ガイド連動クイズ · 簡単 レベル

AI モデルの説明クイズ

測定された証拠を使用して、モデルの入力と出力、学習されたパラメーター、ハイパーパラメーター、取得、微調整、およびタスクベースのモデル選択をテストします。

関連するガイドパスAIモデル
質問 1 の 8

AI アプリケーションのどのコンポーネントが機械学習モデル自体ですか?

すべての質問をプレビューする8 質問

クイズに答えると、答えと解説が表示されます。

  1. AI アプリケーションのどのコンポーネントが機械学習モデル自体ですか?
    • 構造と学習されたパラメータを使用して入力を出力にマッピングする数学的システム
    • すべての製品機能を含むユーザー インターフェイス
    • モデルがアクセスを許可される一連の Web サイト
    • チームが AI をどのように使用するかを定義するポリシー文書
  2. A team has a firm 100 ms latency limit. In the guide’s illustrative test, which candidate meets it?
    • 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. Which item is a hyperparameter rather than a learned parameter?
    • 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. A search system supplies a retrieved document to a model at query time. What changes without retraining?
    • 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. A developer fine-tunes a model on additional examples. What changes?
    • 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