Common Programming Interview Questions
How do you reverse a string?
How do you determine if a string is a palindrome?
How do you calculate the number of numerical digits in a string?
How do you find the count for the occurrence of a particular character in a string?
How do you find the non-matching characters in a string?
How do you find out if the two given strings are anagrams?
How do you calculate the number of vowels and consonants in a string?
How do you total all of the matching integer elements in an array?
How do you reverse an array?
How do you find the maximum element in an array?
How do you sort an array of integers in ascending order?
How do you print a Fibonacci sequence using recursion?
How do you calculate the sum of two integers?
How do you find the average of numbers in a list?
How do you check if an integer is even or odd?
How do you find the middle element of a linked list?
How do you remove a loop in a linked list?
How do you merge two sorted linked lists?
How do you implement binary search to find an element in a sorted array?
How do you print a binary tree in vertical order?
Conceptual Coding Interview Questions
What is a data structure?
What is an array?
What is a linked list?
What is the difference between an array and a linked list?
What is LIFO?
What is FIFO?
What is a stack?
What are binary trees?
What are binary search trees?
What is object-oriented programming?
What is the purpose of a loop in programming?
What is a conditional statement?
What is debugging?
What is recursion?
What are the differences between linear and non-linear data structures?
General Coding Interview Questions
What programming languages do you have experience working with?
Describe a time you faced a challenge in a project you were working on and how you overcame it.
Walk me through a project you’re currently or have recently worked on.
Give an example of a project you worked on where you had to learn a new programming language or technology. How did you go about learning it?
How do you ensure your code is readable by other developers?
What are your interests outside of programming?
How do you keep your skills sharp and up to date?
How do you collaborate on projects with non-technical team members?
Tell me about a time when you had to explain a complex technical concept to a non-technical team member.
How do you get started on a new coding project?
Best Programming Resources: https://topmate.io/coding/898340
Join for more: https://t.iss.one/programming_guide
ENJOY LEARNING 👍👍
How do you reverse a string?
How do you determine if a string is a palindrome?
How do you calculate the number of numerical digits in a string?
How do you find the count for the occurrence of a particular character in a string?
How do you find the non-matching characters in a string?
How do you find out if the two given strings are anagrams?
How do you calculate the number of vowels and consonants in a string?
How do you total all of the matching integer elements in an array?
How do you reverse an array?
How do you find the maximum element in an array?
How do you sort an array of integers in ascending order?
How do you print a Fibonacci sequence using recursion?
How do you calculate the sum of two integers?
How do you find the average of numbers in a list?
How do you check if an integer is even or odd?
How do you find the middle element of a linked list?
How do you remove a loop in a linked list?
How do you merge two sorted linked lists?
How do you implement binary search to find an element in a sorted array?
How do you print a binary tree in vertical order?
Conceptual Coding Interview Questions
What is a data structure?
What is an array?
What is a linked list?
What is the difference between an array and a linked list?
What is LIFO?
What is FIFO?
What is a stack?
What are binary trees?
What are binary search trees?
What is object-oriented programming?
What is the purpose of a loop in programming?
What is a conditional statement?
What is debugging?
What is recursion?
What are the differences between linear and non-linear data structures?
General Coding Interview Questions
What programming languages do you have experience working with?
Describe a time you faced a challenge in a project you were working on and how you overcame it.
Walk me through a project you’re currently or have recently worked on.
Give an example of a project you worked on where you had to learn a new programming language or technology. How did you go about learning it?
How do you ensure your code is readable by other developers?
What are your interests outside of programming?
How do you keep your skills sharp and up to date?
How do you collaborate on projects with non-technical team members?
Tell me about a time when you had to explain a complex technical concept to a non-technical team member.
How do you get started on a new coding project?
Best Programming Resources: https://topmate.io/coding/898340
Join for more: https://t.iss.one/programming_guide
ENJOY LEARNING 👍👍
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Sometimes reality outpaces expectations in the most unexpected ways.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.
What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.
GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse
GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse
Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report
Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse
Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.
What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.
GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse
GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse
Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report
Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse
Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.
❤4👍3
Amazing NumPy Cheat Sheet.pdf
259.7 KB
Amazing NumPy Cheat Sheet Snippet with 100 exercises for practicing the concept to get hands on to clear the coding round in the interviews
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Top Data Science Tools — By Function 📊
A quick view of the tools commonly used across the data science workflow:
🔹 Data Collection
• Scrapy, BeautifulSoup – Web scraping
• APIs – External data access
• Selenium – Dynamic scraping
• Google BigQuery – Large-scale data ingestion
🔹 Data Cleaning & Processing
• Pandas – Data manipulation
• NumPy – Numerical computing
• OpenRefine – Data cleanup
• Excel – Basic cleaning & formatting
🔹 Modeling & Machine Learning
• Scikit-learn – Classical ML
• TensorFlow – Deep learning
• PyTorch – Research-friendly DL
• XGBoost – Gradient boosting
• Keras – Neural network APIs
🔹 Deployment
• Docker – Containerization
• Kubernetes – Model scalability
• FastAPI – ML APIs
• AWS SageMaker – End-to-end ML deployment
• MLflow – Experiment tracking
🔹 Visualization & BI
• Matplotlib, Seaborn – Statistical plots
• Plotly – Interactive charts
• Tableau, Power BI – Business dashboards
👉 Tools change, but knowing when and why to use them matters more than how many you know.
A quick view of the tools commonly used across the data science workflow:
🔹 Data Collection
• Scrapy, BeautifulSoup – Web scraping
• APIs – External data access
• Selenium – Dynamic scraping
• Google BigQuery – Large-scale data ingestion
🔹 Data Cleaning & Processing
• Pandas – Data manipulation
• NumPy – Numerical computing
• OpenRefine – Data cleanup
• Excel – Basic cleaning & formatting
🔹 Modeling & Machine Learning
• Scikit-learn – Classical ML
• TensorFlow – Deep learning
• PyTorch – Research-friendly DL
• XGBoost – Gradient boosting
• Keras – Neural network APIs
🔹 Deployment
• Docker – Containerization
• Kubernetes – Model scalability
• FastAPI – ML APIs
• AWS SageMaker – End-to-end ML deployment
• MLflow – Experiment tracking
🔹 Visualization & BI
• Matplotlib, Seaborn – Statistical plots
• Plotly – Interactive charts
• Tableau, Power BI – Business dashboards
👉 Tools change, but knowing when and why to use them matters more than how many you know.
👍5❤3
✅ Python for Machine Learning – Beginner to Job-Ready Roadmap 🤖🐍
📍 1️⃣ Python Basics
– Variables, Data Types, Operators
– if-else, loops, functions
✅ Practice: Write a BMI calculator, number guessing game
📍 2️⃣ Data Structures & Libraries
– Lists, Dicts, Tuples, Sets
– NumPy: arrays, slicing, broadcasting
– Pandas: DataFrames, filtering, merging
✅ Practice: Analyze a CSV with Pandas
📍 3️⃣ Data Visualization
– Matplotlib, Seaborn basics
– Plotting histograms, boxplots, heatmaps
✅ Project: Visualize Titanic dataset insights
📍 4️⃣ Data Preprocessing
– Handling nulls, encoding, scaling
– Feature engineering & selection
✅ Practice: Clean a housing prices dataset
📍 5️⃣ Machine Learning with Scikit-learn
– Regression, Classification, Clustering
– Model training, prediction, evaluation
✅ Project: Predict student scores using Linear Regression
📍 6️⃣ Model Evaluation
– Accuracy, Precision, Recall, F1-Score
– Confusion Matrix, ROC-AUC
✅ Practice: Evaluate a classification model
📍 7️⃣ Model Tuning & Pipelines
– GridSearchCV, cross-validation
– Build ML pipelines for clean code
✅ Project: Optimize a Random Forest model
📍 8️⃣ Real-World ML Projects
– House price prediction
– Customer churn analysis
– Image classification
✅ Tip: Use datasets from Kaggle, UCI, or open APIs
💬 Tap ❤️ for more!
📍 1️⃣ Python Basics
– Variables, Data Types, Operators
– if-else, loops, functions
✅ Practice: Write a BMI calculator, number guessing game
📍 2️⃣ Data Structures & Libraries
– Lists, Dicts, Tuples, Sets
– NumPy: arrays, slicing, broadcasting
– Pandas: DataFrames, filtering, merging
✅ Practice: Analyze a CSV with Pandas
📍 3️⃣ Data Visualization
– Matplotlib, Seaborn basics
– Plotting histograms, boxplots, heatmaps
✅ Project: Visualize Titanic dataset insights
📍 4️⃣ Data Preprocessing
– Handling nulls, encoding, scaling
– Feature engineering & selection
✅ Practice: Clean a housing prices dataset
📍 5️⃣ Machine Learning with Scikit-learn
– Regression, Classification, Clustering
– Model training, prediction, evaluation
✅ Project: Predict student scores using Linear Regression
📍 6️⃣ Model Evaluation
– Accuracy, Precision, Recall, F1-Score
– Confusion Matrix, ROC-AUC
✅ Practice: Evaluate a classification model
📍 7️⃣ Model Tuning & Pipelines
– GridSearchCV, cross-validation
– Build ML pipelines for clean code
✅ Project: Optimize a Random Forest model
📍 8️⃣ Real-World ML Projects
– House price prediction
– Customer churn analysis
– Image classification
✅ Tip: Use datasets from Kaggle, UCI, or open APIs
💬 Tap ❤️ for more!
👍5❤4
🚀 Greetings from PVR Cloud Tech!! 🌈
🔥 Do you want to become a Master in Azure Cloud Data Engineering?
If you're ready to build in-demand skills and unlock exciting career opportunities, this is the perfect place to start!
📌 Start Date: 08th December 2025
⏰ Time: 09 PM – 10 PM IST | Monday
🔹 Course Content:
https://drive.google.com/file/d/1YufWV0Ru6SyYt-oNf5Mi5H8mmeV_kfP-/view
📱 Join WhatsApp Group:
https://chat.whatsapp.com/D0i5h9Vrq4FLLMfVKCny7u
📥 Register Now:
https://forms.gle/mHup49JAZDREAarw6
📺 WhatsApp Channel:
https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n
Team
PVR Cloud Tech:)
+91-9346060794
🔥 Do you want to become a Master in Azure Cloud Data Engineering?
If you're ready to build in-demand skills and unlock exciting career opportunities, this is the perfect place to start!
📌 Start Date: 08th December 2025
⏰ Time: 09 PM – 10 PM IST | Monday
🔹 Course Content:
https://drive.google.com/file/d/1YufWV0Ru6SyYt-oNf5Mi5H8mmeV_kfP-/view
📱 Join WhatsApp Group:
https://chat.whatsapp.com/D0i5h9Vrq4FLLMfVKCny7u
📥 Register Now:
https://forms.gle/mHup49JAZDREAarw6
📺 WhatsApp Channel:
https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n
Team
PVR Cloud Tech:)
+91-9346060794
❤4
Tired of AI that refuses to help?
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@UnboundGPT_bot doesn't lecture. It just works.
✓ Multiple models (GPT-4o, Gemini, DeepSeek)
✓ Image generation & editing
✓ Video creation
✓ Persistent memory
✓ Actually uncensored
Free to try → @UnboundGPT_bot or https://ko2bot.com
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