Scale AI Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Nderipi dambudziko guru iro Scale AI inonyanya kugadzirisa kune vanogadzira AI?
   • Kupa yemhando yepamusoro yakanyorwa uye yakachengetedzwa kudzidziswa data
   • Kugadzira machipisi emakombuta
   • Kugadzira mabhurawuza
   • Mabhatiri ekugadzira
2. Imhandoi yedata iyo Scale AI yakatarisa pakunyora payakatanga?
   • Mimhanzi zvakarekodhwa
   • Mifananidzo yemotokari dzinozvityaira
   • Stock market tickers
   • Weather setiraiti data
3. Ko RLHF, sevhisi Scale inopa maLLMs, inomirirei?
   • Random Library Hash Basa
   • Rapid Logic Hardware Framework
   • Kusimbisa Kudzidza kubva Kuvanhu Feedback
   • Recursive Mutauro Hosting Format
4. Why can expert-generated training examples help improve a model?
   • They guarantee the model will be correct on every future task
   • They remove the need to evaluate the model
   • They replace all training data with synthetic labels
   • They provide task-specific demonstrations or feedback for capabilities not well represented in available general data
5. Which platform does Scale AI identify as part of its specialized GenAI contributor ecosystem?
   • Outlier
   • MLflow Tracking
   • Kubatana Catalog
   • EnCodec
6. Kupfuura kunyora, ndeipi AI yekuchengetedza sevhisi inopa Scale?
   • Kutengesa antivirus software
   • Tsvuku-team uye yekuongorora mhando dzekuchengetedza uye kuvimbika
   • Kugadzirisa mafoni
   • Kugadzira vhidhiyo yemutambo mifananidzo
7. What kind of judgment data is commonly used in RLHF?
   • Only the model’s parameter file names
   • A list of GPU serial numbers
   • Human comparisons or rankings of model responses
   • Unlabeled images with no task signal
8. What is the purpose of red-teaming an AI model or system?
   • Guarantee that the system can never fail
   • Replace all model training data
   • Measure only GPU temperature
   • Probe it with adversarial or misuse scenarios to identify safety weaknesses