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NEW QUESTION: 1
SQLからData Lake Storageに毎日のインベントリデータをインポートするために、どのAzure Data Factoryコンポーネントを一緒に使用することをお勧めしますか？ 回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation:
Box 1: Self-hosted integration runtime
A self-hosted IR is capable of nunning copy activity between a cloud data stores and a data store in private network.
Scenario: Daily inventory data comes from a Microsoft SQL server located on a private network.
Box 2: Schedule trigger
Daily schedule
Box 3: Copy activity
Scenario:
Stage inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed.
NEW QUESTION: 2
AzureDatabricksで構造化ストリーミングソリューションを構築することを計画しています。このソリューションは、5分間隔で新しいイベントをカウントし、その間隔中に到着したイベントのみを報告します。出力はDeltaLakeテーブルに送信されます。
どの出力モードを使用する必要がありますか？
A. 完了
B. 追加
C. 更新
Answer: B
Explanation:
Explanation
Append Mode: Only new rows appended in the result table since the last trigger are written to external storage. This is applicable only for the queries where existing rows in the Result Table are not expected to change.
Reference:
https://docs.databricks.com/getting-started/spark/streaming.html
NEW QUESTION: 3
ラベル付き画像のセットを使用するマルチクラス画像分類の深層学習モデルを作成します。 PyTorch 1.3フレームワークを使用してモデルをトレーニングするtrain.pyという名前のスクリプトファイルを作成します。
見積もりツールを使用してスクリプトを実行する必要があります。このコードでは、推定ツールの環境に追加のPythonライブラリをインストールする必要はありません。モデルのトレーニングに必要な時間を最小限に抑える必要があります。
スクリプトの実行に使用する推定器を定義する必要があります。
どの推定器タイプを使用する必要がありますか？
A. Estimator
B. SKLearn
C. PyTorch
D. TensorFlow
Answer: C
Explanation:
Explanation
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models
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