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Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
| Section | Weight | Objectives |
|---|---|---|
| Explore data and run experiments | 20-25% | - Explore and visualize data
|
| Optimize language models for AI applications | 25-30% | - Implement generative AI solutions
|
| Train and deploy models | 25-30% | - Monitor and maintain models
|
| Design and prepare a machine learning solution | 20-25% | - Design a machine learning solution
|
Azure Machine Learning ワークスペースを管理します。
パイプライン コンポーネントの出力として urijolder データ型を選択します。
構成でサポートされているデータ アクセス モードを定義する必要があります。
どのモードを定義する必要がありますか?
Correct Answer: B 🗳️
Workspace1 という名前の Azure Machine Learning ワークスペースを管理します。
アクセスキーを使用して、ワークスペース1 に Azure Blob Storage データストアを登録する必要があります。データストアの登録に必要なすべてのモジュールをインポートするための Python SDK v2 コードを開発します。
データストアを定義するには、Python SDK v2 コードを完成させる必要があります。
コードをどのように完成させるべきですか? 回答するには、回答エリアで適切なオプションを選択してください。
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:
財務チームから、finance-data という名前の Azure Storage BLOB コンテナー内のデータを使用してモデルをトレーニングするように依頼されました。
コンテナーを Azure Machine Learning ワークスペースのデータストアとして登録し、コンテナーが存在しない場合にエラーが発生するようにする必要があります。
コードをどのように完成させるべきですか? 回答するには、回答エリアで適切なオプションを選択してください。
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: register_azure_blob_container
Register an Azure Blob Container to the datastore.
Box 2: create_if_not_exists = False
Create the file share if it does not exists, defaults to False.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore
機械学習モデルを使用してインテリジェントなソリューションを構築しています。
環境は次の要件をサポートする必要があります。
データサイエンティストはクラウド環境でノートブックを構築する必要がある
データ サイエンティストは、機械学習パイプラインで自動特徴エンジニアリングとモデル構築を使用する必要があります。
動的なワーカー割り当てを備えた Spark インスタンスを使用して再トレーニングするには、ノートブックをデプロイする必要があります。
ノートブックは、ローカルでバージョン管理するためにエクスポート可能である必要があります。
環境を整える必要があります。
どの 4 つのアクションを順番に実行する必要がありますか? 回答するには、適切なアクションをアクション リストから回答領域に移動し、正しい順序に並べます。
Correct Answer:

Explanation:
Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library Step 2: Install Microsot Machine Learning for Apache Spark You install AzureML on your Azure HDInsight cluster.
Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.
Step 3: Create and execute the Zeppelin notebooks on the cluster
Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.
Notebooks must be exportable to be version controlled locally.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook
https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html
Azure Machine Learning ワークスペースがあり、スイープジョブを使用してモデルのハイパーパラメータを調整する予定です。
低パフォーマンスのジョブの早期終了と継続的なハイパーパラメータをサポートするサンプリング方法を見つける必要があります。
解決策: ハイパーパラメータ空間で Sobol サンプリング法を使用します。
ソリューションは目標を満たしていますか?
Correct Answer: A 🗳️
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Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
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