Tech Mahindra Interview Questions and Answers for Freshers

I have a strong background in customer service, excellent communication skills, and a passion for helping customers.

• Extensive experience in customer service roles

• Excellent communication skills

• Strong problem-solving abilities

• Passion for helping customers

• Proven track record of resolving customer issues efficiently

I am fluent in English, Hindi, and Spanish. I have knowledge about PhonePe's services and features.

• Fluent in English, Hindi, and Spanish

• Knowledge about PhonePe's services and features

• Experience in customer service roles

Dynamic professional with a strong background in backend operations, skilled in optimizing processes and enhancing team performance.

• Over 5 years of experience in backend operations management, focusing on efficiency and productivity.

• Proficient in data analysis and reporting, using tools like SQL and Excel to drive decision-making.

• Led a project that reduced processing time by 30% through automation and streamlined workf...read more

My most memorable moments revolve around teamwork, problem-solving, and achieving goals that made a significant impact.

• Successfully led a project that streamlined backend processes, reducing response time by 30%.

• Collaborated with cross-functional teams to launch a new feature that increased user engagement by 25%.

• Resolved a critical system outage within hours, restoring service and earning recognition from management.

In my last role, I managed backend operations, streamlined processes, and improved efficiency across various departments.

• Oversaw daily operations, ensuring smooth workflow and timely project completion.

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

• Collaborated with cross-functional teams to enhance communication and project tracking.

• Conducted regular training sessions for staff on new softwar...read more

I have researched salary packages in backend operations, noting trends and variations based on experience and location.

• Entry-level positions typically range from $50,000 to $70,000 annually.

• Mid-level roles can earn between $70,000 and $100,000, depending on skills and responsibilities.

• Senior positions often exceed $100,000, especially in tech hubs like San Francisco or New York.

• Benefits such as bonuses, stock options, ...read more

The output after inner join of table 1 and table 2 will be 2,3,5.

• Inner join only includes rows that have matching values in both tables.

• Values 2, 3, and 5 are present in both tables, so they will be included in the output.

• Null values are not considered as matching values in inner join.

Query to find customer names with the maximum orders from Customers and Orders tables.

• Use JOIN to combine Customers and Orders tables on CustomerID.

• Group by CustomerID and count orders to find the maximum.

• Use a subquery to filter customers with the maximum order count.

• Example SQL: SELECT c.customerName FROM Customers c JOIN Orders o ON c.customerID = o.CustomerID GROUP BY c.customerID HAVING COUNT(o.OrderId) = (SELECT ...read more

The project involves building a data pipeline to ingest, process, and analyze large volumes of data from various sources in Azure.

• Utilizing Azure Data Factory for data ingestion and orchestration

• Implementing Azure Databricks for data processing and transformation

• Storing processed data in Azure Data Lake Storage

• Using Azure Synapse Analytics for data warehousing and analytics

• Leveraging Azure DevOps for CI/CD pipeline aut...read more

Designing an effective ADF pipeline involves considering various metrics and factors.

• Understand the data sources and destinations

• Identify the dependencies between activities

• Optimize data movement and processing for performance

• Monitor and track pipeline execution for troubleshooting

• Consider security and compliance requirements

• Use parameterization and dynamic content for flexibility

• Implement error handling and retries fo...read more

Optimize data processing by partitioning, indexing, and using efficient storage formats.

• Partitioning: Divide large datasets into smaller, manageable chunks. For example, partitioning a sales dataset by year.

• Indexing: Create indexes on frequently queried columns to speed up data retrieval. For instance, indexing customer IDs in a transaction table.

• Data Compression: Use compressed formats like Parquet or ORC to reduce st...read more

Use Slowly Changing Dimensions (SCD) to preserve historical data while reconstructing a table.

• Implement SCD Type 1 for overwriting old data without keeping history.

• Use SCD Type 2 to create new records for changes, preserving history.

• Example of SCD Type 2: If a customer's address changes, add a new record with the new address and mark the old record as inactive.

• SCD Type 3 allows for limited history by adding new columns...read more

Databricks enhances data processing with advanced analytics, collaboration, and scalability beyond ADF's capabilities.

• Databricks provides a collaborative environment for data scientists and engineers to work together using notebooks.

• It supports advanced analytics and machine learning workflows, which ADF lacks natively.

• Databricks can handle large-scale data processing with Apache Spark, making it more efficient for big...read more

Bigtable is a NoSQL database for real-time analytics, while BigQuery is a fully managed data warehouse for running SQL queries.

• Bigtable is a NoSQL database designed for real-time analytics and high throughput, while BigQuery is a fully managed data warehouse for running SQL queries.

• Bigtable is used for storing large amounts of semi-structured data, while BigQuery is used for analyzing structured data using SQL queries.

• ...read more

To remove duplicate rows from BigQuery, use the DISTINCT keyword. To find the month of a given date, use the EXTRACT function.

• To remove duplicate rows, use SELECT DISTINCT * FROM table_name;

• To find the month of a given date, use SELECT EXTRACT(MONTH FROM date_column) AS month_name FROM table_name;

• Make sure to replace 'table_name' and 'date_column' with the appropriate values in your query.

The operator used in Composer to move data from GCS to BigQuery is the GCS to BigQuery operator.

• The GCS to BigQuery operator is used in Apache Airflow, which is the underlying technology of Composer.

• This operator allows you to transfer data from Google Cloud Storage (GCS) to BigQuery.

• You can specify the source and destination parameters in the operator to define the data transfer process.

Code to square each element in the input array.

• Iterate through the input array and square each element.

• Store the squared values in a new array to get the desired output.

Dataflow is a fully managed stream and batch processing service, while Dataproc is a managed Apache Spark and Hadoop service.

• Dataflow is a serverless data processing service that automatically scales to handle your data, while Dataproc is a managed Spark and Hadoop service that requires you to provision and manage clusters.

• Dataflow is designed for both batch and stream processing, allowing you to process data in real-t...read more

BigQuery architecture includes storage, execution, and optimization components for efficient query processing.

• BigQuery stores data in Capacitor storage system for fast access.

• Query execution is distributed across multiple nodes for parallel processing.

• Query optimization techniques include partitioning tables, clustering tables, and using query cache.

• Using partitioned tables can help eliminate scanning unnecessary data.

• ...read more

RDD vs dataframe vs dataset in PySpark

• RDD (Resilient Distributed Dataset) is the basic abstraction in PySpark, representing a distributed collection of objects

• Dataframe is a distributed collection of data organized into named columns, similar to a table in a relational database

• Dataset is a distributed collection of data with the ability to use custom classes for type safety and user-defined functions

• Dataframes and Data...read more