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| Section | Weight | Objectives |
|---|---|---|
| Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - Guardrails - System prompts and prompt templates - Context window optimization |
| Solution Design & Architecture | 17% | - Decomposition techniques for complex problem solving - End-to-end architecture design - Translating business problems into Claude-based AI solutions - Multi-agent systems and orchestration - Alignment with business value, cost, performance, and SLAs - Architectural patterns
|
| Evaluation, Testing & Optimization | 16% | - System issue diagnosis - Evaluation metrics and datasets - Cost and performance optimization - Production monitoring and optimization - A/B testing - Evaluation framework design |
| Governance, Safety & Risk Management | 14% | - Security and risk management - AI safety and guardrails - Human-in-the-loop validation - Regulatory and compliance requirements - Ethical AI considerations |
| Developer Productivity & Operational Enablement | 7% | - Claude tooling configuration for teams - Developer enablement - AI-assisted developer workflows - Debugging and operational issue resolution |
| Integration | 19% | - Claude integration mechanisms
- Enterprise system integration - RAG pipeline design
|
| Stakeholder Communication & Lifecycle Management | 14% | - Discovery and requirements gathering - Communicating architectural decisions - Service-level agreements - Solution lifecycle management - Architecture documentation - Stakeholder management |
>> CCAR-P Test Lab Questions <<
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NEW QUESTION # 28
You are reviewing a peer's draft system prompt that contains contradictory instructions: one section says never to speculate beyond the supplied source, while another says to confidently fill in any gaps.
Which response is most appropriate?
Answer: A
Explanation:
The prompt contains mutually incompatible behavioral requirements. The architect should resolve the contradiction explicitly so the model receives one coherent grounding policy. If the application requires source-supported answers, the prompt should prohibit unsupported completion, permit an insufficient- evidence response, and require citations or supporting passages. Adding another priority instruction leaves the underlying conflict in place and creates ambiguity about when speculation is permitted. Increasing temperature makes behavior more variable rather than more correct. Retaining both instructions produces inconsistent outputs across requests and model versions. Anthropic recommends clear, direct, specific prompting and notes that conflicting project instructions can cause Claude to select among them unpredictably. Prompting best practices ; project-instruction troubleshooting
NEW QUESTION # 29
A Claude-based research assistant begins producing responses that confidently contradict its retrieved source documents despite no change to the retrieval pipeline.
Which two diagnostic actions most directly identify the root cause of this behavior? (Select two.)
Answer: A,C
Explanation:
The retrieval pipeline is stated to be unchanged, so the first investigation should isolate changes in generation behavior and grounding instructions. Replaying the failing cases on the previous model version determines whether the regression follows the model upgrade or current model-prompt combination. Inspecting the system prompt establishes whether source-use rules, uncertainty behavior, citation requirements, or prohibitions against unsupported claims were removed or weakened. Increasing context or replacing the embedding model changes a component for which no failure evidence exists and may introduce additional noise. Lowering temperature may reduce variation but does not explain systematic contradiction of retrieved evidence. The investigation should compare identical requests, retrieved passages, prompts, and model versions before applying a fix. Define success criteria and evaluations
NEW QUESTION # 30
You are reviewing a peer's end-to-end design for a Claude-based platform expected to scale to thousands of concurrent users.
For each statement, indicate Yes if it reflects sound architectural practice. Otherwise, select No.
Answer:
Explanation:
Explanation:
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.
NEW QUESTION # 31
You are rolling out monitoring for a Claude-based deployment and must complete the specification steps before instrumenting the deployment.
Which two steps must be completed BEFORE instrumenting the deployment to emit metrics and traces?
(Select two.)
Each correct answer presents part of the solution.
Answer: D,E
Explanation:
Instrumentation must be driven by an explicit measurement specification. The team must first define the metrics and evaluation slices required to detect performance, cost, quality, safety, or reliability failures. Slices should include representative traffic, adversarial inputs, user segments, tools, and relevant content categories.
SLOs and error budgets must also be established before instrumentation because they determine required measurements, aggregation windows, and acceptable failure levels. Alert thresholds cannot be tuned until normal-state data has been collected. Dashboards and on-call documentation depend on the final metric definitions and observed operational behavior. Anthropic supports metrics, event, and trace export through OpenTelemetry, but telemetry is useful only when tied to measurable success and operating objectives.
Claude Code monitoring
NEW QUESTION # 32
You are identifying signals that a deployment should re-enter design rather than continue iterating in place.
Which signal most directly indicates the need for a new design cycle?
Answer: B
NEW QUESTION # 33
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