NTT Data Business Intelligence Analyst Interview Questions and Answers

Gangster Disciples (GD) is a gang, while Graphical Display (GD) refers to a visual representation of data.

Aptitude tests are used to assess a person's ability to perform specific tasks, think critically, and solve problems. They are commonly used in job recruitments, college admissions, and competitive exams.

### **Types of Aptitude Tests**
1. **Numerical Aptitude** – Assesses mathematical skills, including arithmetic, algebra, ratios, percentages, and data interpretation.
2. **Logical Reasoning** – Evaluates pattern recognition, sequences, and logical deductions.
3. **Verbal Ability** – Tests grammar, comprehension, vocabulary, and sentence formation.
4. **Abstract Reasoning** – Measures the ability to identify patterns, trends, and relationships among shapes or figures.
5. **Spatial Reasoning** – Tests the ability to visualize and manipulate objects in space.
6. **Mechanical Reasoning** – Assesses understanding of mechanical and physical concepts, often for technical roles.
7. **Situational Judgment Test (SJT)** – Evaluates decision-making and problem-solving in workplace scenarios.

### **Common Exam Patterns**
- Multiple-choice questions (MCQs)
- Timed sections
- Increasing difficulty level
- Negative marking (in some tests)

### **Preparation Tips**
✔️ **Practice Regularly** – Solve sample questions and previous papers.
✔️ **Time Management** – Work on speed and accuracy.
✔️ **Learn Shortcuts** – Use mental math tricks and reasoning techniques.
✔️ **Improve Reading Skills** – Enhance vocabulary and comprehension for verbal sections.
✔️ **Use Online Mock Tests** – Simulate real test conditions.

Would you like sample questions or specific test details for a job or exam?

Q1. Sql queries pandas coding
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Q2. Some questions related to power bi
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Q1. Salary discussion and feedback
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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
Ans. 
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
Ans. 
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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NER training using deep learning

Q1. Describe the approach taken for assignment
Ans. 
I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
• Break down the assignment into smaller tasks to make it more manageable

• Set deadlines for each task to stay on track

• Regularly check progress to ensure everything is on schedule

• Seek feedback from colleagues or supervisors to improve the quality of work
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Q2. Scenario based questions
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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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They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
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Q2. Experience in cloud technologies
Ans. 
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
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Q1. Tell me about yourself
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Q2. Project details and challenges faced in the project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
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