Anthropic CCAR-P : Claude Certified Architect - Professional

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Anthropic CCAR-P Exam Syllabus Topics:

Anthropic Claude Certified Architect - Professional Sample Questions:

1. An architect is reviewing a Claude-based candidate-screening tool prior to deployment. A stakeholder asserts that because the model was not trained on company data, no bias evaluation is necessary.
Which two responses most accurately challenge this assertion? (Select two.)

A) Transparency obligations are satisfied by disclosing that an AI system is in use, without further evaluation.
B) The system should be evaluated on outcome-disparity metrics across protected groups before deployment.
C) Bias evaluation is unnecessary when the model provider has published a responsible-use policy.
D) The model may carry demographic biases from pretraining that manifest in screening outcomes regardless of fine-tuning.
E) Fairness testing is required only when the training dataset is known to contain protected-class labels.

2. A Claude architect is auditing configuration scope assignments.
Which two statements correctly identify an appropriate use of user-scope configuration versus other scopes?
(Select two.)

A) Storing API authentication keys so they are not committed to version control.
B) Persisting personal editor theme preferences that follow an engineer across projects.
C) Enforcing a company-wide policy that disables a feature for all engineers.
D) Defining MCP server endpoints shared by all contributors to a specific repository.
E) Saving a preferred Claude response language that applies to all repositories the engineer uses.

3. You are building an ethics-review checklist for deployments supported by artificial intelligence.
Which two checks belong on the list? (Select two.)
Each correct answer presents a complete solution.

A) Verify that outputs do not rely on generalizations about people that the underlying data does not support.
B) Confirm that latency and throughput targets are met across supported user populations.
C) Confirm that high-impact decisions retain human accountability rather than being attributed to the model.
D) Confirm that vendor licensing terms permit the planned production use of the model.
E) Restrict ethics review to outputs that exceed a defined model-confidence threshold.

4. The platform team at Trenova Systems, Inc. needs to reduce per-query cost and p95 latency for a high-volume Claude pipeline without degrading output quality on the core use case.
Which two optimizations directly target both cost and latency simultaneously? (Select two.)

A) Route straightforward query types to a smaller, faster Claude model and reserve the full model for complex cases.
B) Increase max_tokens to reduce the frequency of truncated responses requiring follow-up calls.
C) Enable prompt caching on the static system-prompt prefix to reduce billable input tokens on repeated calls.
D) Expand the system prompt to include additional few-shot examples on every request.
E) Add a retrieval step that fetches the full source corpus into the context window before generation.

5. You are designing a content moderation classifier that processes high volumes of user-generated comments under a tight per-message latency budget using well-defined classification labels.
Which model selection best aligns with the workload?

A) Opus, because every moderation decision requires maximum reasoning depth regardless of classification complexity.
B) Sonnet with extended thinking enabled, because deeper reasoning should be applied to every moderation request to improve edge-case handling.
C) Sonnet, because larger general-purpose models are preferred even when workload latency requirements are strict.
D) Haiku, because its latency and cost profile align with high-volume classification workloads that require limited reasoning depth.

Solutions: