AI-Mock-Interviewer/PDF_Extracted_Data huggingface dataset:
Question: 3. What is Physical Integrity?
Answer: Physical integrity refers to safeguarding data's completeness and correctness during storage and retrieval. Physical integrity is jeopardized when natural calamities hit, electricity goes out, or hackers interrupt database functionality . Data processing managers, system programmers, applications programmers, and internal auditors may be unable to access correct data due to human mistakes, storage degradation, and many other difficulties.
Question: 4. what is Logical Integrity?
Answer: In a relational database, logica l integrity ensures that data remains intact when utilized in various ways. Logical integrity , like physical integrity , protects data from human mistakes and hackers, but differently . Logic integrity may be divided into four categories.
Question: 5. Explain the Integrity of entities.
Answer: Entity integrity relies on generat ing primary keys to guarantee that data isn't shown more than once and that no field in a database is null. These unique values identify pieces of data. It's a characteristic of relational systems, which store data in tables that may be connected and used in many ways.
Question: 6. What is Referential Consistency?
Answer: The term "referential integrity" refers to a set of procedures that ensure that data is saved and utilized consistently . Only appropriate modifications, additions, or deletions of data are made, thanks to rules in the database's structure concerning how foreign keys are utilized. Rules may contain limits that prevent redundant data input, ensure proper data entry , and prohibit entering data that does not apply .
Question: 7. What is Domain Integrity?
Answer: Domain integrity is a set of operations that ensures that each piece of data in a domain is accurate. A domain is a set of permitted values that a column can hold in this context. Constraints and other measures that limit the format, kind, and amount of data submitted might be included.
Question: 8. User -defined integrity
Answer: User-defined integrity refers to the rules and limitations that users create to meet their requirements. When it comes to data security , entity , referential, and domain integrity aren't always adequate, and business rules must frequently be considered and included in data integrity safeguards.
Question: 9. What are the risks to data integrity?
Answer: The integrity of data recorded in a database can be affected for many reasons. The following are a few examples: Human error: Data integrity is jeopardized when people enter information erroneously , duplicate or delete data, fail to follow proper protocols, or make mistakes when implementing procedures designed to protect data. A transfer error occurs when data cannot be correctly transferred from one point in a database. In a relational database, transfer errors occur  when data is present in the destination table but not in the source table. Viruses and bugs: Spyware, malware, and viruses are programs that can infiltrate a computer and change, erase, or steal data. Sudden computer or server breakdowns, as well as issues with how a computer or other device performs, are instances of serious failures that might indicate that your hardware has been hacked. Compromise hardware might cause data to be rendered inaccurately or incompletely . Also, they might limit or reduce data access or make information dif ficult to utilize. The following steps can be taken to reduce or remove data integrity risks: Limiting data access and modifying permissions to prevent unauthorized parties from making changes to dataValidating data, both when it's collected and utilized, ensures that it's accurate. Using logs to track when data is added, edited, or removed is a good way to back up data. Internal audits are carried out regularly . Using software to spot errors.
Question: 1. What is a cursor in SQL Server?
Answer: A cursor is a database object that represents a result set and handles data one row at a time
Question: 2. How to utilize the Transact-SQL Cursor ?
Answer: Make a cursor declaration, Activate the cursor , Row by row, get the data. Deallocate cursor , Close cursor
Question: 3. Define the different sorts of cursor locks
Answer: There are three different types of locks. ONL Y READ: This stops the table from being updated.
Question: 4. Tips for cursor optimization
Answer: When not in use, close the pointer.
Question: 5. The cursor's disadvantages and limitations
Answer: Cursor consume s network resources by requiring a round-trip each time it pulls a record.
Question: 1. What constitutes good data visualization?
Answer: Use of color theory, Data positioning, Bars over circles and squares, Reducing chart junk by avoiding 3D charts and eliminating the use of pie charts to show proportions
Question: 2. How can you see more than three dimensions in a single chart?
Answer: Typically , data is shown in charts using height, width, and depth in pictures; however , to visualize more than three dimensions, we employ visual cuessuch as color , size, form, and animations to portray changes over time.
Question: 3. What processes are involved in the 3D Transformation of data visualization?
Answer: Data transformation in 3D is necessary because it provides a more comprehensive picture of the data and the ability to see it in more detail. The overall procedure is as follows: ●Viewing Transformation ●Workstation Transformation ●Modeling Transformation ●Projection Transformation
Question: 4. What is the definition of Row-Level Security?
Answer: Row-level security limits the data a person can see and access based on their access filters. Depending on the visualization tool being used, users can specify row-level security . Several prominent visualization technologies, including Qlik, Tableau, and Power BI, are available.
Question: 5. What Is Visualization “Depth Cueing”?
Answer: Depth cueing is a fundamental challenge in vision approaches. Some 3D objects lack visual line and surface identification due to a lack of depth information. To draw attention to the visible lines, draw them as dashed lines and delete the unseen ones.
Question: 6. Explain Surface Rendering in Visualization?
Answer: Lightening conditions in the screen, Degree of transparency, Assigned characteristics, Exploded and cutaway views, How rough or smooth the surfaces are to be, Three dimensional and stereoscopic views
Question: 7. What is Informational Visualization?
Answer: Information visualization focused on computer-assisted tools to study huge amounts of abstract data. The User Interface Research Group at Xerox PARC, which includes Dr. Jock Mackinlay , was the first to develop the phrase "information visualization." Selecting, manipulating, and showing abstract data in a way that allows human engagement for exploration and comprehension is a practical use of information visualization in computer applications. The dynamics of visual representation and interaction are important features of information visualization. Strong approaches allow the user to make real-time changes to the display , allowing for unequaled observation of patterns and structural relationships in abstract data.
Question: 8. What are the benefits of using Electrostatic Plotters?
Answer: They outperform pen plotters and high-end printers in terms of speed and quality . A scan-conversion feature is now available on several electrostatic plotters. There are color electrostatic plotters on the market, and they make numerous passes over the page to plot color images.
Question: 9. What is Pixel Phasing?
Answer: Pixel phasing is an antialiasing method that smooths out stair steps by shifting the electron beam closer to the places defined by the object shape.
Question: 10. Define Perspective Projection
Answer: This is accomplished by projecting points to the display plane and conver ging points. As a result, items further away from the viewing point should be smaller than those present here.
Question: 11. Explain winding numbers in visualization
Answer: The winding number approach determines whether a particular point is inside or outside the polygon. This approach gives all the edges that cross the scan line a direction number . If the edge begins below the line and finishes above the scan line, the direction should be -1; otherwise, it shouldbe 1. When the value of the winding number is nonzero, the point is considered to be inside polygons or two-dimensional objects.
Question: 12. What is Parallel Projection?
Answer: Parallel projection is the process of creating a 2D representation of a 3D scene—project points from the object's surface along parallel lines on the display plane. Different 2D perspectives of things may be created by projecting the visible spots.
Question: 13. What is a blobby object?
Answer: Some objects may not retain a constant form but instead vary their surface features in response to particular motions or close contact with other objects. Molecular structures and water droplets are two examples of blobby objects.
Question: 14. What is Non-Emissive?
Answer: They are optical effects that turn light from any source into pictorial forms, such as sunshine. A good example is the liquid crystal display .
Question: 15. What is Emissive?
Answer: Electrical energy is converted into light energy by the emissive display . Examples include plasma screens and thin film electroluminescent displays.
Question: 16. What is Scan Code?
Answer: When a key is pushed on the keyboard, the keyboard controller stores a code corresponding to the pressed key in the keyboard buffer, which is a section of memory . The scan code is the name given to this code.
Question: 17. What is the difference between a window port and a viewport?
Answer: A window port refers to a section of an image that a window will display . The view port is the display area of the selected portion of the form in which the selected component is displayed.
Question: 1. What is the distinction between deep learning and machine learning?
Question: 2. Give a detailed explanation of the Decision Tree algorithm
Question: 3. What exactly is sampling? How many different sampling techniques are you familiar with?
Question: 4. What is the distinction between a type I and a type II error?
Question: 5. What  is the definition of linear regression? What are the definitions of the words p-value, coefficient, and r-squared value? What are the functions of each of these elements?
Question: 6. What is statistical interaction?
Question: 7. What is selection bias?
Question: 8. What does a data set with a non-Gaussian distribution look like?
Question: 9. What is the Binomial Probability Formula, and how does it work?
Question: 10. What distinguishes k-NN clustering from k-means clustering?
Question: 11. What steps would you take to build a logistic regression model?
Question: 12. Explain the 80/20 rule and its significance in model validation.
Question: 13. Explain the concepts of accuracy and recall. What is their relationship to the ROC curve?
Question: 14. Distinguish between the L1 and L2 regularization approaches.
Question: 15. What is root cause analysis, and how does it work?
Question: 16. What is hash table collisions?
Question: 17. Before implementing machine learning algorithms, what are some procedures for data wrangling and cleaning?
Question: 18. What is the difference between a histogram and a box plot?
Question: 19. What is cross-validation, and how does it work?
Question: 20. Define the terms "false-positive" and "false-negative." Is it preferable to have a large number of false positives or a lar ge number of false negatives?
Question: 21. In your opinion, which is essential, model performance or accuracy , when constructing a machine learning model?
Question: 22. What are some examples of scenarios in which a general linear model fails?
Question: 23. Do you believe that 50 little decision trees are preferable to a single huge one? Why?
Question: 1. What are the most important data scientist tools and technical skills?
Answer: Because data science is such a sophisticated profession, you'll want to demonstrate to the hiring manager that you're familiar with all of the most up-to-date industry-standard tools, software, and programming languages. Data scientists typically use R and Python among the different statistical programming languages used in data research. Both may be used for statistical tasks, including building a nonlinear or linear model, regression analysis, statisti cal testing, data mining, and so on. RStudio Server is another essential data science application, whereas Jupyter Notebook is frequently used for statistical modelling, data visualizations, and machine learning functions, among other things. Tableau, PowerBI, Bokeh, Plotly , and Infogram are just a few of the dedicated data visualization tools that Data Scientists use frequently . Data scientists must also have a lot of SQL and Excel skills.“Any specific equipment or technical skills required for the position you're interviewing for should also be included in your response. Examine the job description, and if there are any tools or applications you haven't used before, it's a good idea to familiarize yourself with them before the interview .”
Question: 2. How should outlier values be treated?
Answer: Outliers can be eliminated in some cases. You can remove garbage values or values that you know aren't true. Outliers with extreme values that differ significantly from the rest of the data points in a collection can also be deleted. Suppose you can't get rid of outliers. In that case, you may rethink whether you choose the prope r model, employ methods (such random forests) that aren't as affected by outlier values, or attempt normalizing your data.
Question: 3. Tell me about a unique algorithm you came up with.
Question: 4. What are some of the advantages and disadvantages of your preferred statistics software?
Question: 5. Descr ibe a data science project where you had to work with a lot of code. What did you take away from the experience?
Question: 6. How would you use five dimensions to portray data properly?
Question: 7. Assume using multiple regression to create a predictive model. Describe how you plan to test this model.
Question: 9. How do you know that your modifications are better than doing nothingwhile updating an algorithm?
Question: 10. What would  you do if you had an unbalanced data set for prediction (i.e., many more negative classes than positive classes)?
Question: 11. How  would you validate a model you constructed using multiple regression to produce a predictive model of a quantitative outcomevariable?
Question: 12. I have two equivalent model s of accuracy and processing power. Which one should I use for production, and why should I do so?
Question: 12. I have two equivalent model s of accuracy and processing power. Which missing values. What are your plans for dealing with them?
Question: 1. What qualities do you believe a competent Data Scientist should possess?
Answer: Your response to this question will reveal a lot about how you view your position and the value you offer to a company to a hiring mana ger. In your response, you might discuss how data science necessitates a unique set of competencies and skills. A skilled Data Scientist must be able to combine technical skills like parsing data and creating models with business sense like understanding the challenges they're dealing with and recognizing actionable insights in their data. Y ou might also include a Data Scientist you like in your response, whether it's a colleague you know or an influential industry figure.
Question: 3. What are some of your strengths and weaknesses?
Question: 4. Which data scientist do you aspire to be the most like?
Question: 5. What attracted you to data science in the first place?
Question: 6. What unique skills do you believe you can provide to the team?
Question: 7. What made you leave your last job?
Question: 8. What sort of compensation/pay do you expect?
Question: 9. Give a few instances of data science best practices.
Question: 10. What data science project at our organization would you want to work on?
Question: 11. Do you like to work alone or in a group of Data Scientists?
Question: 12. In five years, where do you see yourself?
Question: 13. How do you deal with tense situations?
Question: 14. What inspires and motivates you?
Question: 15. What criteria do you use to determine success?
Question: 16. What kind of work atmosphere do you want to be in?
Question: 17. What do you enjoy doing outside of data science?
Question: 1. Tell me about a time when you were a multi-discipl inary team member.
Answer: A Data Scientist  works with a diverse group of people in technical and non- technical capacities. Working with developers, designers, product experts, data analysts, sales and market ing teams, and top-level executives, not to mention clients, is not unusual for a Data Scientist. So, in your response tothis question, show that you're a team player who enjoys the opportunity to meet and interact with people from other departments. Choose a scenario in which you reported to the company's highest-ranking officials to demonstrate not just that you can communicate with anybody but also how important your data-driven insights have been in the past.
Question: 2. Could you tell me about a moment when you used your leadership skills on the job?
Question: 3. What steps do you use to resolve a conflict?
Question: 4. What method do you like to use to establish rapport with others?
Question: 5. Discuss a successful presentation you delivered and why you believe it went well.
Question: 6. How would you communicate  a complex technical issue to a colleague orclient who is less technical?
Question: 7. Describe a situation in which you had to be cautious when discussing sensitive information. How did you pull it of f?
Question: 8. On a scale of 1 to 10, how good are your communication skills? Give instances of situations that prove the rating is correct.
Answer: Extras
Question: 1. Tell me about a moment when you were tasked with cleaning and organizing a large data collection
Answer: According to studies, Data Scientists spend most of their time on data preparation rather than data mining or modelling. As a resul t, if you've worked as a Data Scientist before, you've almost certainly cleaned and organized a large data collection. It's also true that this is a job that just a few individuals  like. However , data cleaning is one of the most crucial processes. As a result, you should walk the hiring manager through your data preparation process, including deleting duplicate observations, correcting structural problems, filtering outliers, dealing with missing data, and validating data.
Question: 2. Tell me about a data project you worked on and met a difficulty . What was your reaction?
Question: 3. Have  you gone above and beyond your normal responsibil ities? If so, how would you go about doing it?
Question: 4. Tell me about a period when you were unsuccessful and what you learned from it.
Question: 5. How have you used data to improve a customer's or stakeholder's experience?
Question: 6. Give me an example of a goal you've attained and how you got there.
Question: 7. Give an example of a goal you didn't achieve and how you dealt with it.
Question: 8. What strategies did you use to meet a tight deadline?
Question: 9. Tell me about an instance when you successfully settled a disagreement
Question: 1. What's the difference between support vector machines and logistic regression? What is an example of when you would choose to use one overthe other?
Question: 2. What is the integral representation of a ROC area under the curve?
Question: 3. A disc is spinning on a spind le, and you don’t know which direction the disc is spinning. A set of pins is given to you. How will you utilize the pins to show how the disc is spinning?
Question: 3. What  would you do if you discovered that eliminating missing values from a dataset resulted in bias?
Question: 4. What metrics would you consider when addressing queries about a product's health, growth, or engagement?
Question: 5. When attempting to address business difficulties with our product, what metrics would you consider?
Question: 6. How would you know whether a product is performing well or not?
Question: 7. What is the best way to tell if a new observation is an outlier? What is the difference between a bias-variance trade-off?
Question: 8. Discuss how to randomly choose a sample of a product's users.
Question: 9. Before using machine learning algorithms, explain the data wrangling and cleaning methods.
Question: 10. How would you deal with a binary classification that isn't balanced?
Question: 11. What makes a good data visualization different from a bad one?
Question: 12. What's the best way to find percentiles? Write the code for it.
Question: 13. Make a function that determines whether a word is a palindrome.
Question: why do you want to work in this company?
Question: do you consider yourself successful?
Question: are you willing to travel?
Question: what are your salary expectations?
Question: what would you consider your greatest strengths?
Question: what would you consider your greatest weakness?
Question: what motivates you?
Question: why did you leave your last job?
Question: what experience do you have in this field?
Question: what do coworkers say about you?
Question: why should we hire you?
Question: are you a team player?
Question: what is your philosophy towards work?
Question: what have you learned from mistakes on the job?
Question: how would you know you were successful on this job?
Answer: Being successful means goals that are set are met. Being successful also means standards are not only reach, but also even exceeded wherever possible .
Question: are you willing to work overtime nights weekends?
Question: what will you do if you dont get this position?
Question: what have you done to improve your knowledge in the last year?
Question: how you would be an asset to this company?
Question: how long would you expect to work for us in case you are hired?
Question: why do you think you would do well at this job?
Question: what irritates you about coworkers?
Question: do your skills match this job or another job more closely?
Question: what has disappointed you about a job?
Question: if you were hiring a person for this job what would you look for?
Question: what role do you tend to play in a team?
Question: what was the most difficult decision for you made?
Question: are you willing to make sacrifices for this company?
Question: what qualities do you look for in a boss?
Question: are you applying in other companies as well?
Question: do you know anyone who works in our company?
Question: how do you propose to compensate for your lack of experience?
Question: have you ever worked in a job that you hated?
Question: what would your previous supervisor say your strongest point is?
Answer: Some of my strongest points at work are being hardworking, patient and a quick learner.
Question: what is the most difficult thing about working with you?
Question: what suggestions have you made in your previous employment that was implemented?
Question: would you rather be liked or feared?
Question: how do you cope with stress?
Question: would you rather work for money or job satisfaction?
Question: what was your biggest challenge with your previous boss?
Question: do you enjoy working as part of a team?
Question: has anything ever irritated you about people youve worked with?
Question: do you have any questions for me?
Question: what is the sql server query execution sequence?
Question: what is normalization?
Question: what are the three degrees of normalization and how is normalization done in each degree?
Question: what are the different database objects?
Question: what is collation?
Question: what is a constraint and what are the seven constraints?
Question: what is a surrogate key?
Question: and how can it be improved?
Answer: The Derived Column a  new column that is generated on the fly by applying expressions to transformation input columns. Ex: FirstName +   + LastName AS Full name Derived column affect the performances of the data base due to the creation of a temporary new column. Executio n plan can save the new column to have better performance next time.