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NEW QUESTION: 1
You use Microsoft .NET Framework 4 to create a Windows Forms application. You have a dataset as shown in the following exhibit.

---
You plan to add a DataGridView to display the dataset.
You need to ensure that the DataGridView meets the following requirements:
Shows Order Details for the selected order. Shows only Order Details for items that have UnitPrice greater than 20 Sorts Products by ProductName
Which code segment should you use?
A. order DetailsBindingSource.DataSource = ordersBindingSource; order_DetailsBindingSource.DataMember = "FK_Order_Details_Orders". order_DetailsBindingSource .Filter = "UnitPrice > 20"; productsBindingSource.Sort "ProductName"; C
B. productsDataGridView.DataSource = ordersBindingSource; productsBindingSource.Filter = "UnitPrice > 20"; productsBindingSource.Sort = "ProductName";
C. order_DetailsDataGridViev.DataSource = ordersBindingSource; order_DetailsBindingSource.Filter = "UnitPrice > 20"; productsBindingSource.Sort "ProductName";
D. ordersBindingSource.DataSource = producxsBindingSource; ordersBindingSource.DataMember = "FK_Order_Details_Products"; productsBindingSource.Filter = "UnitPrice > 20"; productsBindingSource.Sort = ProductName";
Answer: A

NEW QUESTION: 2
英語のテキストコンテンツをフランス語のテキストコンテンツに翻訳するための機械学習モデルを構築しています。
テキストコンテンツのシーケンスを学習するには、機械学習モデルを構築してトレーニングする必要があります。
どのタイプのニューラルネットワークを使用する必要がありますか？
A. Multilayer Perceptions (MLPs)
B. Convolutional Neural Networks (CNNs)
C. Recurrent Neural Networks (RNNs)
D. Generative Adversarial Networks (GANs)
Answer: C
Explanation:
To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both.
They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
References:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571

NEW QUESTION: 3
Which process describes the lifecycle of a Mapper?
A. The TaskTracker spawns a new Mapper to process all records in a single input split.
B. The TaskTracker spawns a new Mapper to process each key-value pair.
C. The JobTracker spawns a new Mapper to process all records in a single file.
D. The JobTracker calls the TaskTracker's configure () method, then its map () method and finally its close () method.
Answer: A
Explanation:
For each map instance that runs, the TaskTracker creates a new instance of your mapper.
Note:
*The Mapper is responsible for processing Key/Value pairs obtained from the InputFormat. The mapper may perform a number of Extraction and Transformation functions on the Key/Value pair before ultimately outputting none, one or many Key/Value pairs of the same, or different Key/Value type.
*With the new Hadoop API, mappers extend the org.apache.hadoop.mapreduce.Mapper class.
This class defines an 'Identity' map function by default - every input Key/Value pair obtained from
the InputFormat is written out.
Examining the run() method, we can see the lifecycle of the mapper:
/**
*Expert users can override this method for more complete control over the
*execution of the Mapper.
*@param context
*@throws IOException
*/
public void run(Context context) throws IOException, InterruptedException {
setup(context);
while (context.nextKeyValue()) {
map(context.getCurrentKey(), context.getCurrentValue(), context);
}
cleanup(context);
}
setup(Context) - Perform any setup for the mapper. The default implementation is a no-op method.
map(Key, Value, Context) - Perform a map operation in the given Key / Value pair. The default
implementation calls Context.write(Key, Value)
cleanup(Context) - Perform any cleanup for the mapper. The default implementation is a no-op
method.
Reference: Hadoop/MapReduce/Mapper