Microsoft – Data Scientist Interview Questions

Here is the list Data Scientist Interview Questions which are recently asked in Microsoft company. These questions are included for both Freshers and Experienced professionals. Our Data Science Training has Answered all the below Questions.

Interview Questions for Soft skills

Most interviews start with questions about your profile, projects, and goals. You should have good explanations for:

1. Tell me about yourself and your background (obviously)

Tell me about yourself

Start with an overview and things what make you unique.
Focus on the job and prior career highlights.
Provide examples and conclude with future goal.
About your academic background (university, academic achievements, etc.).

2. Talk about a time you worked in a team; conflicts if you had, how you managed a team

"I work very well with a team because I like the support of others. I find that I help others when they are struggling, and I appreciate when someone else can help me when I have a question in my project."

Describe your conflict and mention your role
Your way of approach and actions to solve the problem
Share the results with positive outcome

3. Tell us about a time you faced a challenge and how did you overcome that?

Explain the previous challenges and yours to overcome it.
Tell your answer to the job description.
Be specific and honest.
Make sure your answers are in a positive light and Use examples.

4. When have you multi-tasked? Can you tell us about key learning?

Multi-tasking is common in project development and every software professional have to multitask to finish the project by using the resources in the given time frame. The key learning from multi-tasking are,

Focus with full concentration.
Perform each task skillfully with complete cooperation.

5. How will you convince someone to do their work when running late on schedule?

Speak through your concerns over their lateness, present them with evidence and refer back to your company's policy on employee lateness. Explain that you want to understand what is causing their lateness and find out if there's something you could help with.

6. How would you explain Data Science to a layman?

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

7. Where do you see yourself in the next 5 years?

Tell clearly about your career goals and brainstorm for the next five years.
Explain the connections between your goals and the job description.

8. What are your expectations for the job?

“My expectations for the company would be to provide a work environment in which I can contribute to the team, I receive appreciation for my contributions, I have job stability and the ability to grow with the company.

9. Can you tell me about a time you had a lot on the plate? How did you handle that?

Explain the difficult time you faced in your life and the steps taken to handle it with examples. I took the small steps of improvement and work up withfocus on the positive. Be optimistic to handle it.

10. How do you work with someone you are not compatible with?

Stay Calm and have An Open Mind. Figure out Where You Stand and explain things by having a progressive, health conversation. Politely agree to disagree points if any.

11. Explain your largest failure at work. How did you learn from this experience?

Choose a specific real failure that happened in the workplace, specifically a failure related to the work you're doing now.
Share your story with focus on what you learned.

Free PDF : Get our updated Data Science Course Content pdf

Interview Questions for SQL

Was given a data and scenario: what kind of join would you use?

1. What are Entities and Relationships?

An entity is something that maintains a separate and distinct existence. A relationship type represents the association between entity types. For example,'Enrolled in' is a relationship type that exists between entity type Student and Course.

2. Have you used normalization in your queries?

Normalization is the process of minimizing redundancy from a relation or set of relations. The process of taking a database design, and apply a set of formal criteria and rules, is called Normal Forms.

3. How do you use the JOIN clauses?

To join clauses where there is no contrast or choice.
To join more than two clauses; the earlier clauses can be joined by a comma, but the last two must be joined by and.

4. Given a scenario, would you use DELETE or DROP?

Uses a lock	DELETE Table lock	DROP Table lock
Works in WHERE	Yes	No
Removes	One, some, or all rows in a table.	Entire table structure: data, privileges, indexes, constraints, triggers.
Resets ID auto-increment	No	Doesn't apply

5. Can you write a user-defined function in SQL?

CREATE FUNCTION [database_name.] function_name (parameters)
RETURNS data_type AS.
SQL statements.
RETURN value.
ALTER FUNCTION [database_name.] function_name (parameters)
RETURNS data_type AS.
SQL statements.
RETURN value.

6. What do you know about NoSQL?

The term “NoSQL” stands for “non SQL” that NoSQL databases are databases that store data in a format other than relational tables. NoSQL databases can store relationship data—they just store it differently than relational databases do.

7. What is the difference between SQL and NoSQL?

SQL databases are relational, NoSQL databases are non-relational. SQL databases use structured query language and have a predefined schema. NoSQL databases have dynamic schemas for unstructured data.

8. Can you talk about a time when you used SQL?

I have written the following when I used SQL

Select queries
INSERT, UPDATE and DELETE statements (including truncate where appropriate)
Temp tables.
Control flow statements (IF/ELSE)
CASE statements.

9. Do you have experience working with NoSQL?

Yes, I have worked with NoSQL to storing and modeling structured, semi-structured, and unstructured data in one database.The structure of many different forms of data is more easily handled and evolved with a NoSQL database.

10. What are OLAP and OLTP? Can you explain with examples?

Within the data science field, there are two types of data processing systems: online analytical processing (OLAP) and online transaction processing (OLTP). LAP products include IBM Cognos, Oracle OLAP and Oracle Essbase. Classic examples of OLTP systems are order entry, retail sales, and financial transaction systems.

11. Have you used stored procedures?

A Stored Procedure is a type of code in SQL that can be stored for later use and can be used many times. So, whenever you need to execute the query, instead of calling it you can just call the stored procedure.

12. How do you match patterns/characters in SQL?

SQL pattern matching enables to use _ to match any single character and % to match an arbitrary number of characters (including zero characters). In MySQL, SQL patterns are case-insensitive by default. Some examples are shown here. Do not use = or <> when you use SQL patterns.

13. Do you know about hash tables? Where do you use it?

A hash table is a data structure that is used to store keys/value pairs. It uses a hash function to compute an index into an array in which an element will be inserted or searched. By using a good hash function, hashing can work well.

14. How to measure query performance?

SQL Server Execution Times: CPU time = 422 ms, elapsed time = 2296 ms. It is a fairly accurate measure of how long that query took to run (and how long to parse and compile). The CPU time is how much CPU is used, the elapsed time is how much time the query took overall.

Interview Questions for Python

1. On a scale of 1–10, how comfortable are you with Python?

The interviewer ask this question to know how much do you know that you don’t know, so make sure to back your score with explanation on what you need to score higher. You can even throw it back to them and ask how your company would make me reach an 8 or 9.

2. How do you write functions in Python?

Begin the definition of a new function with def followed by the name of the function and follow the rules for variable names.
Then parameters in parentheses, colon.
Then an indented block of code.

3. Give some use cases for Pandas, NumPy.

NumPy provides various computing tools such as comprehensive mathematical functions, linear algebra routines. Pandas are used for handling, clearing of data, margining and joining of dataset, alignment and indexing.

4. What libraries do you frequently use?

Numpy.
Scipy.
Pandas

5. What are the local and global variables in Python?

Variables that are only referenced inside a function are implicitly global. If a variable is assigned a value anywhere within the function's body, it's assumed to be a local unless explicitly declared as global.

6. Mention five benefits of using Python?

Easy to Read, Interpreted Language.
Dynamically Typed with Improved Productivity.
Free and Open-Source with Vast Libraries Support.
Portability.

7. How do you create a dictionary in python?

The dictionary can be created by placing a sequence of elements within curly {} braces, separated by 'comma'. Dictionary holds a pair of values, one being the Key and the other corresponding pair element being its Key:value.

8. How will you convert a string to an int in python? (Data type conversion)

To convert a string to integer in Python, use the int() function. This function takes two parameters: the initial string and the optional base to represent the data. Use the syntax print(int("STR")) to return the str as an int , or integer.

9. Can you illustrate a few commands for data cleaning?

Data cleaning can be done using Numpy, Pandas using combining str methods, applymap function to clean entire data set, tidying up fields, renaming columns and skipping rows.

10. How can you get information about the data frame?

The info() function is used to print a concise summary of a DataFrame. This method prints information about a DataFrame including the index dtype and column dtypes, non-null values and memory usage.

11.What will be the output of the following code snippet? [I was given sample code]

Look into the code snippet carefully and write the output by taking each code step into consideration. Explain to the interviewer in detail with the output of the code.

12. Write a program to reverse a list (small code snip questions like this)

To reverse a list in Python, using the built-in reverse () or reversed () method to reverse the list without creating a new list. Reverse and reversed functions are used to reverse the elements in the original list object.

Interview Questions for Machine Learning

1. How do you decide what data attributes to consider for EDA?

Exploratory Data Analysis helps ensure that you choose the correct statistical techniques to analyze and forecast the data. The two basic types of EDA techniques are graphical techniques and quantitative techniques.

2. Can you think of problem statements where you can use supervised and unsupervised ML algorithms?

Some popular examples of supervised machine learning algorithms are: Linear regression for regression problems, random forest for classification. Some popular examples of unsupervised learning algorithms are: k-means for clustering problems, apriori algorithm for association rule learning problems.

3. For data visualization, what would be the first step you would do?

The first step in data visualization is to be clear on the question to be answered following by knowing the data as the next step.

4. Given a scenario, what would you prioritize: bias or variance?

Bias is an error between the actual values and the model's predicted values. Variance is also an error but from the model's sensitivity to the training data. A prioritization of Bias over Variance will lead to a model that overfits the data. Prioritizing Variance will have a model underfit the data.

5. How much data do you usually allocate for your training, validation, and test sets and why?

It is common to allocate 50 percent or more of the data to the training set, 25 percent to the test set, and the remainder to the validation set. Some training sets may contain only a few hundred observations; others may include millions.

6. Can you talk about a machine learning project you did recently?

Define the Problem that you worked with and explain how you prepared the Data.
Steps taken to evaluate Algorithms and to improve Results.
How you present Results.

7. What’s your approach to preparing data for analysis?

Access the data.
Ingest the data.
Cleanse the data.
Format the data.
Combine the data.
And finally, analyze the data.

8. How do you gauge the effectiveness of a machine learning model?

Accuracy: the proportion of the total number of predictions that was correct.
Positive Predictive Value or Precision: the proportion of positive cases that were correctly identified.
Negative Predictive Value: the proportion of negative cases that were correctly identified.

9. Do you have experience with Big Data tools for machine learning?

Big data analytics can make sense of the data by uncovering trends and patterns. Machine learning can accelerate this process with the help of decision-making algorithms. It can categorize the incoming data, recognize patterns and translate the data into insights helpful for business operations.

Book a Free Mock Interviews and Test your Data Science skills with our Experts

TOP MNC's DATA SCIENCE INTERVIEW QUESTIONS & ANSWERS

Here we listed all Data Science Interview Questions and Answers which are asked in Top MNCs. Periodically we update this page with recently asked Questions, please do visit our page often and be updated in Data Science.

Microsoft Data Scientist Interview Questions