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Turn 2026 AI skills trends into one role-specific interview story: define the workflow, show your decision, and separate measured gains from estimates.
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Build an AI agent eval around a timed-out tool call: distinguish final state from agent narration, test duplicate actions, and explain repeated trials.
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Practice an MCP security scenario where access changes after approval. Explain execution-time authorization, narrow scope, revocation, and audit evidence.
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Practice a performance interview where faster code is not enough: preserve correctness, control the benchmark, and defend one optimization at a time.
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Present an unshipped AI project honestly: distinguish prototype evidence from production claims, explain a blocked launch, and demonstrate your decisions.
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Before an AI-enabled hiring round, clarify whether you face recorded answers or adaptive questions, then rehearse the right format and keep a factual record.
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Diagnose agentic coding benchmark regressions by controlling runtime limits, separating infrastructure failures, and reporting comparable repeated trials.
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Practice context engineering with a corrected project claim: preserve source order, invalidate stale summaries, and recover without inventing experience.
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