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NEW QUESTION: 1
You create a script for training a machine learning model in Azure Machine Learning service.
You create an estimator by running the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
Parameter source_directory is a local directory containing experiment configuration and code files needed for a training job.
Box 2: Yes
script_params is a dictionary of command-line arguments to pass to the training script specified in entry_script.
Box 3: No
Box 4: Yes
The conda_packages parameter is a list of strings representing conda packages to be added to the Python environment for the experiment.
NEW QUESTION: 2
What does the BCSD Write Load Adjustment Factor account for when designing SRDF solutions?
A. I/O burstiness
B. Data compression
C. I/O Locality of reference
D. Write folding
Answer: A
NEW QUESTION: 3
小売企業の開発者は、不正検出ソリューションを注文処理ソリューションに統合する必要があります。不正検出ソリューションは、注文を確認するのに10分から30分かかります。ピーク時には、Webサイトは1分あたり100件の注文を受け取ることができます。
不正検出ソリューションを注文処理パイプラインに追加するための最もスケーラブルな方法は何ですか？
A. すべての新しい注文をSQSキューに追加します。キューの深さのメトリックをスケールの単位として使用するAutoScalingグループを構成し、不正検出ソリューションがインストールされた複数のAZにまたがる動的なサイズのEC2インスタンスのフリートを起動して、このキューから注文をプルします。合格または不合格のステータスで注文を更新します。
B. すべての新しい注文をAmazonSQSキューに追加します。このキューから注文をプルするために、不正検出ソリューションがインストールされた複数のAZにまたがる10個のEC2インスタンスのフリートを設定します。合格または不合格のステータスで注文を更新します。
C. すべての新しい注文をAmazonDynamoDBに書き込みます。すべての新しい注文を含めるようにDynamoDBストリームを設定します。 Lambda関数をサブスクライブして、KinesisStreamからレコードのバッチを自動的に読み取ります。ラムダ関数には不正検出ソフトウェアが含まれており、合格または不合格のステータスで注文を更新します。
D. すべての新しい注文をAmazon KinesisStreamに追加します。 Lambda関数をサブスクライブして、KinesisStreamからレコードのバッチを自動的に読み取ります。ラムダ関数には不正検出ソフトウェアが含まれており、合格または不合格のステータスで注文を更新します。
Answer: A
NEW QUESTION: 4
下图所示的接口类型是（)
A. SMA 公头
B. N 型公头
C. N 型母头
D. SMA 母头
Answer: B