Questionário vinculado ao guia · Difícil Nível
Questionário sobre IA e empregos
Testa a exposição da tarefa, incerteza de adoção, aumento, carga de trabalho de revisão, agência do trabalhador, qualidade do trabalho e evidências do mercado de trabalho local.
Pergunta 1 de 8
An occupation has a high AI exposure score. What does that score establish?
Visualize cada pergunta8 perguntas
As respostas e explicações aparecem conforme você responde ao teste.
- An occupation has a high AI exposure score. What does that score establish?
- Potencial sobreposição de tarefas, não uma previsão direta de perda de emprego
- Que todo trabalhador será substituído
- Quantos novos empregos serão criados
- A data em que um local de trabalho adotará IA
- 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
- 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
- A tool speeds up one stage but adds a large review queue. What should be evaluated?
- Only generation speed
- The number of prompts used
- The model’s benchmark score alone
- Total work and job quality after review and correction are included
- 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
- 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
- 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
- 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