EXL Service Interview Questions and Answers

Q1. What are joins in explain types with examples
Ans. 
Joins are used to combine rows from two or more tables based on a related column between them.
• Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

• INNER JOIN returns rows when there is at least one match in both tables

• LEFT JOIN returns all rows from the left table and the matched rows from the right table

• RIGHT JOIN returns all rows from the right table and the matched rows from the left table

• FULL JOIN...read more
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Q2. What is the diff between All and Allexcept
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All returns all values in a table, while Allexcept returns all values except those specified in the argument.
• All returns all values in a column or table.

• Allexcept returns all values in a column or table except those specified in the argument.

• Example: All(Table[Column]) returns all values in the specified column.

• Example: Allexcept(Table, Table[Column]) returns all values in the table except those in the specified column...read more
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Q1. What are differences between Append and merge
Ans. 
Append adds new data to the end of a dataset, while merge combines two datasets based on a common key.
• Append adds rows to the end of a dataset, while merge combines rows from two datasets based on a common key column.

• Append is useful for adding new observations to an existing dataset, while merge is used to combine datasets with related information.

• Append does not require a common key, while merge requires a common key...read more
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Q2. What are Data Gateways
Ans. 
Data Gateways are software or hardware solutions that enable secure and efficient transfer of data between different systems or networks.
• Data Gateways act as intermediaries between data sources and data destinations

• They help in translating data formats, protocols, and security measures to ensure smooth data transfer

• Examples include Amazon API Gateway, Microsoft Azure Data Gateway, and IBM DataPower Gateway
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Q3. What are Dynamic Row filter
Ans. 
Dynamic Row filter is a feature that allows users to filter rows in a table based on certain criteria that can change dynamically.
• Dynamic Row filter allows users to specify conditions for filtering rows in a table.

• Users can set up dynamic filters that change based on user input or other factors.

• For example, a dynamic row filter could be set up to only show rows where the value in a certain column is greater than a user...read more
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Q1. Scenario based question
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Q2. Personality Questions
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Q1. Introduce yourself
Ans. 
Data scientist with a strong background in statistics, machine learning, and data visualization, passionate about solving complex problems.
• Educational Background: Master's degree in Data Science from XYZ University.

• Technical Skills: Proficient in Python, R, SQL, and machine learning libraries like TensorFlow and Scikit-learn.

• Professional Experience: Worked at ABC Corp, where I developed predictive models that improved ...read more
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Q2. Tell me about RAG project which you have mentioned in your resume.
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Q3. For what purpose are you using langchain in your RAG project?
Ans. 
LangChain enhances RAG projects by streamlining data retrieval and processing for improved AI model performance.
• Facilitates integration of various data sources, such as APIs and databases, for seamless information retrieval.

• Enables efficient document processing and indexing, allowing for quick access to relevant data.

• Supports the creation of custom pipelines for data transformation, enhancing the quality of input for A...read more
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Q4. Are you having knowledge of langgraph or other library for AI agents?
Ans. 
LangGraph is a library for building AI agents that can understand and generate natural language.
• LangGraph allows for the creation of conversational agents that can engage in dialogue.

• It supports various natural language processing tasks, such as sentiment analysis and text summarization.

• Example: Using LangGraph to build a chatbot that can answer customer queries in real-time.

• The library integrates with machine learning...read more
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Q5. Can you tell me about models you have used for computer vision project?
Ans. 
I have utilized various models like CNNs, transfer learning, and segmentation techniques for diverse computer vision projects.
• Convolutional Neural Networks (CNNs) for image classification tasks, e.g., classifying images of animals.

• Transfer learning with pre-trained models like VGG16 and ResNet for fine-tuning on specific datasets, such as facial recognition.

• Object detection using YOLO (You Only Look Once) for real-time...read more
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Q6. What about deployment of Machine Learning and Deep Learning models?
Ans. 
Deployment of ML/DL models involves integrating them into production systems for real-time predictions and decision-making.
• Model Serving: Use frameworks like TensorFlow Serving or TorchServe to expose models as APIs.

• Containerization: Deploy models using Docker to ensure consistency across environments.

• Monitoring: Implement tools like Prometheus or Grafana to track model performance and data drift.

• Scaling: Use cloud ser...read more
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Q7. How many teams are there within my office?
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Q8. Why you are looking for job change?
Ans. 
I'm seeking new challenges to grow my skills and contribute to innovative projects in a dynamic environment.
• Desire for professional growth: I'm eager to expand my expertise in machine learning and data analysis.

• Interest in innovative projects: I want to work on cutting-edge technologies, such as AI and big data solutions.

• Cultural fit: I'm looking for a collaborative environment that values creativity and teamwork.

• Caree...read more
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Q9. Why fine-tuning of models is required?
Ans. 
Fine-tuning adjusts pre-trained models to improve performance on specific tasks or datasets.
• Enhances model accuracy by adapting to specific data distributions.

• Reduces overfitting by leveraging knowledge from pre-trained models.

• Saves time and resources compared to training from scratch.

• Example: Fine-tuning a language model for sentiment analysis on product reviews.

• Allows for transfer learning, where knowledge from one d...read more
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Q10. Tell me about LSTM.
Ans. 
LSTM (Long Short-Term Memory) is a type of recurrent neural network designed to learn long-term dependencies in sequential data.
• LSTMs are used in natural language processing for tasks like language translation and sentiment analysis.

• They can remember information for long periods, making them suitable for time series forecasting.

• LSTMs mitigate the vanishing gradient problem common in traditional RNNs.

• An example applicat...read more
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Q11. What is difference between Normal LSTM and bi-directional LSTM?
Ans. 
Normal LSTM processes data in one direction, while bi-directional LSTM processes data in both forward and backward directions.
• Normal LSTM reads input sequences from start to end, capturing temporal dependencies in one direction.

• Bi-directional LSTM consists of two LSTMs: one processes the input sequence forward, and the other processes it backward.

• This dual processing allows bi-directional LSTMs to capture context from ...read more
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Q12. How you will train sentence classification model?
Ans. 
Train a sentence classification model using labeled data, feature extraction, and machine learning algorithms.
• Collect a labeled dataset of sentences with corresponding categories (e.g., positive, negative, neutral).

• Preprocess the text data: tokenize, remove stop words, and apply stemming or lemmatization.

• Convert sentences into numerical features using techniques like TF-IDF or word embeddings (e.g., Word2Vec, GloVe).

• Sp...read more
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Q13. Given a list of numbers, create dictionary where key is unique value and 'value of this key' is number of occurrences within the given list. Share your screen and write python code for this.
Ans. 
Create a dictionary from a list where keys are unique numbers and values are their counts.
• Use Python's built-in collections module, specifically Counter, to simplify counting occurrences.

• Example: For the list [1, 2, 2, 3], the output should be {1: 1, 2: 2, 3: 1}.

• Alternatively, use a loop to iterate through the list and build the dictionary manually.
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Q14. How to deal with imbalanced dataset?
Ans. 
Imbalanced datasets can skew model performance; various techniques can help mitigate this issue.
• Resampling techniques: Use oversampling (e.g., SMOTE) or undersampling to balance classes.

• Use different evaluation metrics: Focus on precision, recall, and F1-score instead of accuracy.

• Implement cost-sensitive learning: Assign higher misclassification costs to minority class instances.

• Try ensemble methods: Techniques like Ra...read more
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Q15. What are your salary expectations, preferred location and notice period?
Ans. 
I'm seeking a competitive salary, flexible location, and a notice period of two weeks.
• Salary expectations: Based on market research, I expect a salary in the range of $90,000 to $120,000, depending on the role and responsibilities.

• Preferred location: I am open to remote work but would prefer a hybrid model with occasional office visits in New York or San Francisco.

• Notice period: I am currently employed and would need t...read more
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Q16. Sort a given list of numbers without using in-built sort function. Need to write python code.
Ans. 
Implementing a sorting algorithm in Python to sort a list of numbers without using built-in functions.
• Use the Bubble Sort algorithm: repeatedly swap adjacent elements if they are in the wrong order.

• Example: For the list [5, 2, 9, 1], after one pass it becomes [2, 5, 1, 9].

• Consider using the Selection Sort algorithm: find the minimum element and swap it with the first unsorted element.

• Example: For the list [64, 25, 12, ...read more
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Q1. Basic question on the tool I use in previous company and ask question on the components of it.
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Q1. More on what is my role and managerial round.
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Q1. 2 hour long interview with a panel of 4. Possibly every single question that can be asked and repeatedly same things. They tried understanding the process and functions elaborately way too much which was ...read more
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Q1. Everything was already asked. But they need to drench and waste time of the candidate and themselves so they set up another round of technical to ask the same questions again and again.
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Q1. About previous experience
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About previous experience assignments

Q1. About previous experience
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Q2. Experience abut old company
Ans. 
I gained valuable experience in operations management, enhancing efficiency and team collaboration at my previous company.
• Led a team of 15 in streamlining daily operations, resulting in a 20% increase in productivity.

• Implemented a new inventory management system that reduced stock discrepancies by 30%.

• Conducted regular training sessions to improve team skills, which boosted employee satisfaction scores by 15%.

• Collabora...read more
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Q1. About previous experience
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