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Supervised Learning Assessment Test

This Supervised Learning test evaluates candidates' understanding of key concepts such as Greedy Algorithm, Supervised Learning Algorithms, Hyperparameter Tuning, Dynamic Programming, Model Evaluation and Selection, Machine Learning Basics, Feature Engineering and Selection, and Model Interpretability.

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

  • Assesses knowledge of machine learning tasks and algorithms.
  • Evaluates candidate's model evaluation and selection skills.
  • Tests hands-on proficiency in algorithmic problem-solving.
  • Identifies communication skills through video explanation of biases.

Skills Covered

Machine Learning Basics
Supervised Learning Algorithms
Model Evaluation and Selection
Feature Engineering and Selection
Hyperparameter Tuning
Model Interpretability
Greedy Algorithm
+1 more
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About

Supervised Learning Assessment Test

This Supervised Learning test is designed to assess candidates' proficiency in essential machine learning concepts, including Greedy Algorithm, Supervised Learning Algorithms, Hyperparameter Tuning, Dynamic Programming, Model Evaluation and Selection, Machine Learning Basics, Feature Engineering and Selection, and Model Interpretability. It aims to evaluate the ability to apply these concepts in practical scenarios, ensuring a comprehensive understanding of supervised learning techniques. The test is ideal for identifying candidates who can effectively design, implement, and optimize machine learning models, making it a valuable tool for hiring managers seeking skilled professionals in the field.

Target Audience

This assessment is suitable for roles such as Machine Learning Engineer and AI/ML Researcher, providing a robust evaluation of candidates' capabilities in supervised learning.

Prerequisites
  • Strong understanding of basic machine learning concepts
  • Familiarity with various supervised learning algorithms
  • Knowledge of hyperparameter tuning techniques
  • Experience with greedy algorithms and dynamic programming
  • Ability to evaluate and select appropriate models
  • Skills in feature engineering and selection
  • Understanding of model interpretability and its importance
Test Overview
Duration
60 mins
Questions
9
Passing Score
70%

Questions

Propose Metric Biases Explanation
Model Interpretability
Model Interpretability
SHAP
Machine Learning
Supervised Learning
Bias Analysis
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Beautiful Subsets
Concatenation
Concatenation
L3
Modular Arithmetic
Pair Comparisons
Problem Solving
What this question evaluates
This question assesses the candidate's skill in working with sub-arrays, subset selection, concatenation, and understanding of pair comparisons. It also evaluates the candidate's ability in modular arithmetic and calculating total pairs.
Type:
Programming
Difficulty:
Hard
Time:
45 mins
Attempts:
100+
Success Rate:
70.01%
Equalize half of the elements
Conditional Analysis
Conditional Analysis
Integer Variables Manipulation
Optimization Strategies
Problem Solving
What this question evaluates
This question assesses the candidate's ability to manipulate integer variables, analyze conditions, and minimize costs in a given scenario involving changing values based on a status variable.
Type:
Programming
Difficulty:
Medium
Time:
45 mins
Attempts:
100+
Success Rate:
70.01%
Interpreting Black Box Models
Model Interpretability
Model Interpretability
Machine Learning
Supervised Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Hyperparameter Tuning in Machine Learning
Machine Learning
Machine Learning
Hyperparameter Tuning
Optimization
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding PCA in Feature Engineering
Feature Engineering
Feature Engineering
PCA
Machine Learning
Dimensionality Reduction
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Evaluating Model's Generalization Performance
machine learning
machine learning
model evaluation
generalization
limited data
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Objective of Training in Supervised Learning
Supervised Learning
Supervised Learning
Machine Learning
Model Training
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Identifying Suitable Machine Learning Task
Machine Learning Basics
Machine Learning Basics
Supervised Learning
Hiring
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Easy
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Propose Metric Biases Explanation
Model Interpretability
Model Interpretability
SHAP
Machine Learning
Supervised Learning
Bias Analysis
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Beautiful Subsets
Concatenation
Concatenation
L3
Modular Arithmetic
Pair Comparisons
Problem Solving
What this question evaluates
This question assesses the candidate's skill in working with sub-arrays, subset selection, concatenation, and understanding of pair comparisons. It also evaluates the candidate's ability in modular arithmetic and calculating total pairs.
Type:
Programming
Difficulty:
Hard
Time:
45 mins
Attempts:
100+
Success Rate:
70.01%
Equalize half of the elements
Conditional Analysis
Conditional Analysis
Integer Variables Manipulation
Optimization Strategies
Problem Solving
What this question evaluates
This question assesses the candidate's ability to manipulate integer variables, analyze conditions, and minimize costs in a given scenario involving changing values based on a status variable.
Type:
Programming
Difficulty:
Medium
Time:
45 mins
Attempts:
100+
Success Rate:
70.01%
Interpreting Black Box Models
Model Interpretability
Model Interpretability
Machine Learning
Supervised Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Hyperparameter Tuning in Machine Learning
Machine Learning
Machine Learning
Hyperparameter Tuning
Optimization
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding PCA in Feature Engineering
Feature Engineering
Feature Engineering
PCA
Machine Learning
Dimensionality Reduction
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Evaluating Model's Generalization Performance
machine learning
machine learning
model evaluation
generalization
limited data
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Objective of Training in Supervised Learning
Supervised Learning
Supervised Learning
Machine Learning
Model Training
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Identifying Suitable Machine Learning Task
Machine Learning Basics
Machine Learning Basics
Supervised Learning
Hiring
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Easy
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
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Question Types

Choose from multiple formats including MCQs, coding challenges, and system design questions.

Scoring Rules

Define custom scoring algorithms and weightage for different question types.

Time Settings

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Custom Branding

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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.
Learn how it works  

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%
Hiroshi Tanaka
Candidate
Passed
85%
AI Summary
Skills Performance
Score
Machine Learning Basics
87%
Supervised Learning Algorithms
80%
Model Evaluation and Selection
85%
Feature Engineering and Selection
82%
Areas of Improvement
Review
Feature Engineering and Selection
Practice
Supervised Learning Algorithms
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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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.
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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.
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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.