Data Analyst Interview Resources

Jupyter Notebooks are essential for data analysts working with Python. Here’s how to make the most of this great tool: 1. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents. 2. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀: Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary. 3. 𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀: Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization. 𝟰. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗠𝗼𝗱𝘂𝗹𝗮𝗿: Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python scripts and import them when needed. 5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆: Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, titles, and legends to make them more informative. 6. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀: Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible. 7. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀: Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials. Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication. I have curated the best interview resources to crack Python Interviews 👇👇 https://topmate.io/analyst/907371 Hope you'll like it Like this post if you need more resources like this 👍❤️

𝗧𝗼𝗽 𝟱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 1)Data Science Foundations 2)SQL for Data Science 3)Python for Data Science 4)Introduction to Data Science 5)Data Science Projects  𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/4hDFv7E Enroll For FREE & Get Certified 🎓

Hey guys, Since many of you were asking me to send Free Sessions on Full Stack Development 📌So we have come with a FREE webinar for you!! 👨🏻‍💻 👩🏻‍💻 REGISTER HERE 👇👇 https://link.guvi.in/SQLspecialist01462 This will help you to speed up your job hunting process 💪 ENJOY LEARNING 👍👍

Scenario based Interview Questions & Answers for Data Analyst 1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer. Question: - Write a SQL query to find the total number of orders placed by each customer. Expected Answer: SELECT CustomerID, COUNT(*) AS TotalOrders FROM Orders GROUP BY CustomerID; 2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years. Question: - Write a SQL query to find the names of employees who have been with the company for more than 5 years. Expected Answer: SELECT Name FROM Employees WHERE DATEDIFF(year, HireDate, GETDATE()) > 5; Power BI Scenario-Based Questions 1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region. Expected Answer: - Load the dataset into Power BI. - Create relationships if necessary. - Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales). - Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart). - Use the "Filters" pane to filter data as needed. - Format the visualization to enhance clarity and readability. 2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API. Expected Answer: - Use Power BI Desktop to connect to the API. - Go to "Get Data" > "Web" and enter the API URL. - Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported). - Create visualizations using the imported data. - Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh. 3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application. Expected Answer: - Analyze the current performance using Performance Analyzer. - Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations. - Use aggregated tables to pre-compute results. - Simplify DAX calculations. - Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals. - Ensure proper indexing on the data source. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

𝗧𝗮𝘁𝗮 𝗚𝗿𝗼𝘂𝗽 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍 TCS plans to hire 40,000 trainees in 2025, here are these 3 virtual internships by Tata Group that you can take which will take roughly 4-6 hours to complete. After completing this internship you will get a free certificate that you can add in your resume which will help to increase your chances of getting hired.  𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/40Ej1MM Enroll For FREE & Get Certified 🎓

Microsoft is Hiring for Multiple Position in January 2025 Job Types: WFH/WFO/Remote * Internship / Fresher / Experience Get Free Referral: 👇 https://shorturl.at/ck6Jl (It's free of cost, don't pay any amount) It's better to apply through referrals to increase interview chances Like this post if you want me to post more referral opportunities ❤️ All the best 👍👍

1. What are the different subsets of SQL? Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects. Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database. Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions. 2. List the different types of relationships in SQL. There are different types of relations in the database: One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other. One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another. Many-to-Many – This is used when defining a relationship that requires several instances on each sides. Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ. 3. How to create empty tables with the same structure as another table? To create empty tables: Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active. 4. What is Normalization and what are the advantages of it? Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are: Better Database organization More Tables with smaller rows Efficient data access Greater Flexibility for Queries Quickly find the information Easier to implement Security

𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗶𝘁𝗶 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 😍 🚀 100% Free – No hidden costs, no application fees 📜 Get a Verified Certificate – Add it to your LinkedIn & Resume 🎓 Learn from Citi Experts – Industry-backed training 📊 Real-World Projects – Gain hands-on experience ⏳ Self-Paced Learning 𝐋𝐢𝐧𝐤👇 :-  https://pdlink.in/40SGpYf Enroll For FREE & Get Certified🎓

Data Analyst Interview questions ✅ Explain the Data Analysis Process: The data analysis process typically involves several key steps. These steps include: Data Collection: Gathering the relevant data from various sources. Data Cleaning: Removing inconsistencies, handling missing values, and ensuring data quality. Data Exploration: Using descriptive statistics, visualizations, and initial insights to understand the data. Data Transformation: Preprocessing, feature engineering, and data formatting. Data Modeling: Applying statistical or machine learning models to extract patterns or make predictions. Evaluation: Assessing the model's performance and validity. Interpretation: Drawing meaningful conclusions from the analysis. Communication: Presenting findings to stakeholders effectively. What is the Difference Between Descriptive and Inferential Statistics?: Descriptive statistics summarize and describe data, providing insights into its main characteristics. Examples include measures like mean, median, and standard deviation. Inferential statistics, on the other hand, involve making predictions or drawing conclusions about a population based on a sample of data. Hypothesis testing and confidence intervals are common inferential statistical techniques. How Do You Handle Missing Data in a Dataset?: Handling missing data is crucial for accurate analysis: I start by identifying the extent of missing data. For numerical data, I might impute missing values with the mean, median, or a predictive model. For categorical data, I often use mode imputation. If appropriate, I consider removing rows with too much missing data. I also explore if the missingness pattern itself holds valuable information. What is Exploratory Data Analysis (EDA)?: EDA is the process of visually and statistically exploring a dataset to understand its characteristics: I begin with summary statistics, histograms, and box plots to identify data trends. I create scatterplots and correlation matrices to understand relationships. Outlier detection and data distribution analysis are also part of EDA. The goal is to gain insights, identify patterns, and inform subsequent analysis steps. Give an Example of a Time When You Used Data Analysis to Solve a Real-World Problem: In a previous role, I worked for an e-commerce company, and we wanted to reduce shopping cart abandonment rates. I conducted a data analysis project: Collected user data, including browsing behavior, demographics, and purchase history. Cleaned and preprocessed the data. Explored the data through visualizations and statistical tests. Built a predictive model to identify factors contributing to cart abandonment. Found that longer page load times were a significant factor. Proposed optimizations to reduce load times, resulting in a 15% decrease in cart abandonment rates over a quarter. Hope it helps :)

Trump’s Limits of Control Are Beyond Normal Not only can the president freeze all funding amid a review, but he must also then be permitted to permanently eliminate items from appropriations statutes at a whim. It’s a move that threatens not only a radical curtailment of Congress’ authority but imperils the separation of American civil society from the partisan tides of the White House. The Constitution’s text is clear that Congress must authorize appropriations and the president must “take care” that those laws are “faithfully executed.” There is no basis in constitutional text or history for the president to claim open-ended power to impound funds in the manner of the OMB memo. Could the White House withhold relief funds before the election, and then give money to solely Republican-leaning districts? Imagine that the White House withdraws funding from every hospital in the country providing reproductive care and abortions. #OMB #constitution #impoudment 📱 American Оbserver - Stay up to date on all important events 🇺🇸

𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍 1) Generative AI 2) Big data artificial intelligence 3 ) Microsoft Al for beginners 4) Prompt Engineering for Chat GPT 𝐋𝐢𝐧𝐤👇 :-  https://pdlink.in/40Fbg9d Enroll For FREE & Get Certified🎓

Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources: 🗓️Week 1: Foundation of Data Analytics ◾Day 1-2: Basics of Data Analytics Resource: Khan Academy's Introduction to Statistics Focus Areas: Understand descriptive statistics, types of data, and data distributions. ◾Day 3-4: Excel for Data Analysis Resource: Microsoft Excel tutorials on YouTube or Excel Easy Focus Areas: Learn essential Excel functions for data manipulation and analysis. ◾Day 5-7: Introduction to Python for Data Analysis Resource: Codecademy's Python course or Google's Python Class Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas. 🗓️Week 2: Intermediate Data Analytics Skills ◾Day 8-10: Data Visualization Resource: Data Visualization with Matplotlib and Seaborn tutorials Focus Areas: Creating effective charts and graphs to communicate insights. ◾Day 11-12: Exploratory Data Analysis (EDA) Resource: Towards Data Science articles on EDA techniques Focus Areas: Techniques to summarize and explore datasets. ◾Day 13-14: SQL Fundamentals Resource: Mode Analytics SQL Tutorial or SQLZoo Focus Areas: Writing SQL queries for data manipulation. 🗓️Week 3: Advanced Techniques and Tools ◾Day 15-17: Machine Learning Basics Resource: Andrew Ng's Machine Learning course on Coursera Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics. ◾Day 18-20: Data Cleaning and Preprocessing Resource: Data Cleaning with Python by Packt Focus Areas: Techniques to handle missing data, outliers, and normalization. ◾Day 21-22: Introduction to Big Data Resource: Big Data University's courses on Hadoop and Spark Focus Areas: Basics of distributed computing and big data technologies. 🗓️Week 4: Projects and Practice ◾Day 23-25: Real-World Data Analytics Projects Resource: Kaggle datasets and competitions Focus Areas: Apply learned skills to solve practical problems. ◾Day 26-28: Online Webinars and Community Engagement Resource: Data Science meetups and webinars (Meetup.com, Eventbrite) Focus Areas: Networking and learning from industry experts. ◾Day 29-30: Portfolio Building and Review Activity: Create a GitHub repository showcasing projects and code Focus Areas: Present projects and skills effectively for job applications. 👉Additional Resources: Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus. Online Platforms: DataSimplifier, Kaggle, Towards Data Science Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!

𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗦𝗤𝗟😍 Whether you’re a beginner or looking to level up your SQL expertise, this roadmap will guide you through mastering SQL step by step✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3PTpsGY SQL is a must-have skill in data analytics and software development—master it, and unlock endless career opportunities!✅️

1. What is RDBMS? How is it different from DBMS? RDBMS stands for Relational Database Management System that stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables. 2.What is ETL in SQL? ETL stands for Extract, Transform and Load. It is a three-step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into the tidy format, which will come in the second phase.Finally, we would have to load this tidy data into tools which would help us to find insights. 3. What is a kernel function in SVM? In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable. 4. What do you understand by the F1 score? The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.

𝗚𝗲𝘁 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗜𝗻 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗡𝗩𝗜𝗗𝗜𝗔, 𝗮𝗻𝗱 𝗠𝗲𝘁𝗮 (𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸) 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝘀𝗲 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀😍 1️⃣ Amazon Interviewing Guide 2️⃣ Google Interview Tips 3️⃣ Microsoft Hiring Tips 4️⃣ NVIDIA Hiring Process 5️⃣ Meta Onsite SWE Prep Guide 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/40OSJJ6 Crack Interview & Get Your Dream Job In Top MNCs

Essential Python and SQL topics for data analysts 😄👇 Python Topics: Python Resources - @pythonanalyst 1. Data Structures    - Lists, Tuples, and Dictionaries    - NumPy Arrays for numerical data 2. Data Manipulation    - Pandas DataFrames for structured data    - Data Cleaning and Preprocessing techniques    - Data Transformation and Reshaping 3. Data Visualization    - Matplotlib for basic plotting    - Seaborn for statistical visualizations    - Plotly for interactive charts 4. Statistical Analysis    - Descriptive Statistics    - Hypothesis Testing    - Regression Analysis 5. Machine Learning    - Scikit-Learn for machine learning models    - Model Building, Training, and Evaluation    - Feature Engineering and Selection 6. Time Series Analysis    - Handling Time Series Data    - Time Series Forecasting    - Anomaly Detection 7. Python Fundamentals    - Control Flow (if statements, loops)    - Functions and Modular Code    - Exception Handling    - File SQL Topics: SQL Resources - @sqlanalyst 1. SQL Basics - SQL Syntax - SELECT Queries - Filters 2. Data Retrieval - Aggregation Functions (SUM, AVG, COUNT) - GROUP BY 3. Data Filtering - WHERE Clause - ORDER BY 4. Data Joins - JOIN Operations - Subqueries 5. Advanced SQL - Window Functions - Indexing - Performance Optimization 6. Database Management - Connecting to Databases - SQLAlchemy 7. Database Design - Data Types - Normalization Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work! Share with credits: https://t.me/sqlspecialist Hope it helps :)

☢️Nuclear War Alert ☢️ Large-Scale Nuclear Training Exercise to Take Place in Schenectady, New York As tensions between the United States and Europe over the Atlantic region escalate, US prepares for nuclear conflict. FBI:

“From January 26-31, 2025, a large-scale, multi-agency nuclear incident training exercise will take place in the vicinity of Schenectady, New York, and surrounding counties of Albany, Saratoga, and Schenectady. The exercise is an opportunity for participating entities to practice and enhance operational readiness to respond in the event of a nuclear incident in the United States or overseas. Due to the sensitive nature of the capabilities being implemented, the training activities are not open to the public or media.”

#War #nuclearexercises #US #Europe #Greenland #Arcticregion #nuclearproliferation 🇪🇺 Keep up with the latest Star Union News  🖥

Data Analytics Interview Preparation Part-2 [Questions with Answers] How did you get your job? I was hired after an internship. To get the internship, I prepared a bunch for general Python questions (LeetCode etc.) and studied the basics of machine learning (several different algorithms, how they work, when they're useful, metrics to measure their performance, how to train them in practice etc.). To get the internship I had to pass a technical interview as well as a take-home machine learning (ML) exercise. Then, it was just a question of doing a good job in the internship! What are your data related responsibilities in your job? I work on our recommendation system. It’s deep learning based. I work on a lot of features to try and improve it (reinforcement learning & NLP etc). Since I'm in a start-up, it's also up to our team to put the models we design into production. So, after a phase of research & development and model design, in notebooks, it's time to create a real pipeline, by creating scripts. This enables us to define, train, replace, compare and check the status of the models in production. It's basically all in Python, using Keras/TensorFlow, Pandas, Scikit-learn and NumPy. We also do a lot of analysis for the business team to help them compute metrics of interest (related to revenue, acquisition etc.). For that, we use an external utility called Metabase. It is is hooked up to our database where we write SQL queries and visualize the results and create dashboards (using Tableau/Looker etc). I would say my role is quite "full-stack" since we are all involved from the phase of R&D to deployment on our cluster. Was it difficult to get this role? I got hired after an internship. If you come from a scientific background, it's not that hard to transition into data science. All the math is something you will probably have seen already (especially if you're doing maths or physics). So, with some preparation and coding practice, you can start applying to internships. It took me maybe a month or two of preparation to get some basic ideas of the typical Python data stack (Pandas, Keras, SciKit-learn etc) before I started to send out CVs. Then, if you get an internship, try your best to do the best you can and then maybe you'll be hired after! I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

𝟱 𝗙𝗥𝗘𝗘 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Ready to dive into the world of Machine Learning? Here are 5 powerful resources that will guide you every step of the way—from beginner concepts to advanced techniques. 𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/40wyXk8 Enroll For FREE & Get Certified🎓

💥📚These SQL interview questions typically asked in a Data Analyst interview? 1.What distinguishes a Primary key from a Unique key? Primary key uniquely identifies each record in a table and cannot contain null values, whereas a Unique key also uniquely identifies records but can contain null values and multiple unique keys can exist in a table. 2. Define Candidate key. Candidate key is a key or set of keys that uniquely identifies each record in a table. It can be a combination of Primary and Alternate keys. 3.Explain the concept of Constraint in SQL. A Constraint is a specific rule or limit defined in a table to enforce data integrity. Examples include NOT NULL and AUTO INCREMENT. 4. Differentiate between TRUNCATE and DELETE commands. TRUNCATE is a DDL command that removes all data from a table while preserving the table's structure, and it is faster than DELETE. DELETE is a DML command that removes specific rows based on conditions and operates slower than TRUNCATE as it deletes data row by row. 5.Compare and contrast a 'View' and a 'Stored Procedure'. A View is a virtual table derived from one or more base tables, often used to simplify complex queries, while a Stored Procedure is a precompiled collection of SQL statements stored on the database server, used to perform specific tasks or operations. 6.What sets apart a Common Table Expression from a temporary table? A Common Table Expression (CTE) is a temporary result set defined within the execution scope of a single SELECT, DELETE, or UPDATE statement, while a temporary table is stored in TempDB and persists until the session ends. 7.Contrast a clustered index with a non-clustered index. A clustered index determines the physical ordering of data in a table and there can be only one clustered index per table. In contrast, a non-clustered index is similar to an index in a book where data is stored separately from the index, and multiple non-clustered indexes can exist for a table. 8.Define triggers in SQL and their purpose. Triggers are SQL codes that automatically execute in response to certain events on a table, such as INSERT, UPDATE, or DELETE operations. They are used to maintain data integrity and perform actions based on specific conditions. Here you can find essential SQL Interview Resources👇 https://topmate.io/analyst/864764 Hope it helps :)