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Data Mining Test

Candidates Assessed

15502+

Organisations Served

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Our Data Mining Online Test helps recruiter & hiring managers to assess Data Mining skills of candidates effortlessly. The test evaluates candidates’ knowledge on Data Processing, Data Warehouse and OLAP Technology, Data Preprocessing, Mining Frequent Patterns, Data Cleaning, Data Reduction, Data Mining Process, Data Integration and Transformation. Online Data Mining knowledge test is useful for hiring job-fit Data Mining Analyst, Data Mining Specialist, Data Mining Consultant and Data Mining Expert. The test has proven to reduce time-to-hire by 45%. 

Data Mining Test

About Data Mining Test

Our Data Mining Online Test helps recruiters & hiring managers to effectively assess the skills of the Data Mining analyst before an interview. Data Mining is the computational process of discovering patterns in the form of large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems. This Data Mining Online Test is designed to check the development and programming skills of Data Mining Developer as per Industry Standards. 

 
This Data Mining knowledge test is designed & validated by our experienced Subject Matter Experts (SME) to evaluate the candidate’s knowledge about Data Mining basics before hiring. Using powerful reporting, you can have a detailed analysis of the test results to help you make a better hiring decision and predict the candidate’s performance.

Are you a jobseeker looking to sharpen your skills?

Test Summary

This Data Mining Interview Test enables employers and recruiters to identify potential data Mining Consultant & Specialist 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, rather than theoretical knowledge. 
 
Data Mining Assessment Test may contain MCQ's (Multiple Choice Questions), MAQ's (Multiple Answer Questions), Fill in the Blanks, Descriptive, Whiteboard Questions, Audio / Video Questions, LogicBox (AI-based Pseudo-Coding Platform), Coding Simulations, True or False Questions, etc. 

Test Duration: 20 minutes

No. of Questions: 10

Level of Expertise: Entry-level/Mid/Senior

Useful for hiring

  • Data Mining Analyst
  • Data Mining Specialist
  • Data Mining Consultant
  • Data Mining Expert

Topics Covered

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Data Processing

The pre-hire test assesses candidate's knowledge pf Data process to produce meaningful information  

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Data Warehouse and OLAP Technology

The test evaluates candidate’s understanding of Online Analytical Processing Server (OLAP). It allows managers and analysts to get an insight into information through fast, consistent, and interactive access to information 

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Data Preprocessing

iMocha’s skills test assesses candidates' skills of transforming raw data into an understandable format 

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Mining Frequent Patterns

The online technical skills test checks candidate’s ability to understand Association Rule Mining  

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Data Integration and Transformation

The online Data Mining test helps recruiters assess candidate’s skills of converting data from one format to another, typically from the format of a source system into the required format of a destination system 

Sample Questions

Choose from our 100,000+ question library or add your own questions to make powerful custom tests

Question types:

Multiple Option

Topic:

Data Validation

Difficulty:

Easy

Q 1. When applying for a credit card, candidates may be asked to provide their driver’s license number. Candidates who do not have a driver’s license may naturally leave this field blank. Forms should allow candidates to fill in the blank field. Which field is more likely suitable to the credit card analogy above?
"Not allowed"
"Not available"
"Not applicable"
"Not open"

Question types:

Multiple Option

Topic:

Regression

Difficulty:

Easy

Q 2. I have a set of data to analyse the impact of staff salary on retention with my model specified thus:
Y = a+bX
X = staff salary (the predictor variable)
Y = staff salary (the predictor variable)
a and b are coefficients.

How can this model be used to approximate the data?
Correlation and ANOVA
Regression and log-linear models
Scatterplot and Covariance
Descriptive Statistics

Question types:

Multiple Option

Topic:

Demographic Segmentation

Difficulty:

Easy

Q 3. A data mining system should be able to produce a description summarizing the characteristics of customers who spend more than $1,000 a year at Fara Consulting. The result could be a general profile of the customers, such as they are 40–50 years old, employed, and have excellent credit ratings. The system should allow the users to drill down on any dimension. Which one of the following responses is less likely?
Such as on location in order to view these customers according to their type of availability
Such as on occupation in order to view these customers according to their type of employment
Such as on address in order to view these customers according to their type of apartment
Such as an occupation in order to view these customers according to their gender

Sample Report

View Full Report . . .

Skill wise performance report by iMocha

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You can customize this test by

difficulty level
Setting difficulty level of test      

Choose easy, medium or hard questions from our skill libraries to assess candidates of different experience levels.                       

multiple skills
Combining multiple skills into one test

Add multiple skills in a single test to create an effective assessment. Assess multiple skills together.                                              

adding own skill
Adding your own questions to the test

Add, edit or bulk upload your own coding questions, MCQ, whiteboarding questions & more.                       

tailor made test
Requesting a tailor-made test                  

Get a tailored assessment created with the help of our subject matter experts to ensure effective screening.

Related Skills

Trusted By

Rehana Nisar, Global Product & Services Recruitment Head, Gartner
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“We realized that to acquire quality talent, our recruitment process was in dire need of automation. Too many of our technical team were simply wasting hours conducting interviews that did not yield the desired results. For us, the foremost criteria was to find a recruitment partner who could tick all the right boxes.”

Rehana Nisar, Global Product & Services Recruitment Head,

Gartner

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