NTT Data Ai Ml Engineer Interview Questions and Answers

Q1. Scenario based questions
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Q1. What are types of prompts?
Ans. 
Types of prompts include text prompts, image prompts, audio prompts, and video prompts.
• Text prompts: prompts that are in written form

• Image prompts: prompts that are in visual form

• Audio prompts: prompts that are in audio form

• Video prompts: prompts that are in video form
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Q2. What is the difference between the Symmetric and Asymmetric?
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Symmetric encryption uses the same key for both encryption and decryption, while asymmetric encryption uses different keys for encryption and decryption.
• Symmetric encryption is faster and more efficient than asymmetric encryption.

• Asymmetric encryption provides better security as it uses a public key for encryption and a private key for decryption.

• Examples of symmetric encryption algorithms include AES and DES, while ex...read more
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30 MCQs where 15 Need to be answered correctly to get shortlisted.
Sanfoundary source is very helpful in cracking it.

Q1. Explain Oops concepts
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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...read more
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Q2. Explain file handling
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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.
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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. Difference between Lemmatization and stemming
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Lemmatization produces the base or dictionary form of a word, while stemming reduces words to their root form.
• Lemmatization considers the context and meaning of the word, resulting in a valid word that makes sense.

• Stemming simply chops off prefixes or suffixes, potentially resulting in non-existent words.

• Example: Lemmatization of 'better' would result in 'good', while stemming would reduce it to 'bet'.
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Q1. Explain the ML project you recently worked?
Ans. 
Developed a recommendation system for an e-commerce platform using collaborative filtering
• Used collaborative filtering to analyze user behavior and recommend products

• Implemented matrix factorization techniques to improve recommendation accuracy

• Evaluated model performance using metrics like RMSE and precision-recall curves
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Q2. Questions related to ML fundamentals like supervised learning, unsupervised learning, evaluation and ML algorithms
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Q3. Project specific questions
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Q4. Easy-medium coding questions
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Q1. What technologies you are working on?
Ans. 
I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.
• Python programming language

• TensorFlow framework

• scikit-learn library
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Q2. How you will approach on machine learning problem?
Ans. 
I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.
• Understand the problem statement and define the objectives clearly.

• Collect and preprocess the data to make it suitable for training.

• Select a suitable machine learning algorithm based on the problem type (clas...read more
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Q1. About Handling Missing Values
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Handling missing values is crucial for accurate data analysis and model performance in machine learning.
• Identify missing values using methods like isnull() in pandas.

• Impute missing values with mean, median, or mode; e.g., replacing NaN with the mean of the column.

• Use algorithms that support missing values, such as XGBoost.

• Consider dropping rows or columns with excessive missing data; e.g., if more than 50% of a column ...read more
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Q2. Questions on Embedding
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Q1. Normal HR round that happens as the initial screening process
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Python basic coding questions (pandas, numpy, OOPs concept.
AI ML theory questions

Q1. Normal salary discussion
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Q1. Question on random forest and linear regression
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