Clock
Test duration:
20
min
Notes
No. of questions:
10
Tie
Level of experience:
Entry-level/Mid/Senior

Alteryx Skill Test

Our Data Science Alteryx test is the ideal pre-employment test for recruiters and hiring managers to hire job-fit candidates for roles such as Data Science Developer - Alteryx, Alteryx Developer, Data Analyst, and Alteryx Consultant. The Alteryx test can reduce hiring time by 40% and improve the interview-to-selection ratio by 62% by filtering out irrelevant candidates.

11345
+
Candidates
Assessed
125
+
Organizations Served
Fujitsu
Paypal
Capgemini
Deloitte
Coupa
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Vanguard
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About

Alteryx Skill Test

Alteryx is widely used in data science and self-service analytics with a platform that can prep, blend, enrich and analyze data and manage and deploy the data. Data analysts like to use Alteryx for its ability to quickly blend complex data and create intuitive workflows in a much easier way. Alteryx designers help to access data from any data source like a data warehouse, ERP and cloud-based application, and Hadoop data stores for further data analysis. Our Data Science Alteryx Test helps recruiters & hiring managers to effortlessly assess a candidate’s ability to work on the Alteryx platform. The alteryx test is designed by experienced Subject Matter Experts (SME) to evaluate and hire Alteryx developers/professionals as per the industry standards. Why use iMocha’s Alteryx test? You can reduce candidate filtration time by up to 85% with our Data science Alteryx test. The test can be used to assess candidates with different skill sets. Depending on your job requirement, you can use an existing set or request us to make a custom-made skill set.

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How it works

Test Summary

Alteryx test helps to screen the candidates who possess traits as follows:   

  

  • Good knowledge and experience working on Alteryx analytics software 
  • Excellent data analysis skills in identifying different trends, patterns, and other data issues within the dataset with Alteryx designer 
  • Familiarity with dataset types in Alteryx 
  • Ability to develop different Alteryx workflows 
  • Ability to troubleshoot and develop controls to support accurate execution 

Candidates can take this Data science Alteryx test from anywhere in the world from the comfort of their home and per their time zone. This test is designed considering EEOC guidelines. It will help you to assess & hire diverse talent without any bias.


Data science Alteryx test may contain MCQs (Multiple Choice Questions), MCQs (Multiple Answer Questions), Fill in the Blanks, Whiteboard Questions, Audio / Video Questions, AI-LogicBox (Pseudo-Coding Platform), Coding Simulators, True or False Questions, etc.

Test Duration
20
No. of Questions
10
Level of Expertise
Entry-level/Mid/Senior
Useful for hiring
  • Data Analyst
  • Data Science Developer - Alteryx
  • Alteryx Developer
  • Alteryx Consultant
Topics Covered
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Alteryx Tools

Our Data Science Alteryx test analyzes candidates’ knowledge of using Alteryx tools to cleanse, blend, and analyze modern data types.

Workflow Testing

The Alteryx test checks the candidate’s ability to mimic the production environment into the testing phase so that it can be tested from an end user’s perspective.
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Managing Data Inputs

The Alteryx test will allow you to assess a candidate’s competency to manage data such as prep, blend, enrich, analyze, manage and deploy
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Dataset types in Alteryx

Our online technical screening platform gauges candidate’s knowledge of Alteryx Dataset such as string, numeric, date-time, and Boolean data types
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Alteryx and SQL database connection

The Alteryx skills test also evaluates candidates based on their understanding of SQL & Alteryx connections
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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 Answer

Topic:

Alteryx Syntax Knowledge

Difficulty:

Mediam

Question:

Imagine you need to write an IF statement in Alteryx. The IF statement should add “High” label for Sales bigger than 1000 and “Low” for lower than that. Which of the statements will do this?

Note: There can be multiple correct answers to this question.


Options

  • IF [Sales]&gt;1000 THEN &ldquo;Low&rdquo; ELSE &ldquo;HIGH&rdquo; ENDIF<br />&nbsp;
  • IF [Sales]&lt;1000 THEN &ldquo;Low&rdquo; ELSE &ldquo;HIGH&rdquo; ENDIF<br />&nbsp;
  • IIF([Sales]&lt;1000, &ldquo;High&rdquo;, &ldquo;Low&rdquo;)<br />&nbsp;
  • IIF([Sales]&lt;1000, &ldquo;Low&rdquo;, &ldquo;High&rdquo;)<br />&nbsp;
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