Accenture Interview Questions, Process, and Tips

30 MCQs where 15 Need to be answered correctly to get shortlisted.
Sanfoundary source is very helpful in cracking it.

Q1. Explain Oops concepts
Ans. 
Oops concepts refer to Object-Oriented Programming principles such as Inheritance, Encapsulation, Polymorphism, and Abstraction.
• Inheritance: Allows a class to inherit properties and behavior from another class.

• Encapsulation: Bundling data and methods that operate on the data into a single unit.

• Polymorphism: Ability to present the same interface for different data types.

• Abstraction: Hiding the complex implementation det
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Q2. Explain file handling
Ans. 
File handling refers to the process of managing and manipulating files on a computer system.
• File handling involves tasks such as creating, reading, writing, updating, and deleting files.

• Common file operations include opening a file, reading its contents, writing data to it, and closing the file.

• File handling in programming languages often involves using functions or libraries specifically designed for file operations.

• E...read more
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Q3. Explain supervised and unsupervised learning algorithms of your choice.
Ans. 
Supervised learning uses labeled data to train a model, while unsupervised learning finds patterns in unlabeled data.
• Supervised learning requires input-output pairs for training

• Examples include linear regression, support vector machines, and neural networks

• Unsupervised learning clusters data based on similarities or patterns

• Examples include k-means clustering, hierarchical clustering, and principal component analysis
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Q4. Coding question on pandas which had 10 followup questions
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Q1. Simple discussion on compensation
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Q1. Oops concepts and implementation
Ans. 
Oops concepts refer to object-oriented programming principles like inheritance, encapsulation, polymorphism, and abstraction.
• Inheritance allows a class to inherit properties and behavior from another class.

• Encapsulation involves bundling data and methods that operate on the data into a single unit.

• Polymorphism allows objects of different classes to be treated as objects of a common superclass.

• Abstraction focuses on hid...read more
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Q2. Sql Concepts and Ssms
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Q1. .Net Technology basics
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Q2. Solid principles
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Q1. Describe any NLP project end to end.
Ans. 
Developed a sentiment analysis model using NLP to analyze customer reviews for a product.
• Collected and preprocessed text data from various sources

• Performed tokenization, stopword removal, and lemmatization

• Built a machine learning model using techniques like TF-IDF and LSTM

• Evaluated the model's performance using metrics like accuracy and F1 score

• Deployed the model for real-time sentiment analysis of new reviews
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Q2. What is cosine similarity?
Ans. 
Cosine similarity is a measure of similarity between two non-zero vectors in an inner product space.
• It measures the cosine of the angle between the two vectors.

• Values range from -1 (completely opposite) to 1 (exactly the same).

• Used in recommendation systems, text mining, and clustering algorithms.
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Q3. What is difference between iterator and iterable?
Ans. 
Iterator is an object that allows iteration over a collection, while iterable is an object that can be iterated over.
• Iterator is an object with a next() method that returns the next item in the collection.

• Iterable is an object that has an __iter__() method which returns an iterator.

• Example: List is iterable, while iter(list) returns an iterator.
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Q4. Write a python function for cosine similarity.
Ans. 
Python function to calculate cosine similarity between two vectors.
• Define a function that takes two vectors as input.

• Calculate the dot product of the two vectors.

• Calculate the magnitude of each vector and multiply them.

• Divide the dot product by the product of magnitudes to get cosine similarity.
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Q5. How did you evaluate your model. what is F1 score.
Ans. 
F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall.
• F1 score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).

• It is a better metric than accuracy when dealing with imbalanced datasets.

• A high F1 score indicates a model with both high precision and high recall.

• F1 score ranges from 0 to 1, where
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Q1. What is parallel cursor method
Ans. 
Parallel cursor method is a technique used in database programming to improve performance by processing multiple rows simultaneously.
• Parallel cursor method allows multiple rows to be fetched and processed concurrently, reducing overall processing time.

• It is commonly used in scenarios where large datasets need to be processed efficiently.

• By leveraging parallel cursor method, developers can optimize database operations a
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Q2. Can you debug a CDS views
Ans. 
Yes, I can debug CDS views by using tools like SQL Trace and ABAP Debugger.
• Use SQL Trace to analyze the SQL statements executed by the CDS view

• Use ABAP Debugger to step through the code and analyze variables

• Check for any syntax errors or logical errors in the CDS view definition
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Q3. More questions on Enhancement techniques
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