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The train might be early, so we'd better play safe and leave now. ()A.火车或许会早开，我们最好安全地玩耍。B.火车或许会早开，我们最好现在就动身。C.火车或许会早到，为了稳妥起见，我们最好现在就动身。D.火车或许会早到，我们最好安全地玩耍。

题目
The train might be early, so we'd better play safe and leave now. ()

A.火车或许会早开，我们最好安全地玩耍。

B.火车或许会早开，我们最好现在就动身。

C.火车或许会早到，为了稳妥起见，我们最好现在就动身。

D.火车或许会早到，我们最好安全地玩耍。

相似考题

1.We()achequeforRMB 200.A.encloseB.putC.putinD.leave

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

3.—What do you think of the _______car?—I think it is much better than his one.A. latest, lastB. lately, lastlyC. early, latelyD. early, lastly

4.I steer my ship with hope,________ fear ________ .A. leaving⋯awayB. leaving⋯behindC. leave⋯behindD. leave ⋯away

参考答案和解析
正确答案：C
解析：翻译本句的关键在于理解短语 “play safe”(“谨慎行事”、“采取安全措施”)。
更多“The train might be early, so we'd better play safe and leave now. () ”相关问题

  • 第1题：

    Don't forget to _________ the key _________ me when you go out.

    A.leave...out

    B.leave...on

    C.leave...for

    D.leave...with


    参考答案：D

  • 第2题：

    45．—We have to get up at 7 : 15 tomorrow morning．

    —Yes．__________we will be late for the 7 : 40 train．

    A．before '

    B．or

    C．if

    D．so


    正确答案：B
    45．B【解析】句意：明早我们必须7：15起床，是的，不然就赶不上7: 40的火车了。此句相当于If we don，t get up at 7 1 15 tomorrow morning,we will be late for the 7：40 train．根据题意选B。

  • 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：

    “The ceremony has already started. ”“Look! The flag () now. ”

    A、is being raised

    B、is risen

    C、is being rose

    D、is raising


    参考答案：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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