Cuestionario vinculado a una guía · Medio Nivel

Prueba de IA generativa

Evalúe los resultados de la IA generativa según su mecanismo y uso, separando los hechos fuente de las afirmaciones del modelo y verificando la precisión, los artefactos y el riesgo.

Rutas de guía relacionadasIA generativa
Pregunta 1 de 8

Which output best illustrates generative AI as described in the guide?

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  1. Which output best illustrates generative AI as described in the guide?
    • A newly produced summary, image, audio clip or code sample
    • A fixed list of records copied without change
    • A report showing only whether a login succeeded
    • A hand-entered table with no model output
  2. How does an autoregressive text model produce a sequence, according to the guide?
    • It retrieves a complete answer from a fixed encyclopedia
    • It predicts successive tokens using the text so far
    • It denoises random pixels into a picture
    • It converts a waveform into a class label
  3. A fluent generated summary includes a claim not found in its source. What should the reviewer do?
    • Assume fluency signals accuracy
    • Remove the source to avoid comparison
    • Separate source statements from model inference and verify the claim
    • Publish it because summaries may add context
  4. Which description matches diffusion-based image generation in the guide?
    • A system that searches the web for a matching finished picture
    • A system that copies clip art outlines
    • A system that writes one pixel row at a time
    • A system that transforms noisy representations into samples
  5. What should be checked when evaluating generated code?
    • Only whether its comments are clear
    • Whether it behaves as intended, using meaningful tests
    • Whether it was produced in one try
    • Whether the prompt contains a question mark
  6. Why should a generated citation be opened and checked?
    • Citation-shaped text can refer to a source that does not exist
    • Every model removes source details
    • A citation is always a training-data license
    • The guide says sources cannot support claims
  7. Which evaluation plan best follows the guide?
    • Use one preference score for text, code and images
    • Judge an output only by how polished it sounds
    • Match checks to the artifact, such as factual consistency for a summary and behavior tests for code
    • Skip review when the model version is new
  8. What information may help reproduce an output when that matters?
    • Only the user’s name
    • The color of the interface
    • la hora del dia
    • The model version, prompt, source material and settings