Infosys Interview Pattern

 Infosys Interview Pattern | Automation Testing

🔹 Round 1: Technical Round (Core + Coding)

👉 This is where most candidates get rejected
What I (interviewer) will ask you:
💡 1. Self Introduction (VERY IMPORTANT)
Not your story
Your project + framework + tools + impact
👉 Example structure:
Experience
Project
Tools (Selenium, Java, API, etc.)
What YOU did

💡 2. Selenium + Automation Concepts
Expect questions like:

Difference between find Element() vs find Elements()
Waits (Implicit vs Explicit)
Handling dropdown / alerts / frames
POM (Page Object Model)
How you handle dynamic elements
👉 I will ask:
“Tell me how YOU used it in your project?”

💡 3. Java (Important for Infosys)
OOP concepts (Inheritance, Polymorphism)
Collections (List, Map)
String programs
👉 Example:
Reverse string
Find duplicates
Palindrome

💡 4. Framework Questions (Game Changer)
👉 I WILL ask this: “Explain your framework architecture”
You must explain:
Test layer
Page layer
Base class
Utilities
Reporting
CI/CD

👉 And MOST IMPORTANT:
Flow (how everything works together)

💡 5. API + SQL (Sometimes Asked)
What is API testing
Status codes
Basic SQL query
Join

💡 6. Scenario-Based Questions (VERY IMPORTANT)
👉 I check your thinking:
Test case failing → what will you do?
Script working yesterday but failing today
Element not found → how will you handle

🔹 Round 2: Managerial Round
👉 Here I check your real experience
Questions:
Explain your project in detail
Your role & responsibilities
Challenges faced
How you handled deadlines
How you work in Agile

👉 Tip: Use real examples only (no theory)
🔹 Round 3: HR Round
Expected salary
Notice period
Relocation
Why Infosys?

🔥 What Infosys REALLY Checks

👉 Not tools… but:
✔ Clarity of basics
✔ Confidence in explanation
✔ Real project understanding
✔ Problem-solving mindset

Locator

 Here are some tips to help you choose the best locator strategy:

1️⃣ ID Locator: The fastest and most reliable. Always prefer `id` whenever possible.

2️⃣ Name Locator: Great for forms, especially when no `id` is available. It's also quick, though not as reliable as `id`.

3️⃣ Class Name Locator: Ideal for identifying elements with shared CSS class names. Use it when the class is unique and stable.

4️⃣ XPath Locator: Powerful and flexible but slower. Use XPath when other locators are not an option, but try to avoid overcomplicating your expressions.

5️⃣ CSS Selector Locator: A great alternative to XPath! Faster and more efficient, especially with complex web pages.

Python Roadmap

 Python Mastery Roadmap

Python is one of the most important skills for data engineering.

But most beginners learn it in a random way.

They learn syntax.
Then jump to pandas.
Then watch a PySpark tutorial.
Then get confused when they try to build an actual pipeline.

The problem is not Python.

The problem is the learning order.

If you want to use Python for data engineering, you need to understand how each layer connects.

Start with the basics:

Python fundamentals, variables, loops, functions, data types, and error handling.

Then move into data structures like lists, tuples, dictionaries, sets, and strings.

After that, learn file handling because real data rarely comes in a perfect table.

You will work with CSV, JSON, Excel, TXT, Parquet, Avro, XML, and YAML files.

Then comes the practical part:

Learn the libraries that data engineers use every day.

Pandas and NumPy for data handling.
Requests for APIs.
SQLAlchemy for database connections.
PyArrow, Polars, OpenPyXL, and BeautifulSoup for more specific use cases.

Once you understand that, move toward databases, data extraction, transformation, ETL pipelines, orchestration, cloud storage, big data, testing, logging, and monitoring.

That is when Python becomes more than a programming language.

It becomes a tool to move, clean, validate, transform, and automate data workflows.

For data engineering, do not just learn Python syntax.

Learn Python in the context of pipelines, storage, APIs, databases, orchestration, and production systems.