HCLTech Ai Ml Engineer Interview Questions and Answers

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?
Ans. 
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
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
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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. Steps of pre-processing
Ans. 
Pre-processing steps involve cleaning, transforming, and preparing data for machine learning models.
• Data cleaning: removing missing values, duplicates, and outliers

• Data transformation: scaling, encoding categorical variables, and feature engineering

• Data normalization: ensuring all features have the same scale

• Data splitting: dividing data into training and testing sets
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Q2. What is lemmatization ?
Ans. 
Lemmatization is the process of reducing words to their base or root form.
• Lemmatization helps in standardizing words for analysis.

• It reduces inflected words to their base form.

• For example, 'running' becomes 'run' after lemmatization.
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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?
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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. Q. explain your project Q. Project-related questions asked basic to hard Q. What is backpropagation in deep learning? Q. What is the Hugging Face module and working Q. Object detection YOLO NAS Vs YOLO 8
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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. Scenario based questions
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