A: How long does it take to go there by train? B:().A、It is 20 poundsB、It takes 20 hoursC、The training is very fast

题目
A: How long does it take to go there by train? B:().

A、It is 20 pounds

B、It takes 20 hours

C、The training is very fast

相似考题

1.If it ( ) tomorrow, we ( ) to the Summer Palace.A、rains, will goB、won't rain, goC、doesn't rain, will go

2.publicclassYikes{publicstaticvoidgo（Longn）{System.out.println（”Long）；}publicstaticvoidgo（Shortn）{System.out.println（”Short）；}publicstaticvoidgo（intn）{System.out.println（”int）；}publicstaticvoidmain（String[]args）{shorty=6；longz=7；go（y）；go（z）；}}Whatistheresult？（）A.intLongB.ShortLongC.Compilationfails.D.Anexceptionisthrownatruntime.

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更多“A: How long does it take to go there by train? B:(). ”相关问题

  • 第1题：

    4．—_________ Mike________ his homework in the evening?

    —No，he doesn't．

    A． Do；does

    B． Does；do

    C． Do；do

    D． Does；does


    正确答案：B
    4.B【解析】考查实义动词的疑问式为“Does+主语+do，故选B。

  • 第2题：

    下列词形变化中，运用了附加手段的是（）

    A.pen-pens
    B.long-longer
    C.foot–feet
    D.man–men
    E.go–went

    答案：A,B
    解析：

  • 第3题：

    下列哪些语句会开始模型的训练：

    A．LinearRegression().fit(x_train,y_train)

    B．lr_mod.predict(x_train)

    C．lasso_mod.fit(x_train,y_train)

    D．vote_mod.predict(x_train)


    B

  • 第4题：

    Which of the following may illustrate the difference between "competence" and__________ "performance"?

    A．What a person "knows" and what he/she "does".
    B．What a person "can do" and what he/she "does".
    C．What a person "does" and what he/she "knows".
    D．What a person "does" and what he/she "can do".

    答案：A
    解析：
    考查语言学知识。competence“语言能力”，指语言使用者对于语言规则系统的潜在意识；performance“语言运用”，指在具体场景中语言的实际运用，二者对应的分别为“knows”和“does”。故选A。

  • 第5题：

    在MINST数据集中，访问训练集train_x的第4个样本，可以通过_______语句实现。

    A．train_x[4]

    B．train_x[3]

    C．train_x[0:3]

    D．train_x[:4]


    train_x[3]

  • 第6题：

    5、对手写数字数据集MNIST中的train_x训练集（60000,28,28)进行切片，下面对切片结果描述错误的是_______。 import tensorflow as tf import numpy as np mnist = tf.keras.datasets.mnist （train_x, train_y), （test_x, test_y) = mnist.load_data（)

    A．train_x[0, :, :]：第1张图片

    B．train_x[0:10, :, :]：前10张图片

    C．train_x[:, 0:28:2, :]：对所有图片隔行采样

    D．train_x[0:28:2, :, :]：对所有图片隔列采样


    D

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