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Testet Aufgabenexposition, Akzeptanzunsicherheit, Erweiterung, Überprüfungsarbeitsbelastung, Arbeitsagentur, Arbeitsqualität und lokale Arbeitsmarktdaten.

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An occupation has a high AI exposure score. What does that score establish?

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  1. An occupation has a high AI exposure score. What does that score establish?
    • Potential task overlap, not a direct forecast of job loss
    • That every worker will be replaced
    • How many new jobs will be created
    • The date a workplace will adopt AI
  2. Why analyze tasks within a job rather than treating the occupation as one unit?
    • Every task in an occupation has the same technical requirements
    • A role combines tasks that may be automated, augmented, or still need human context
    • Occupation titles fully describe daily work
    • One automated task means the role can be removed
  3. A model can draft a report. Why might an organization still not adopt it?
    • Technical feasibility guarantees deployment
    • Only model size determines adoption
    • Reliability, cost, integration, regulation, and work choices affect adoption
    • Worker training is irrelevant
  4. A tool speeds up one stage but adds a large review queue. What should be evaluated?
    • Only generation speed
    • Die Anzahl der verwendeten Eingabeaufforderungen
    • The model’s benchmark score alone
    • Total work and job quality after review and correction are included
  5. Which statement distinguishes augmentation from replacement?
    • A system may assist one workflow while automating part of another
    • Every AI use either fully replaces or never changes a job
    • Augmentation means no work changes
    • Replacement can be inferred from task exposure alone
  6. What labor-market evidence should inform a career decision?
    • A global exposure estimate used as a personal forecast
    • Current information for the specific location or occupation
    • A single company’s product launch
    • An old job title list without a date
  7. Which capability can help workers evaluate AI-supported tasks?
    • Prompt memorization without subject knowledge
    • Avoiding all review responsibility
    • Domain knowledge, output evaluation, exception handling, and clear communication
    • Choosing a model by parameter count
  8. Why should workers be able to challenge inaccurate AI outputs?
    • A challenge process guarantees no errors
    • Only managers can detect mistakes
    • A model recommendation removes human accountability
    • They may carry error responsibility and need a way to correct outcomes