated content into a cacheable prefix at the beginning of the prompt - Prompt Construction
* Trim retrieved passages to spans relevant to the user's question - Input Preparation Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery.
Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic's context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows; prompt caching
29. Frage
A document analysis service processes legal filings averaging 80,000 tokens each. Each filing is queried by attorneys an average of 14 times during a case. The current architecture sends the full filing on every query.
The CFO has asked you to reduce per-query costs while preserving response quality. The security officer requires that filing contents not be stored outside Fabrikam's tenancy.
Which optimization approach should you recommend?
Antwort: C
Begründung:
The 80,000-token filing is a large, stable prefix reused across fourteen requests, making prompt caching the strongest optimization. The first request creates the cache; subsequent identical-prefix requests use lower- priced cache reads while retaining the complete filing, preserving the evidence available to Claude. Anthropic documents cache reads at a fraction of standard input cost and confirms that caches are isolated by organization and, on supported platforms, by workspace. Cached representations remain in memory rather than persistent storage. Options A and C may omit context through summarization or retrieval and therefore require additional quality evaluation. Option D lowers unit cost but may reduce reasoning quality. The design must preserve prefix identity, choose an appropriate TTL, and verify the tenancy configuration.
Study Guide references/topics: Prompt caching ; cache isolation; stable prefixes; long-document analysis; cost optimization.
30. Frage
You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."
Antwort:
Begründung:
Explanation:
* Persist the validated output for downstream consumption and audit - Output Handling
* Order prompt sections so cacheable content appears before per-request content - Prompt Construction
* Summarize prior conversation history when full history is no longer needed - Input Preparation
* Validate the model's structured output against the expected schema - Output Handling
* Move stable repeated content into a cacheable prefix at the beginning of the prompt - Prompt Construction
* Trim retrieved passages to spans relevant to the user's question - Input Preparation Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery.
Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic's context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows ; prompt caching
31. Frage
You are defining when to introduce a project subagent versus relying on Claude Code's general capabilities.
Which scenario most directly justifies a dedicated subagent?
Antwort: D
Begründung:
A dedicated subagent is justified when specialization is recurring and operationally valuable. Option A allows the team to encode a database-review role once, including its system prompt, triggering description, permitted tools, model, and review criteria. This improves consistency across sessions, isolates specialized context, and applies least privilege by withholding unrelated capabilities. Anthropic's custom subagent documentation supports task-specific prompts, model selection, tool allowlists, permission modes, hooks, skills, and persistent memory. A one-time question does not justify the maintenance overhead of a reusable agent definition, while Options C and D explicitly provide no specialization requirement. The subagent should also have clear invocation conditions, evaluation cases, ownership, and lifecycle review.
Study Guide references/topics: Claude Code subagents; task specialization; context isolation; model routing; scoped permissions; reusable automation.
32. Frage
You are auditing a procurement-assistant agent whose defined responsibility is to draft purchase requests for review.
For each tool currently configured on the agent, select yes if the tool should remain after a least-privilege audit. Otherwise, select no if it should be removed.
Antwort:
Begründung:
Explanation:
Yes, No, Yes, No, Yes
Least privilege requires each retained tool to be necessary for the agent's assigned responsibility and restricted to the minimum action level needed. Catalog lookup is appropriate because vendor and SKU information is needed to prepare an accurate request. The policy-document tool is similarly justified because the agent must ground the draft in applicable procurement rules. The draft-write tool is also aligned because it writes only to a review queue, preserving the required human approval boundary.
The inbox-send tool exceeds the stated responsibility. Sending a request directly to a vendor converts a drafting assistant into an externally acting purchasing agent and bypasses the review stage. The production- database administrator tool is unrelated to procurement and introduces unnecessary destructive capability, privilege-escalation exposure, and potential data-access risk.
Anthropic's prompt-injection guidance recommends limiting access to sensitive data and actions and applying least privilege so that a compromised agent can cause minimal damage. Claude Code also supports tool- specific allow, ask, and deny rules that are enforced outside the model. Prompt-Injection Mitigation, Claude Code Permissions Study Guide references/topics: Capability-bloat auditing; least privilege; tool scoping; human review gates; separation of drafting and execution authority.
33. Frage
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