HALODOC Interview Questions, Process, and Tips

It was a live coding round where expectation was to come up with a framework in Java from Scratch

Q1. It was a DS/Algo Round
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Q1. Design Round in HLD
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Design Round in HLD involves creating a high-level design for a software system.
• Identify the main components of the system and their interactions

• Define the architecture of the system including layers, modules, and interfaces

• Consider scalability, performance, security, and maintainability

• Use UML diagrams like class diagrams, sequence diagrams, and component diagrams

• Document the design decisions and rationale
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Q1. Domain specific interview(Android) - Took 1.10 hrs - They asked why and how in most of the questions
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Q2. Android architecture like service, activity, most of the topics covered in 1 hrs interview
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Q3. Jetpack AAC, like work manager, LiveData vs MutableLiveData
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Q1. DSPS round - Asked data structure and concepts and then - Ask 2 or 3 data structures and algorithm problems - Give you 15/20 min each It was asked me string manipulations & tree.
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Q2. Two string manipulation with hashing technique
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Hashing technique can be used for efficient string manipulation operations.
• Hashing can be used for string comparison, substring search, and pattern matching.

• Rabin-Karp algorithm is a popular hashing-based substring search algorithm.

• Hash tables can be used for efficient string indexing and searching.

• Hashing can also be used for string compression and encryption.
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Q3. Tree related problem
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Coding round with domain
- It was asked me paging, searching, MVVM, listing with RV and coding standards

Coding questions were easy, not clear about shortlisting process

Q1. Basic DSA questions on arrays, linked lists
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Q1. Self introduction
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Dsa. , oops and basics of database management

Q1. System design questions basics
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Q2. Hiring managers questions
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Practise data structures based problems, should be able to explain the code and optimizations etc

Design system for e-commerce. How to scale the system with the demand

Q1. Building ML ops platform from scratch.
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Building a ML ops platform involves designing infrastructure, tools, and processes to streamline machine learning workflows.
• Design scalable infrastructure to support ML model training and deployment

• Implement version control for ML models and data

• Automate testing and monitoring of ML models

• Integrate with CI/CD pipelines for continuous deployment

• Provide collaboration tools for data scientists and engineers
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Q2. Data governance strategy
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Q1. Team management. Conflict resolution
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Q2. Stakeholder management. Planning
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2 coding question on arrays and strings