Innovaccer Data Engineer Interview Questions and Answers

Q1. Tell me about yourself, second highest salary , question related to coleasce function
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Aptitude, English and basic programming mcq question were there. Around 70 questions

Aptitude Test is to generally Test your speed and calculation skills and how your mind thinks and solves a question in minimum time. So, the test includes English, Apti, Computer Basic Fundamentals questions to test you all the way.

Q1. This round is to explain the kind of work you have done before and the skills you possess to contribute specifically upto company expectations and hold the necessary experience in skills required.
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Q1. This round is to discuss about yourself and also some technical aspects can also be discussed in the same (depends).
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Medium level apti and verbal.

Q1. Depend upon your resume + Project
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Q1. Project + Basic HR questions
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Q1. What are the differences between Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN)?
Ans. 
RNNs process sequential data, while CNNs excel in spatial data analysis, like images.
• RNNs are designed for sequential data, such as time series or text.

• CNNs are optimized for grid-like data, particularly images.

• RNNs use loops to maintain memory of previous inputs, making them suitable for tasks like language modeling.

• CNNs utilize convolutional layers to detect patterns and features in images, useful in image classifica...read more
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Q2. What is a neural network, how does it function, and what strategies can be employed to avoid overfitting in a neural network?
Ans. 
A neural network is a computational model inspired by the human brain, used for pattern recognition and data analysis.
• Neural networks consist of layers of interconnected nodes (neurons) that process input data.

• They function by adjusting weights through a process called backpropagation to minimize error.

• Common strategies to avoid overfitting include using dropout layers, which randomly deactivate neurons during training...read more
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Q3. How would you approach classification problems when dealing with skewed datasets?
Ans. 
Addressing skewed datasets in classification requires techniques like resampling, using appropriate metrics, and algorithm adjustments.
• Use resampling techniques: Oversample the minority class (e.g., SMOTE) or undersample the majority class.

• Implement cost-sensitive learning: Assign higher misclassification costs to the minority class to guide the model.

• Choose appropriate evaluation metrics: Use F1-score, precision-recal...read more
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Logical questions which are highly useful

Multiple coding questions they asked in pyspark

The group discussion (GD) round is expected to last 20 minutes. The topics were straightforward and easily comprehensible. The primary focus when participating in the GD should be on English fluency. It is not primarily about how content-rich or intellectually impressive your speech is, but rather about the level of fluency in communication.

Q1. Most of the questions will be based on our resume.
Ans. 
Data Engineer with expertise in ETL processes, data warehousing, and big data technologies.
• Proficient in SQL and NoSQL databases, such as PostgreSQL and MongoDB.

• Experience with ETL tools like Apache NiFi and Talend for data integration.

• Worked on data warehousing solutions using Amazon Redshift and Google BigQuery.

• Familiar with big data frameworks like Apache Hadoop and Spark for processing large datasets.

• Implemented da...read more
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