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Big Data Engineer Assessment Test

This Big Data test evaluates candidates' understanding of data processing, storage, and analysis using big data technologies. It assesses their ability to work with large datasets, implement data pipelines, and utilize tools like Hadoop and Spark. Ideal for roles in data science and machine learning.

Proficiency Level
Beginner-Expert
Experience
0-8 years
Duration
30 mins
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Use Case

  • Assesses proficiency in handling supervised ML and regression models.
  • Evaluates knowledge of algorithm configurations and hypothesis testing.
  • Tests comprehension of SQL database management and Spark parallelism.
  • Identifies expertise in feature selection, overfitting reduction, and data modeling.

Skills Covered

Big Data
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About

Big Data Engineer Assessment Test

This Big Data test is designed to assess the foundational skills and knowledge of candidates in handling and processing large datasets using big data technologies. It evaluates their proficiency in implementing data pipelines, managing data storage solutions, and performing data analysis with tools such as Hadoop, Spark, and other relevant big data frameworks. The test aims to identify individuals who can effectively contribute to data-driven projects and support data science and machine learning initiatives. It is suitable for roles that require a strong understanding of big data concepts and the ability to apply them in real-world scenarios.

Target Audience

This assessment is ideal for roles such as Data Scientist, Machine Learning Engineer, Big Data Engineer, Data Analyst, and Business Intelligence Developer.

Prerequisites
  • Understanding of data processing and storage concepts
  • Familiarity with big data technologies like Hadoop and Spark
  • Basic knowledge of data analysis techniques
  • Experience with data pipeline implementation
  • Ability to work with large datasets
  • Understanding of distributed computing principles
  • Familiarity with data science and machine learning concepts
Test Overview
Duration
30 mins
Questions
10
Passing Score
70%

Questions

Create a data model with high success rate
Machine learning models
Machine learning models
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's ability to select the optimal approach for creating a data model with a high success rate in machine learning. It tests the understanding of predictor variables, feature selection, and model optimization.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Weight of Evidence in Big Data
Weight of Evidence
Weight of Evidence
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's understanding of Weight of Evidence (WOE) categories, specifically focusing on the criteria related to the minimum observations required in each category and the possibility of having zero observations for both non-events and events.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Reduce overfitting and feature selection
Lasso Regression
Lasso Regression
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's understanding of lasso regression, its role in reducing overfitting, and its impact on feature selection. It tests knowledge of coefficient constraints, magnitude shrinking, and zero coefficients in the context of regression analysis.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Regression in Big Data
Regression
Regression
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's understanding of heteroscedasticity in regression analysis and the factors that can cause it. It evaluates knowledge of data transformation, model specification, outlier detection, and distribution skewness.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Set the compression code in Big Data
Spark SQL
Spark SQL
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's understanding of setting compression codecs for Parquet files in Spark SQL. It tests the knowledge of precedence order for specifying compression options in table-specific properties.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Data modelling in Big Data
Data modelling
Data modelling
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's understanding of SQL database management, specifically the concept of Identity Property and its implications on table operations. It evaluates the ability to identify correct SQL statements for managing identity columns.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Level of parallelism
RDD
RDD
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's understanding of parallelism in Spark RDDs and the factors influencing the level of parallelism. It evaluates the knowledge of how the level of parallelism is determined when using groupByKey() on RDDs.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Hypothesis testing in Big Data
Hypothesis testing
Hypothesis testing
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's understanding of controlling the False discovery rate in hypothesis testing. It evaluates knowledge of statistical procedures, hypothesis testing, and the concept of False discovery rate.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Random Forest algorithm
Random Forest
Random Forest
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's knowledge of hyperparameters in the Random Forest algorithm by testing their ability to identify which parameter is not a hyperparameter. It evaluates understanding of Random Forest algorithm configuration and parameter selection.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Minimize the loss function
Linear Regression
Linear Regression
Big Data
Data Science
L2
MCQ
What this question evaluates
This question assesses the candidate's understanding of training supervised machine learning models, specifically linear regression. It evaluates the knowledge of minimizing loss functions using gradient descent or its variants.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
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Interactive coding environment with real-time feedback
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Proctoring & Anti-Cheating

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Live Monitoring

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Screen Tracking

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Pattern Analysis

Spot suspicious actions with AI-driven insights.

Access Control

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Real-time Monitoring
Video Feed
Active
Screen Activity
98%
Focus Rate
95%
Liam O'Connor
Candidate
Passed
85%
AI Summary
Skills Performance
Score
Big Data
87%
80%
85%
82%
Areas of Improvement
Review
Practice
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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60 credits / yr
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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.
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.