13% of those questioned gave bad housing as their main ________ of worry.A.resourceB.aimC.sourceD.origin

题目
13% of those questioned gave bad housing as their main ________ of worry.

A.resource

B.aim

C.source

D.origin

相似考题

1.()more experience ,he would have done it better.A、GivenB、GivingC、To giveD、Gave

2.The cake she made looks ( ) but it tastes ( ).A、well/ badB、badly/ wellC、bad/ wellD、bad/ good

3.He ()her a beautiful hat on her next birthday.A. givesB. is going to giveC. gave

4.Tom _________ himself ________ to have got the medal.A. considered⋯luckilyB. proved⋯luckilyC. considered⋯luckyD. questioned⋯luckily

更多“13% of those questioned gave bad housing as their main ________ of worry. ”相关问题

  • 第1题：

    We are () to understand that you are potential buyers of Chinese.

    A、give

    B、giving

    C、given

    D、gave


    参考答案：C

  • 第2题：

    [A] employed

    [B] appointed

    [C] interviewed

    [D] questioned


    正确答案：C

  • 第3题：

    下面程序段的执行结果为______。 import tensorflow as tf boston_housing= tf.keras.datasets.boston_housing （train_x,train_y),（test_x,test_y)= boston_housing.load_data（test_split=0.1) print（"Training set:", len（train_x)) print（"Testing set:", len（test_x))

    A．Training set: 404 Testing set: 102

    B．Training set: 51 Testing set: 455

    C．Training set: 102 Testing set: 404

    D．Training set: 455 Testing set: 51


    程序报错

  • 第4题：

    The home team ______ dearly for their defensive errors.

    A、cost

    B、 took

    C、paid

    D、gave


    参考答案：C

  • 第5题：

    The word “tallied” （Line 3, Para. 2） probably means ______.

    A．calculated

    B．corresponded to

    C．listed

    D．gave


    正确答案：A
    A猜词题。单词所在的句子为“Mr． Luzaich, a mechanical engineer in Windsor, Calif． —in the Sonoma County wine country—first tallied the costs of his reasonable consumption in October 200 1．”（Luzaich先生是加利福尼亚州温莎的机械工程师，他2001年10月第一次……适度饮酒的费用。）文中后面他说的话是他浪费了好多酒的事实，而并未给出他饮酒的费用，因此，四个选项A“计算”，B“符合”，C“列出”，D“给出”中，C、D不符，B也不符合句意，选项A符合逻辑，通过计算他得出自己喝酒其实浪费许多。

  • 第6题：

    8、下面程序段的执行结果为______。 import tensorflow as tf boston_housing= tf.keras.datasets.boston_housing （train_x,train_y),（test_x,test_y)= boston_housing.load_data（test_split=0.1) print（"Training set:", len（train_x)) print（"Testing set:", len（test_x))

    A．Training set: 404 Testing set: 102

    B．Training set: 51 Testing set: 455

    C．Training set: 102 Testing set: 404

    D．Training set: 455 Testing set: 51


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