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Anthropic Claude Certified Architect - Professional Sample Questions:

1. A customer support team has proposed delegating customer refund decisions to a Claude-driven workflow with no human review for refunds under 50 USD. The team's reasoning is that small refunds are low-risk and human review would erase the efficiency gain.
Which Delegation-competency principle should guide your response?

A) Delegation scope should be set primarily by maximizing efficiency gains across the workflow.
B) Delegation scope should reflect the type of risk involved, not the transaction size alone.
C) Delegation should always include human review on every decision the workflow produces.
D) Delegation should be avoided entirely wherever financial transactions occur in the workflow.

2. You are assessing a Claude-based system whose dominant risk is silent quality drift on safety-relevant outputs after a model-version upgrade.
Which assessment activity most directly addresses this risk?

A) Rotate adversarial inputs randomly so no two upgrades are scored on the same set.
B) Maintain an adversarial evaluation set with version-attributed scoring so each upgrade is measured against the same set before promotion.
C) Disable adversarial evaluation entirely during model-version upgrade cycles to reduce evaluation cost, accepting that safety drift will go undetected until it appears in production.
D) Skip evaluation on each model-version upgrade and rely on user-submitted complaints to surface safety drift after the upgraded model has already served production traffic.

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

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

5. You are reviewing instrumentation in a multi-agent system.
Which two findings constitute valid observability gaps in the instrumentation? (Select two.) Each correct answer presents a complete solution.

A) Model identity and version on each turn are not recorded with the turn artifacts.
B) Tool-call payloads and outcomes are recorded with redaction applied to known sensitive fields.
C) Trace spans for each agent step are exported to the shared distributed-tracing backend.
D) Request-scoped correlation identifiers do not propagate across agent and tool calls.
E) Latency and token usage on every span are emitted to the central metrics pipeline.

Solutions: