Kuis Penjelasan Model AI

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. Komponen aplikasi AI manakah yang merupakan model pembelajaran mesin itu sendiri?
   • Sistem matematika yang memetakan masukan ke keluaran dengan struktur dan parameter yang dipelajari
   • Antarmuka pengguna yang berisi setiap fitur produk
   • Sekumpulan situs web yang boleh dikunjungi oleh model
   • Dokumen kebijakan yang menjelaskan cara tim menggunakan AI
2. Sebuah tim memiliki batas latensi tetap 100 ms. Dalam tes ilustratif panduan ini, kandidat mana yang memenuhinya?
   • Model B at 94% accuracy and 400 ms
   • Model A at 92% accuracy and 30 ms
   • Neither, because accuracy must be 100%
   • Both, because they score above 90%
3. Item mana yang merupakan hyperparameter dan bukan parameter yang dipelajari?
   • A weight value adjusted during training
   • A coefficient the fitted model uses for an input
   • A tree-depth limit selected through validation
   • A prediction returned for one example
4. Sistem pencarian menyediakan dokumen yang diambil ke model pada waktu kueri. Perubahan apa yang terjadi tanpa pelatihan ulang?
   • It necessarily retrains model weights
   • It fine-tunes the model on every query
   • It permanently changes the model version
   • It adds available evidence without changing model weights
5. Pengembang menyempurnakan model berdasarkan contoh tambahan. Perubahan apa?
   • Model weights, without guaranteeing the information is current
   • Only the text retrieved for one query
   • The application’s user interface but never the model
   • The validation set but not model behavior
6. A team adapts a foundation model to a new document task. Which statement about this reuse is accurate?
   • It is necessarily the first AI model ever built
   • It can be adapted to multiple tasks, but still needs evaluation
   • It is only a model for building construction
   • It must be fine-tuned before it can answer any prompt
7. Why should a team re-evaluate after changing a model version?
   • Interfaces always change whenever versions change
   • A new version makes latency measurements unnecessary
   • Behavior can change even while interfaces stay stable
   • A model name proves it is more accurate
8. How should a team choose among candidate models for a defined application?
   • Select the largest parameter count by default
   • Use a leaderboard score without checking its errors
   • Choose the newest version without testing
   • Compare representative inputs, errors and operating constraints, then prefer the simplest adequate option