Mphasis Lead Engineer Interview Questions and Answers

We are using Java 11 in our project, which includes features like local variable type inference, HTTP client API, and improved security.

• Java 11 introduced local variable type inference, allowing the compiler to infer the type of a variable based on the initializer.

• The HTTP client API in Java 11 provides a more modern and flexible way to send HTTP requests and handle responses.

• Java 11 also includes improved security fea...read more

We are using Spring Boot version 2.5.2 in our project, which includes features like improved startup performance, enhanced actuator endpoints, and updated dependencies.

• Spring Boot 2.5.2 offers improved startup performance compared to previous versions.

• Enhanced actuator endpoints provide better monitoring and management capabilities.

• Updated dependencies ensure compatibility with the latest libraries and frameworks.

The 12 factors approach is a methodology for building modern, scalable, and maintainable microservices.

• Codebase: One codebase tracked in revision control, many deploys

• Dependencies: Explicitly declare and isolate dependencies

• Config: Store config in the environment

• Backing services: Treat backing services as attached resources

• Build, release, run: Strictly separate build and run stages

• Processes: Execute the app as one or m...read more

Use Java 8 streams to find the 3rd highest element in an unsorted array with duplicates.

• Convert the array to a stream using Arrays.stream()

• Use distinct() to remove duplicates

• Sort the stream in descending order using sorted(Comparator.reverseOrder())

• Skip the first two elements using skip(2)

• Find and return the third element using findFirst()

Python libraries commonly used by data engineers include Pandas, NumPy, Matplotlib, and Scikit-learn.

• Pandas: Used for data manipulation and analysis.

• NumPy: Provides support for large, multi-dimensional arrays and matrices.

• Matplotlib: Used for creating visualizations and plots.

• Scikit-learn: Offers machine learning algorithms and tools for data analysis.

PySpark is a Python API for Apache Spark, a powerful open-source distributed computing system.

• PySpark allows users to write Spark applications using Python programming language.

• It provides high-level APIs in Python for Spark's functionality, making it easier to work with large datasets.

• PySpark can be used for data processing, machine learning, graph processing, and more.

• Example: PySpark can be used to perform data anal...read more

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Provides ACID transactions for big data workloads

• Ensures data reliability and quality by enabling schema enforcement and data versioning

• Supports batch and streaming data processing

• Improves data quality and reliability by enabling schema enforcement and data versioning

Create end to end pipelines in Data Factory with SQL and ASQL as source and target respectively.

• Use Copy Data activity to move data from SQL source to ASQL target

• Define linked services for SQL and ASQL in Data Factory

• Create datasets for SQL and ASQL tables

• Map columns between source and target datasets

• Configure data flow activities for any transformations needed

GraphQL is a query language for APIs, Web workers run scripts in background threads, Service workers are scripts that run in the background and can intercept network requests.

• GraphQL is a query language for APIs that allows clients to request only the data they need.

• Web workers run scripts in background threads separate from the main browser thread, improving performance by handling tasks concurrently.

• Service workers a...read more