Zebra Technologies Business Intelligence Analyst Interview Questions and Answers

Q1. Difference between bagging and boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more
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Q2. Parameters of Decision Tree
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Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.
• Max depth: maximum depth of the tree

• Min samples split: minimum number of samples required to split an internal node

• Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

• Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')
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Q3. Explain any one of your project in detail
Ans. 
Developed a predictive model to forecast customer churn in a telecom company
• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall

• Provided actionable insights to the company to reduce customer churn rate
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*****, arjumpudi satyanarayana

Q1. What is the python language
Ans. 
Python is a high-level programming language known for its simplicity and readability.
• Python is widely used for web development, data analysis, artificial intelligence, and scientific computing.

• It emphasizes code readability and uses indentation for block delimiters.

• Python has a large standard library and a vibrant community of developers.

• Example: print('Hello, World!')

• Example: import pandas as pd
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Q2. What is the code problems
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Code problems refer to issues or errors in the code that need to be identified and fixed.
• Code problems can include syntax errors, logical errors, or performance issues.

• Examples of code problems include missing semicolons, incorrect variable assignments, or inefficient algorithms.

• Identifying and resolving code problems is a key skill for data scientists to ensure accurate and efficient data analysis.
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Q3. What is the python code
Ans. 
Python code is a programming language used for data analysis, machine learning, and scientific computing.
• Python code is written in a text editor or an integrated development environment (IDE)

• Python code is executed using a Python interpreter

• Python code can be used for data manipulation, visualization, and modeling
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Q4. What is the project
Ans. 
The project is a machine learning model to predict customer churn for a telecommunications company.
• Developing predictive models using machine learning algorithms

• Analyzing customer data to identify patterns and trends

• Evaluating model performance and making recommendations for reducing customer churn
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Q5. What is the lnderssip
Ans. 
The question seems to be incomplete or misspelled.
• It is possible that the interviewer made a mistake while asking the question.

• Ask for clarification or context to provide a relevant answer.
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Q1. Overfitting and Underfitting
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Q2. Find Nth-largest element
Ans. 
Find Nth-largest element in an array
• Sort the array in descending order

• Return the element at index N-1
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Q3. NLP Data preprocessing
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Q1. Salary Discussion
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Q2. Fitment discussion
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Q1. Tell me about yourself?
Ans. 
I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
• Background in statistics and machine learning

• Experience in solving complex problems using data-driven approaches

• Passionate about leveraging data to drive insights and decision-making
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Q2. Describe in detail about one of my main project.
Ans. 
Developed a predictive model for customer churn in a telecom company.
• Collected and cleaned customer data including usage patterns and demographics.

• Used machine learning algorithms such as logistic regression and random forest to build the model.

• Evaluated model performance using metrics like accuracy, precision, and recall.

• Implemented the model into the company's CRM system for real-time predictions.
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Q3. Few questions related to my projects.
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Q1. Questions on Basics python(Since i am fresher)
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Q1. Asked questions related to stats , Senario based. Interviewer was very nice
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Q2. Asked questions on Python Programming and asked for Programs Like How can update List , Asked to Build a Tough Dict , Class etc.
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Q1. Why u want to join?
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Q2. What is your future goal?
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Q1. Pandas basics and SQL joins nlp
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Basic DP, Array Questions

Q1. Resume Walkthrough and Discussion, Medium level coding questions
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Q1. Discussion with Manager
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Q1. Normal HR round
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Q1. How tfidf works in NLP
Ans. 
TF-IDF is a statistical measure used to evaluate the importance of a word in a document.
• TF-IDF stands for Term Frequency-Inverse Document Frequency

• It is used to weigh a word's importance in a document by considering its frequency in the document and across all documents

• The formula for TF-IDF is: TF-IDF = TF * IDF

• TF (Term Frequency) measures how frequently a term appears in a document

• IDF (Inverse Document Frequency) mea...read more
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Q2. What is the difference between group by and window function in sql
Ans. 
Group by is used to group data based on a column while window function is used to perform calculations on a specific window of data.
• Group by is used to aggregate data based on a specific column

• Window function is used to perform calculations on a specific window of data

• Group by is used with aggregate functions like sum, count, avg, etc.

• Window function is used with analytical functions like rank, lead, lag, etc.

• Group by ...read more
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Q1. Tell me about the project you worked on 4 years back
Ans. 
Developed a predictive model to forecast customer churn for a telecom company.
• Used machine learning algorithms like logistic regression and random forest.

• Preprocessed and cleaned the dataset by handling missing values and outliers.

• Performed feature engineering to create new variables for better model performance.

• Evaluated model performance using metrics like accuracy, precision, and recall.

• Implemented the model in prod
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Q2. Why are you leaving your current company
Ans. 
Seeking new challenges and opportunities for growth.
• Looking for a more challenging role that aligns with my career goals.

• Seeking a company that values innovation and encourages professional development.

• Want to work in a more collaborative and diverse team environment.

• Desire to explore new technologies and industries.

• Current company lacks opportunities for advancement or career growth.
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