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Avaliação do Teste de Aptidão em Vendas
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Test duration:
30
min
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No. of questions:
30
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Level of experience:

Teste de Aptidão em Vendas

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Avaliação do Teste de Aptidão em Vendas

Um vendedor requer múltiplas habilidades para ter sucesso no trabalho e muitas vezes ele (a) não tem ensino superior. 

HABILIDADES-CHAVE NECESSÁRIAS:

 

COMPREENSÃO [LEITURA E ORAL] – Habilidade Verbal e Raciocínio Verbal

BOA COMUNICAÇÃO - Habilidade Verbal e Raciocínio Verbal

DEVE SABER DADOS – Raciocínio Numérico

TEM HABILIDADE PARA COMPREENDER A OFERTA DE PRODUTOS DA PRÓPRIA EMPRESA E DIFERENCIAÇÃO DO PRODUTO DOS COMPETIDORES- Raciocínio Verbal e Raciocínio Lógico

ARTICULAR BASTANTE PARA CONVENCER COMPRADORES DE VALOR EM PRODUTO/SERVIÇO VENDIDO - Raciocínio Lógico

TEM HABILIDADE PARA PENSAR CONSTRUTIVAMENTE E CRIAR ESTRATÉGIAS – Raciocínio Abstrato

TEM HABILIDADE PARA RESOLVER PROBLEMAS – Aptidão Quantitativa

DESCRIÇÃO DO TESTE

CAPACIDADE VERBAL 

A capacidade verbal é essencial porque a gramática e o vocabulário são um pré-requisito para falar e escrever bem em inglês

RACIOCÍNIO VERBAL

Compreensão de leitura indica uma capacidade de compreender e raciocinar com os conceitos em uma página escrita.

APTIDÃO QUANTITATIVA 

Aptidão quantitativa irá indicar pensamento analítico e capacidade de resolver problemas.

RACIOCÍNIO LÓGICO

O raciocínio lógico é avaliado através de:  

•    Declaração/Conclusão

•    Causa/Efeito e 

•    perguntas de Declaração/Fato

Estas perguntas indicarão a qualidade do raciocínio dedutivo e do raciocínio indutivo que contribuem para a capacidade do pensamento crítico

RACIOCÍNIO ABSTRATO

O Raciocínio Abstrato indicará a capacidade de pensar claramente, identificar padrões lógicos, aprender novas habilidades e resolver problemas complexos. É um indicador da inteligência fluida e mostra uma habilidade de criar estratégias.

RACIOCÍNIO NUMÉRICO

O teste de raciocínio numérico mede a capacidade de interpretar, analisar e tirar conclusões lógicas com base em dados numéricos apresentados em gráficos e tabelas que descrevem diferentes situações relacionadas a negócios.

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Test Summary

<p><strong>Detalhes do Teste:&nbsp;25 perguntas 50 minutos</strong><br />

&nbsp;</p> <ul> <li> <p><strong>Racioc&iacute;nio num&eacute;rico: 5 perguntas 10 minutos</strong></p> </li> <li> <p><strong>Racioc&iacute;nio L&oacute;gico: 10 perguntas 20 minutos</strong></p> </li> <li> <p><strong>Habilidade verbal: 10 perguntas 20 minutos</strong></p> </li>

</ul> <p data-fulltext="" data-placeholder="Translation" dir="ltr">&nbsp;</p>

&nbsp;<br />

This Google AdWords test enables employers and recruiters to identify &amp; hire PPC experts by evaluating working skills and job readiness. For this reason, the emphasis is laid upon evaluating the knowledge of applied skills gained through real work experience.&nbsp;</p> <p paraeid="{aaf82d5a-4b83-4f4d-9adb-3e3b6bf633bf}{68}" paraid="1533027840">The Google AdWords assessment may contain MCQs (Multiple Choice Questions), MAQs (Multiple Answer Questions), Fill in the Blanks, Whiteboard Questions, Audio/Video Questions, AI-LogicBox&nbsp;(AI-based essay evaluation), Job-based Simulations, True or False Questions, etc.&nbsp;&nbsp;&nbsp;&nbsp;</p> <p paraeid="{aaf82d5a-4b83-4f4d-9adb-3e3b6bf633bf}{72}" paraid="365379863">&nbsp;</p>

Test Duration
30
No. of Questions
30
Level of Expertise
Useful for hiring
  • Executivo de Vendas
  • Telemarketing
Topics Covered
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Sample Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.

Question types :

Multiple Option

Topic:

Difficulty:

Hard


Q 1. You have been asked to train « Naive Bayes model for spam detection dataset

Using Python command, what wil you add in the given code at XXXX to deciore, it and train your modal for the given dataset?

import numpy os np

X= nporroyll 3. 2b[1 21 [2.21.[3,21)
Y= nporray(fi.a,a.1))

XXXX

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

Question types :

Multiple Option

Topic:

Machine teaming

Difficulty:

Hard


Q 1. Q2 Lets assume that you have 6 Pandas DataFrome(af) in the below tole.

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074

When the following commend is executed, what hoppens to the DatoFrome?

df= defiinat()
Y= nporray(fi.a,a.1))

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
it gives an error becouse of the third row which has on empty string.
None of these

Question types :

Multiple Option

Topic:

Difficulty:

Easy

Question:

Número de diferentes tipos de baterias vendidas por uma empresa ao longo dos anos (números em milhares).

Types of batteries sold by a company
Em que ano a percentagem de baterias 4AH foi maior do que o total de baterias vendidas?

Options
  • 1994
  • 1995
  • 1996
  • 1997

<div>N&uacute;mero de diferentes tipos de baterias vendidas por uma empresa ao longo dos anos (n&uacute;meros em milhares).<br />

<br />

<img alt="Types of batteries sold by a company" src="https://imtcdn.azureedge.net/636274189457730936_374.jpg" style="max-width:420px;" /><br />

Em que ano a percentagem de baterias 4AH foi maior do que o total de baterias vendidas?</div><br /><div><div>Options</div><ul><li>1994</li><li>1995</li><li>1996</li><li>1997</li></ul></div>

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