In class, there are some students who are really good at coding from the start, and seeing them can make us feel quite demotivated, especially since they often appear overconfident.

But it's not important how much someone already knows. If you start and practice consistently, it's not that tough to match their level or even surpass them.

And often, these overconfident people don’t perform as well as you can because you have the desire to learn, while they think they already know everything.

So, my friend, don’t get demotivated—just give it time!

When you're studying DSA, you probably think, "This won't be directly used in the actual work of a company, so why am I even doing this?"

And in life, where will this even come in handy? Well, it won't be useful directly, but the hard work you're putting in—sitting day and night solving questions—that habit of working hard will pay off.

It's not really about DSA, but about the effort you're willing to give that will decide which company you land your internship or placement in ❤️

DSA question by understanding patterns

If the input array is sorted then
- Binary search
- Two pointers

If asked for all permutations/subsets then
- Backtracking

If given a tree then
- DFS
- BFS

If given a graph then
- DFS
- BFS

If given a linked list then
- Two pointers

If recursion is banned then
- Stack

If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer

If asked for maximum/minimum subarray/ subset/options then
- Dynamic programming

If asked for top/least K items then
- Heap
- QuickSelect

If asked for common strings then
- Map
- Trie

Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space

List of most asked Programming Interview Questions.

Are you preparing for a coding interview? This tweet is for you. It contains a list of the most asked interview questions from each topic.

Arrays

- How is an array sorted using quicksort?
- How do you reverse an array?
- How do you remove duplicates from an array?
- How do you find the 2nd largest number in an unsorted integer array?

Linked Lists

- How do you find the length of a linked list?
- How do you reverse a linked list?
- How do you find the third node from the end?
- How are duplicate nodes removed in an unsorted linked list?

Strings

- How do you check if a string contains only digits?
- How can a given string be reversed?
- How do you find the first non-repeated character?
- How do you find duplicate characters in strings?

Binary Trees

- How are all leaves of a binary tree printed?
- How do you check if a tree is a binary search tree?
- How is a binary search tree implemented?
- Find the lowest common ancestor in a binary tree?

Graph

- How to detect a cycle in a directed graph?
- How to detect a cycle in an undirected graph?
- Find the total number of strongly connected components?
- Find whether a path exists between two nodes of a graph?
- Find the minimum number of swaps required to sort an array.

Dynamic Programming

1. Find the longest common subsequence?
2. Find the longest common substring?
3. Coin change problem?
4. Box stacking problem?
5. Count the number of ways to cover a distance?

20 recently asked 𝗞𝗔𝗙𝗞𝗔 interview questions.

- How do you create a topic in Kafka using the Confluent CLI?
- Explain the role of the Schema Registry in Kafka.
- How do you register a new schema in the Schema Registry?
- What is the importance of key-value messages in Kafka?
- Describe a scenario where using a random key for messages is beneficial.
- Provide an example where using a constant key for messages is necessary.
- Write a simple Kafka producer code that sends JSON messages to a topic.
- How do you serialize a custom object before sending it to a Kafka topic?
- Describe how you can handle serialization errors in Kafka producers.
- Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
- How do you handle deserialization errors in Kafka consumers?
- Explain the process of deserializing messages into custom objects.
- What is a consumer group in Kafka, and why is it important?
- Describe a scenario where multiple consumer groups are used for a single topic.
- How does Kafka ensure load balancing among consumers in a group?
- How do you send JSON data to a Kafka topic and ensure it is properly serialized?
- Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
- Explain how you can work with CSV data in Kafka, including serialization and deserialization.
- Write a Kafka producer code snippet that sends CSV data to a topic.
- Write a Kafka consumer code snippet that reads and processes CSV data from a topic.


All the best 👍🏻👍🏻

Important Data Engineering Concepts for Interviews

1. ETL Processes: Understand the ETL (Extract, Transform, Load) process, including how to design and implement efficient pipelines to move data from various sources to a data warehouse or data lake. Familiarize yourself with tools like Apache NiFi, Talend, and AWS Glue.

2. Data Warehousing: Know the fundamentals of data warehousing, including the star schema, snowflake schema, and how to design a data warehouse that supports efficient querying and reporting. Learn about popular data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake.

3. Data Modeling: Master data modeling concepts, including normalization and denormalization, to design databases that are optimized for both read and write operations. Understand entity-relationship (ER) diagrams and how to use them to model data relationships.

4. Big Data Technologies: Gain expertise in big data frameworks like Apache Hadoop and Apache Spark for processing large datasets. Understand the roles of HDFS, MapReduce, Hive, and Pig in the Hadoop ecosystem, and how Spark’s in-memory processing can accelerate data processing.

5. Data Lakes: Learn about data lakes as a storage solution for raw, unstructured, and semi-structured data. Understand the key differences between data lakes and data warehouses, and how to use tools like Apache Hudi and Delta Lake to manage data lakes efficiently.

6. SQL and NoSQL Databases: Be proficient in SQL for querying and managing relational databases like MySQL, PostgreSQL, and Oracle. Also, understand when and how to use NoSQL databases like MongoDB, Cassandra, and DynamoDB for storing and querying unstructured or semi-structured data.

7. Data Pipelines: Learn how to design, build, and manage data pipelines that automate the flow of data from source systems to target destinations. Familiarize yourself with orchestration tools like Apache Airflow, Luigi, and Prefect for managing complex workflows.

8. APIs and Data Integration: Understand how to integrate data from various APIs and third-party services into your data pipelines. Learn about RESTful APIs, GraphQL, and how to handle data ingestion from external sources securely and efficiently.

9. Data Streaming: Gain knowledge of real-time data processing using streaming technologies like Apache Kafka, Apache Flink, and Amazon Kinesis. Learn how to build systems that can process and analyze data in real time as it flows through the system.

10. Cloud Platforms: Get familiar with cloud-based data engineering services offered by AWS, Azure, and Google Cloud. Understand how to use services like AWS S3, Azure Data Lake, Google Cloud Storage, AWS Redshift, and BigQuery for data storage, processing, and analysis.

11. Data Governance and Security: Learn best practices for data governance, including how to implement data quality checks, lineage tracking, and metadata management. Understand data security concepts like encryption, access control, and GDPR compliance to protect sensitive data.

12. Automation and Scripting: Be proficient in scripting languages like Python, Bash, or PowerShell to automate repetitive tasks, manage data pipelines, and perform ad-hoc data processing.

13. Data Versioning and Lineage: Understand the importance of data versioning and lineage for tracking changes to data over time. Learn how to use tools like Apache Atlas or DataHub for managing metadata and ensuring traceability in your data pipelines.

14. Containerization and Orchestration: Learn how to deploy and manage data engineering workloads using containerization tools like Docker and orchestration platforms like Kubernetes. Understand the benefits of using containers for scaling and maintaining consistency across environments.

15. Monitoring and Logging: Implement logging for data pipelines to ensure they run smoothly and efficiently. Familiarize yourself with tools like Prometheus, Grafana, etc. for real-time monitoring and troubleshooting.

Pyspark Interview Questions!!


Interviewer: "How would you remove duplicates from a large dataset in PySpark?"

Candidate: "To remove duplicates from a large dataset in PySpark, I would follow these steps:

Step 1: Load the dataset into a DataFrame

df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True)

Step 2: Check for duplicates

duplicate_count = df.count() - df.dropDuplicates().count()
print(f"Number of duplicates: {duplicate_count}")

Step 3: Partition the data to optimize performance
df_repartitioned = df.repartition(100)
Step 4: Remove duplicates using the dropDuplicates() method
df_no_duplicates = df_repartitioned.dropDuplicates()
Step 5: Cache the resulting DataFrame to avoid recomputing
df_no_duplicates.cache()
Step 6: Save the cleaned dataset

df_no_duplicates.write.csv("path/to/cleaned/data.csv", header=True)

Interviewer: "That's correct! Can you explain why you partitioned the data in Step 3?"

Candidate: "Yes, partitioning the data helps to distribute the computation across multiple nodes, making the process more efficient and scalable."

Interviewer: "Great answer! Can you also explain why you cached the resulting DataFrame in Step 5?"

Candidate: "Caching the DataFrame avoids recomputing the entire dataset when saving the cleaned data, which can significantly improve performance."

Interviewer: "Excellent! You have demonstrated a clear understanding of optimizing duplicate removal in PySpark."

Date: 18-03-2025
Company name: TCS
Role: Data Scientist

01. What is slicing in Python?

Answer-  Slicing is used to access parts of sequences like lists, tuples, and strings. The syntax of slicing is-[start:end:step]. The step can be omitted as well. When we write [start:end] this returns all the elements of the sequence from the start (inclusive) till the end-1 element. If the start or end element is negative i, it means the ith element from the end. The step indicates the jump or how many elements have to be skipped.

02.  What do you mean by ensemble learning?

Answer- Numerous models, such as classifiers are strategically made and combined to solve a specific computational program which is known as ensemble learning. The ensemble methods are also known as committee-based learning or learning multiple classifier systems. One of the most suitable examples of ensemble modeling is the random forest trees where several decision trees are used to predict outcomes.

03.  Why is the KNN Algorithm known as Lazy Learner?

Answer- When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.

04. What does this mean: *args, **kwargs in Python? And why would we use it?

Answer-  We use *args when we aren’t sure how many arguments are going to be passed to a function, or if we want to pass a stored list or tuple of arguments to a function. **kwargs is used when we don’t know how many keyword arguments will be passed to a function, or it can be used to pass the values of a dictionary as keyword arguments.

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Stay Safe & Happy Learning 💙

🚀 CI/CD & Deployment Strategies
✅ How do you set up end-to-end CI/CD automation for a microservices application?
✅ What are the key differences between self-hosted CI/CD solutions (e.g., Jenkins) and cloud-managed CI/CD services (e.g., GitHub Actions, GitLab CI/CD)?
✅ How do you implement progressive delivery using feature flags and canary deployments?
✅ What challenges do you face in deploying CI/CD pipelines across multi-cloud environments, and how do you solve them?

🚀 Infrastructure as Code (IaC) & Configuration Management
✅ How do you ensure idempotency in configuration management tools like Ansible or Chef?
✅ What are the best practices for state file management in Terraform?
✅ How do you structure Terraform modules for scalability and reusability?
✅ How would you use Ansible to dynamically configure infrastructure based on cloud metadata?

🚀 GitOps & Source Control
✅ How do you implement drift detection and auto-remediation in a GitOps workflow?
✅ What is the role of Helm vs. Kustomize in Kubernetes GitOps?
✅ How do you secure Git repositories and CI/CD pipelines to prevent supply chain attacks?
✅ Explain how signed commits and commit verification improve security in Git workflows.

🚀 Cloud Security & Compliance
✅ How do you implement Just-in-Time (JIT) access controls in a cloud environment?
✅ What are the differences between IAM roles, policies, and service accounts, and how do you use them effectively?
✅ How do you implement network segmentation and firewall rules in Kubernetes for security hardening?
✅ What security best practices do you follow when running serverless functions (AWS Lambda, Azure Functions, GCP Cloud Run)?

🚀 Scripting & Automation
✅ Write a Bash script to monitor disk usage and send alerts if usage exceeds 80%.
✅ How would you automate the cleanup of unused Docker images and volumes on a Kubernetes cluster?
✅ Explain how to use Python and AWS SDK (Boto3) to automate EC2 instance creation and tagging.
✅ How do you set up scheduled automation jobs in Kubernetes using CronJobs?

This interview round tested a deep understanding of automation, cloud infrastructure, CI/CD pipelines, and security best practices.