Oracle Interview Questions, Process, and Tips

Q1. Advantage disadvantage of monolith and microservices
Ans. 
Monolith has simplicity but lacks scalability, while microservices offer flexibility but introduce complexity.
• Monolith: Simplicity in development and deployment, easier to debug and test

• Monolith: Lack of scalability, all components tightly coupled

• Microservices: Flexibility to use different technologies for different services

• Microservices: Scalability, each service can be independently scaled

• Microservices: Increased com...read more
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Q2. Lock and Synchronised difference
Ans. 
Lock and Synchronised are both mechanisms used for thread synchronization in Java.
• Lock is a more flexible and powerful mechanism compared to synchronized keyword in Java.

• Lock interface provides more functionalities like tryLock(), lockInterruptibly(), etc.

• Lock allows for more fine-grained control over locking and unlocking of resources.

• Synchronized keyword is simpler to use but may lead to deadlocks in complex scenario...read more
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Q1. Experience above 12 years
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Q2. Experience field and marketing
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Q1. Write selenium code for getting values in a dynamic table
Ans. 
Use Selenium to extract values from a dynamic table
• Identify the table using its locator (id, class, xpath, etc.)

• Iterate through the rows and columns of the table to extract values

• Use Selenium commands like findElements and getText to retrieve the values

• Handle dynamic content by waiting for elements to be present or visible
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Q1. Java union of arrays
Ans. 
To find the union of two arrays in Java, use a HashSet to store unique elements from both arrays.
• Create two arrays of strings.

• Convert arrays to HashSet to remove duplicates.

• Combine both HashSets to get the union of arrays.
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Q1. Regular Expressions related
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The assignment was given online and the questions were on DSA and SQL

Q1. It was an in person technical interview they asked me about projects that I worked on in my academics
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Q1. How do you preprocess large/small dataset
Ans. 
Preprocessing large/small datasets involves cleaning, transforming, and organizing data to prepare it for analysis.
• Remove duplicates and missing values

• Normalize or standardize numerical features

• Encode categorical variables

• Feature scaling

• Handling outliers

• Dimensionality reduction techniques like PCA

• Splitting data into training and testing sets
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Q2. Data augmentation
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Q1. Collections related questions
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Q2. Multi thread questions
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