MindEra Data Analyst Interview Questions and Answers

Q1. Asked questions related to data analytics , prepare for SQL, dash boarding, and visualization based questions
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Q1. Its a general discussion to check fit for the company culture and environment
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Q1. General salary negotiation
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Q1. About ourselves
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Q2. Professional experience
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Q3. Salary expectation
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Q4. Notice period, if experienced
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Q1. Normal question about past experience
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Q2. When you can join and all
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SQL test on Select query, insert, create

Q1. They don’t take interview and they just ask money for for entering and never provide you job
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Q2. They just fooling people
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Q3. They are fake and unprofessional
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Q4. They provide my salary and then I ended my month and they did not gave me my salary
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Q5. Dynpro is fake company who provide the fake job and salary slips
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Q1. Communication and behavioral skills
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Q2. Introduction, Skill set, Relevant experience
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Q1. AWS - EC2, Lambda, VPC, Route53, Cognito, Cloudwatch, Kinesis, API Gateway
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Q2. ITIL - problem management, recently handled outages. SLA vs SLO. KPIs
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Q1. DevOps - SDLC explanation, Apache Tomcat, Linux Administration, Docker and Shell scripting
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Q2. Git, Python, Monitoring tools
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Q1. Salary expectations and other benefits discussion
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Q1. Salary Negotiation and salary structure explanation
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Q1. Database Design was asked with
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Q2. System design networking architecture data pipeline
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Q1. Self introduction
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Q2. Traffic rules
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Forecasting problem - Predict daily sku level sales

Q1. What is difference between bias and variance
Ans. 
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is the error introduced by approximating a real-world problem, leading to underfitting.

• Variance is the error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to ...read more
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Q2. Parametric vs non parametruc model
Ans. 
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
• Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.

• Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.

• Examples of parametric models inc...read more
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Q1. Basic ios Questions No DS questions
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Q2. Basic swift quesions
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Q3. In App Purchase
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Q4. Hardware Device connectivity
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