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Data Wrangling Assessment Test

This Data Wrangling test evaluates candidates' proficiency in transforming raw data into a structured format for analysis. It covers essential skills such as feature engineering, data integration, and handling big data, ensuring candidates can efficiently manage and document data processes.

Proficiency Level
Beginner-Expert
Experience
0-8 years
Duration
60 mins
WeCP Verified
WeCP
Subject Matter Expert
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Use Case

  • Assesses data cleaning, transformation, integration, and extraction skills.
  • Evaluates candidates' problem-solving with algorithms and data structures.
  • Measures communication skills in explaining data processes effectively.
  • Identifies top talent through practical hands-on coding challenges.

Skills Covered

Data Cleaning
Data Transformation
Data Integration
Data Extraction
Feature Engineering
Data Quality Assessment
Handling Time-Series Data
+16 more
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About

Data Wrangling Assessment Test

This Data Wrangling test is designed to assess candidates' ability to transform raw data into a structured format suitable for analysis. It evaluates skills in feature engineering, data integration, and handling big data, ensuring candidates can efficiently manage and document data processes. The test also covers data governance and compliance, handling time-series data, and text data wrangling. Candidates will demonstrate their ability to explain complex data processes, utilize greedy algorithms, and automate data wrangling tasks. Additionally, the assessment includes data quality assessment, data cleaning, and version control and documentation, ensuring a comprehensive evaluation of data wrangling capabilities.

Target Audience

This assessment is ideal for roles such as Data Analysts, Data Engineers, Data Scientists, Business Analysts, and anyone involved in data management and analysis.

Prerequisites
  • Understanding of basic data structures and algorithms
  • Familiarity with data integration techniques
  • Knowledge of data governance and compliance standards
  • Experience with handling time-series and big data
  • Ability to document data wrangling steps
  • Proficiency in text data wrangling
  • Skills in data transformation and cleaning
  • Understanding of version control and documentation practices
Test Overview
Duration
60 mins
Questions
33
Passing Score
70%

Questions

Documenting Data Wrangling Processes
data-management
data-management
data-cleansing
analysis-tools
data-preprocessing
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Cleaning Process
Data Science
Data Science
Data Cleaning
Explanation
Python Libraries
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Array Query Operations
Algorithm Design
Algorithm Design
Array Manipulation
Handling Precision and Large Numbers
L2+
Mathematical Operations
What this question evaluates
This question tests the candidate's expertise in managing array data structures and applying mathematical operations on them. It involves understanding and applying distinct query types on the array while considering large numbers and handling precision with the use of modulo operations.
Type:
Programming
Difficulty:
Hard
Time:
40 mins
Attempts:
100+
Success Rate:
70.01%
Minimize The Maximum
Arithmetic Operations
Arithmetic Operations
L3
Set Manipulation
Set Operations
Transformation Operations
What this question evaluates
This question assesses the candidate's skills in working with sets, transformations, and finding the minimum maximum element. It evaluates understanding of operations involving multiplication and addition.
Type:
Programming
Difficulty:
Medium
Time:
45 mins
Attempts:
100+
Success Rate:
70.01%
Feature Engineering in Data Science
Feature Engineering
Feature Engineering
Data Science
Data Preparation
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Extraction from CSV using pandas in Python
Data Extraction
Data Extraction
Data Science
Python
pandas
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Integration Techniques for Consistency
Data Integration
Data Integration
Data Consistency
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Transformation using Python
Data Transformation
Data Transformation
Python
DateTime
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Missing Data in Age Column
Data Cleaning
Data Cleaning
R
Data Analysis
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Governance Principles for Compliance
Data Governance
Data Governance
Compliance
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Benefits of Git in Data Science
version control
version control
data science
Git
documentation
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Automating Data Wrangling Pipelines
Data Wrangling
Data Wrangling
Automation
Data Preparation
Python
R
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Big Data Techniques
Big Data
Big Data
Data Science
Data Processing
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Python Text Wrangling for Hiring
Python
Python
Text Data Wrangling
Pandas
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Missing Values in Time-Series Data
Data Science
Data Science
Python
Time-Series
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Quality Features
Data Quality
Data Quality
Data Science
Analytics
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Categorical Data in Feature Engineering
Feature Engineering
Feature Engineering
Data Science
Machine Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Extraction Using Python
Data Extraction
Data Extraction
Python
Web Scraping
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Integration for Data Science
Data Integration
Data Integration
Data Quality
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Transformation with Pandas
Data Science
Data Science
Data Transformation
Pandas
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Cleaning Practices
data cleaning
data cleaning
data science
data preparation
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Governance Practice for Access Control
Data Governance
Data Governance
Access Control
Data Compliance
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Git Version Control in Data Science Projects
version control
version control
data preprocessing
machine learning
git
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Advantages of Automating Data Wrangling
Data Wrangling
Data Wrangling
Automation
Data Analysis
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Identifying Python Libraries for Big Data
Handling Big Data
Handling Big Data
Python Libraries
Data Manipulation
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Filling Missing Values with Mean
Data Wrangling
Data Wrangling
Python
Pandas
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Missing Values in Time-Series Data
Data Science
Data Science
Python
Time-Series
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Quality Assessment: Consistency
Data Quality
Data Quality
Data Consistency
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding One-Hot Encoding in Feature Engineering
Feature Engineering
Feature Engineering
Machine Learning
Data Preprocessing
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Extraction Techniques
Data Science
Data Science
Data Extraction
Text Analysis
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Integration in Python
Python
Python
Data Integration
Libraries
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Transformation with Python
Data Transformation
Data Transformation
Python
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Cleaning with Python
Data Cleaning
Data Cleaning
Python
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Documenting Data Wrangling Processes
data-management
data-management
data-cleansing
analysis-tools
data-preprocessing
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Cleaning Process
Data Science
Data Science
Data Cleaning
Explanation
Python Libraries
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Array Query Operations
Algorithm Design
Algorithm Design
Array Manipulation
Handling Precision and Large Numbers
L2+
Mathematical Operations
What this question evaluates
This question tests the candidate's expertise in managing array data structures and applying mathematical operations on them. It involves understanding and applying distinct query types on the array while considering large numbers and handling precision with the use of modulo operations.
Type:
Programming
Difficulty:
Hard
Time:
40 mins
Attempts:
100+
Success Rate:
70.01%
Minimize The Maximum
Arithmetic Operations
Arithmetic Operations
L3
Set Manipulation
Set Operations
Transformation Operations
What this question evaluates
This question assesses the candidate's skills in working with sets, transformations, and finding the minimum maximum element. It evaluates understanding of operations involving multiplication and addition.
Type:
Programming
Difficulty:
Medium
Time:
45 mins
Attempts:
100+
Success Rate:
70.01%
Feature Engineering in Data Science
Feature Engineering
Feature Engineering
Data Science
Data Preparation
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Extraction from CSV using pandas in Python
Data Extraction
Data Extraction
Data Science
Python
pandas
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Integration Techniques for Consistency
Data Integration
Data Integration
Data Consistency
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Transformation using Python
Data Transformation
Data Transformation
Python
DateTime
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Missing Data in Age Column
Data Cleaning
Data Cleaning
R
Data Analysis
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Governance Principles for Compliance
Data Governance
Data Governance
Compliance
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Benefits of Git in Data Science
version control
version control
data science
Git
documentation
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Automating Data Wrangling Pipelines
Data Wrangling
Data Wrangling
Automation
Data Preparation
Python
R
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Big Data Techniques
Big Data
Big Data
Data Science
Data Processing
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Python Text Wrangling for Hiring
Python
Python
Text Data Wrangling
Pandas
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Missing Values in Time-Series Data
Time-Series
Time-Series
Data Cleaning
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Quality Features
Data Quality
Data Quality
Data Science
Analytics
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Categorical Data in Feature Engineering
Feature Engineering
Feature Engineering
Data Science
Machine Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Extraction Using Python
Data Extraction
Data Extraction
Python
Web Scraping
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Integration for Data Science
Data Integration
Data Integration
Data Quality
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Transformation with Pandas
Data Science
Data Science
Data Transformation
Pandas
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Cleaning Practices
data cleaning
data cleaning
data science
data preparation
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Governance Practice for Access Control
Data Governance
Data Governance
Access Control
Data Compliance
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Git Version Control in Data Science Projects
version control
version control
data preprocessing
machine learning
git
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Advantages of Automating Data Wrangling
Data Wrangling
Data Wrangling
Automation
Data Analysis
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Identifying Python Libraries for Big Data
Handling Big Data
Handling Big Data
Python Libraries
Data Manipulation
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Filling Missing Values with Mean
Data Wrangling
Data Wrangling
Python
Pandas
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Handling Missing Values in Time-Series Data
Data Science
Data Science
Python
Time-Series
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Quality Assessment: Consistency
Data Quality
Data Quality
Data Consistency
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding One-Hot Encoding in Feature Engineering
Feature Engineering
Feature Engineering
Machine Learning
Data Preprocessing
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Extraction Techniques
Data Science
Data Science
Data Extraction
Text Analysis
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Integration in Python
Python
Python
Data Integration
Libraries
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Transformation with Python
Data Transformation
Data Transformation
Python
Data Cleaning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Data Cleaning with Python
Data Cleaning
Data Cleaning
Python
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
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Candidate Experience

Interactive coding environment with real-time feedback
Clear instructions and test cases for each question
Built-in code editor with syntax highlighting
Immediate evaluation of submissions
Progress tracking throughout the assessment
Detailed explanations for correct answers
Time management tools to help pace yourself

Proctoring & Anti-Cheating

Sherlock AI Agent

Sherlock is more than just a tool, it's your AI test integrity agent. By continuously monitoring and analyzing candidate behavior in real-time, Sherlock ensures a secure and fair testing environment. Using machine learning, it detects suspicious patterns, so you can focus on reliable results while Sherlock handles test integrity.
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Live Monitoring

Track behavior with real-time video and audio.

Screen Tracking

Multi-screen detection and continuous screen recording during assessment.

Pattern Analysis

Spot suspicious actions with AI-driven insights.

Access Control

Ensure secure tests with browser lockdown.
Real-time Monitoring
Video Feed
Active
Screen Activity
98%
Focus Rate
95%
Leila Cohen
Candidate
Passed
85%
AI Summary
Skills Performance
Score
Data Cleaning
87%
Data Transformation
80%
Data Integration
85%
Data Extraction
82%
Areas of Improvement
Review
Data Extraction
Practice
Data Transformation
Skill Assessment
Detailed evaluation of technical skills and problem-solving abilities.
AI Analysis
Machine learning-powered insights into candidate performance patterns.
Benchmarking
Compare results against industry standards and other candidates.
Action Items
Specific recommendations for skill development and improvement.

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Conduct face to face interviews

Conduct face to face interviews

Access to Objective Way of Assessments - MCQs, Checboxes, Fill in the blanks, Subjective (Manually Scored), File Upload (Manually Scored)

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ATS Integrations

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Access to Practice Way of Assessments - Programming, Debugging, Database, Data Science,

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Standard compliance, security and audits

Standard compliance, security and audits

Access to Real-world, work like Assessments - Projects, DevOps, Machine Learning, Subjective (AI Evaluated), Video (Technical Knowledge), Video (Communication Skill), Whiteboarding (AI Evaluated)

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Add-on credit at $8 /credit

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Data Encryption and Secure Transmission: Ensures that the data is securely stored and transmitted, making it hard for hackers to tamper with the results.

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Comprehensive Assessment Capabilities

Comprehensive Assessment Capabilities

Compliance with Legal Standards: Make sure the tool complies with privacy and data protection laws relevant to your location, so that the data captured during proctoring is legally compliant.

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Advance Skill Analytics

Advance Skill Analytics

User Behavior Analytics Report: Monitors and analyzes mouse movements, keystroke dynamics, and other behaviors to identify anomalies.

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Learning & Development Integration

Learning & Development Integration

Multi-Factor Authentication: Requires multiple forms of verification before granting access to the assessment.

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Employee Friendly User Experience

Employee Friendly User Experience

User-Friendly Interface: A clean, intuitive user interface ensures that candidates can focus on the assessment rather than figuring out how to navigate the platform.

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Retention-Focused Features

Retention-Focused Features

Mobile Compatibility: The platform should be responsive and accessible from various devices, including smartphones and tablets, so candidates can take assessments at their convenience.

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Advance compliance, security and audits

Advance compliance, security and audits

Clear Instructions: The tool should offer straightforward guidelines before each test or section to ensure that candidates understand what's expected of them.

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Proactive support from WeCP Team

Proactive support from WeCP Team

Time Tracking: A visible yet non-intrusive timer can help candidates manage their time effectively during the test.

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Access to WeCP AI Copilot to save cost, time and improve outcomes

Access to Ada (AI Copilot) to save cost, time and improve outcomes

Save and Resume: The ability for candidates to save their progress and come back to finish an assessment can reduce stress and improve the overall experience.

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Enterprise
Talk to us for a comprehensive solution that meets all your enterprise needs.
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Are you an Enterprise?
Talk to us for a comprehensive solution that meets all your enterprise needs.
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Talk to us for a comprehensive solution that meets all your enterprise needs
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Effortless Data Migration: Our support team ensures a smooth transition, keeping your assessments and data intact.
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AI-Assisted Onboarding: WeCP AI Copilot, guides your team through features, making onboarding quick and simple.
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Easy Change Management: Comprehensive training and 24/7 support ensure a seamless switch with minimal operational impact.
Top Recognised Skill Assessment and Interviewing Software
Trusted by 850+ companies in 20+ countries
New: Add-on
Advanced AI
US$ 50per agent/month
Enterprise-grade AI instantly understands common customer issues for your industry, routes tickets and gives agents extra insights. In any plan, Advanced AI is available at the Professional and higher levels.
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Ticketing system
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Ticketing system
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Ticketing system
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Ticketing system
Talk To Sales
New: Add-on
Advanced AI
US$ 50per agent/month
Enterprise-grade AI instantly understands common customer issues for your industry, routes tickets and gives agents extra insights. In any plan, Advanced AI is available at the Professional and higher levels.
check icon
Ticketing system
check icon
Ticketing system
check icon
Ticketing system
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Ticketing system
Talk To Sales

What Our Customers Say

"We have evaluated more than hundreds of thousands of techies using WeCP until now. The tool has been very effective in assessing strengths and weaknesses of candidates."
Allahbaksh Asadullah
Principal Product Architect, Infosys
"With WeCP's automation capabilities, we've not only streamlined the process but also enhanced the candidate experience."
Paula Macnab
Hiring Manager, Yellow
"Comprehensive reports and analytics help us make data-driven hiring decisions. The platform has streamlined our entire process."
Erich Raldmann
Managing Partner, Spherion

Frequently Asked Questions

How does AI proctoring work?
Our AI proctoring system, Sherlock, uses advanced machine learning algorithms to monitor candidate behavior in real-time. It analyzes video, audio, and screen activity to detect potential cheating attempts while maintaining candidate privacy.
Can I customise assessment templates?
Yes! All plans include access to our template library, and you can customize them to match your specific requirements. Professional and Enterprise plans offer additional customization options and the ability to create custom templates from scratch.
What type of reports are available?
We provide comprehensive reports including detailed skill assessments, AI-powered behavioral analysis, comparative analytics, and improvement recommendations. Reports can be customized and exported in various formats.
Is the platform suitable for remote hiring?
Absolutely! Our platform is specifically designed for remote hiring with features like secure browser lockdown, AI proctoring, and real-time monitoring. It ensures the same level of assessment integrity as in-person evaluations.
How do you ensure security of assessment data?
We implement enterprise-grade security measures including end-to-end encryption, secure data storage, and regular security audits. We comply with GDPR and other major data protection regulations.