quiz bu lëkkale ak tegtal · Dëgër Niveau

Quiz de référence IA

Saytu ni liggéeyi benchmark yi, done yi, jekkal yi, ñàkka wóor yi, kontaminasioŋ yi, ak test yiñ jagleel aplikaasioŋ yi di amee ci li poñ IA mëna la wax.

Yooni njiit yu jëm ciRéférences IA
Laaj 1 de 8

What is an AI benchmark?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. What is an AI benchmark?
    • A defined set of tasks, data, and scoring rules used to compare systems
    • A universal measure of intelligence
    • A guarantee the highest-scoring model is best for an application
    • Limu parametru model
  2. Two systems both report “accuracy.” What should be checked before comparing them?
    • Only model names
    • Only the number of parameters
    • Task definition, examples, scoring rules, and test conditions
    • Only whether the two reports use the same metric name
  3. Why record model version, prompt, tools, attempts, and evaluation date?
    • A record guarantees no data leakage
    • Those settings can change benchmark results
    • Settings do not affect model behavior
    • Only the date matters
  4. What is dataset contamination in an evaluation?
    • The benchmark includes multiple task types
    • A model uses a different prompt
    • A dataset contains timestamps
    • Test items or close variants were available during development
  5. A 100-question test shows one model answers 81 correctly and another 83. What does that difference alone establish?
    • Only the observed counts, not a reliable advantage by itself
    • That the second model is universally better
    • That the first model cannot solve the task
    • That the test set was contaminated
  6. Why can an average benchmark score hide weaknesses?
    • Averages always reveal every failure
    • A single metric measures all applications equally
    • Performance may differ across tasks, conditions, or groups
    • Scores do not depend on the task mix
  7. What should be compared alongside task success?
    • Only parameter count
    • Cost and latency
    • Only leaderboard position
    • Only how many times the result was cited
  8. Why add a private evaluation set for an application?
    • It guarantees a higher public score
    • It eliminates the need to document prompts
    • It prevents all model errors
    • It tests whether public-benchmark results transfer to the intended workflow