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After successfully passing the Microsoft DP-100 exam, you will obtain the Microsoft Certified: Azure Data Scientist Associate certification. Getting certified will allow you to qualify for several positions, including the following titles:
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Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
| Section | Objectives |
|---|---|
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Design and prepare a machine learning solution | - Plan and configure Azure Machine Learning workspace - Manage compute and data assets - Select appropriate Azure services for machine learning workloads |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
機械学習モデルを作成しています。 null行を含むデータセットがあります。
Azure Machine Learning StudioのClean Missing Dataモジュールを使用して、データセット内のnullデータと欠落データを特定して解決する必要があります。
どのパラメーターを使用する必要がありますか?
Correct Answer: C 🗳️
Explanation: Only visible for Exams4Collection members. You can sign-up / login (it's free).
注:この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このセクションの質問に回答すると、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
あなたはAzure Machine Learning Studioを使用しているデータサイエンティストです。
ターゲット列を予測するには、値を正規化して出力列をビンに生成する必要があります。
解決策:QuantileIndex正規化でQuantiles正規化を適用します。
ソリューションは目標を満たしていますか?
Correct Answer: B 🗳️
Explanation: Only visible for Exams4Collection members. You can sign-up / login (it's free).
以前に、CSV ファイルのフォルダーに基づく training-dataset という表形式のデータセットを使用してトレーニングされたモデルをデプロイしました。
時間の経過とともに、モデルによって生成された特徴量と予測ラベルが、各月のCSVファイルを含むフォルダーに収集されました。推論データを含むフォルダーに基づいて、2つの表形式のデータセットを作成しました。1つは予測ラベルを含むトレーニングデータと完全に一致するスキーマを持つ予測データセット、もう1つは特徴量列と、ファイル名に基づく日、月、年を含むタイムスタンプ列を含むスキーマを持つ特徴データセットです。
モデルのトレーニング以降の特徴データの変化傾向を特定するには、データドリフトモニターを作成する必要があります。そのためには、データドリフトモニターに必要なデータセットを定義する必要があります。
データドリフトモニターを構成するには、どのデータセットを使用すればよいでしょうか?適切なデータセットを適切なデータドリフトモニターのオプションにドラッグしてください。各ソースは、1回使用することも、複数回使用することも、まったく使用しないこともできます。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要がある場合があります。
注意: 正しい選択ごとに 1 ポイントが付与されます。
Correct Answer:

Explanation:
Box 1: training-dataset
Baseline dataset - usually the training dataset for a model.
Box 2: predictions-dataset
Target dataset - usually model input data - is compared over time to your baseline dataset. This comparison means that your target dataset must have a timestamp column specified.
The monitor will compare the baseline and target datasets.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets
Azure Machine Learning SDK v2 for Pythonとノートブックを使用してモデルをトレーニングします。Pythonコードを使用して、コンピューティング先、環境、トレーニングスクリプトを作成します。トレーニングジョブを送信するための情報を準備する必要があります。どのクラスを使用すればよいですか?
Correct Answer: A 🗳️
バイナリ分類を実行するためにリカレント ニューラル ネットワークを構築しています。
各トレーニングエポックのトレーニング損失、検証損失、トレーニング精度、検証精度が提供されています。分類モデルが過剰適合していないかどうかを確認する必要があります。
次のどれが正しいでしょうか?
Correct Answer: C 🗳️
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