Date: 04-03-2025
Company name: Capgemini
Role: Data Scientist
01. What is the meaning of KPI in statistics?
Answer- KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.
02.What is the concept of graph neural networks in deep learning?
Answer- Graph neural networks are designed to operate on structured data represented as
graphs. They can capture relational dependencies and propagate information across
nodes and edges in a graph. Graph neural networks have been successfully applied to
tasks such as social network analysis, recommendation systems, and molecular chemistry.
03. What is the concept of autoencoders in deep learning?
Answer- Autoencoders are neural networks trained to reconstruct their input data. They consist of
an encoder network that maps the input data to a lower-dimensional representation
(latent space) and a decoder network that reconstructs the input from the latent space.
Autoencoders are used for dimensionality reduction, feature learning, and anomaly
Detection.
04.What is the concept of learning rate in deep learning?
Answer- The learning rate determines the step size at which the weights are updated during
training. Choosing an appropriate learning rate is important, as a small value may result
in slow convergence, while a large value may cause unstable training or overshooting
the optimal solution.
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Company name: Capgemini
Role: Data Scientist
01. What is the meaning of KPI in statistics?
Answer- KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.
02.What is the concept of graph neural networks in deep learning?
Answer- Graph neural networks are designed to operate on structured data represented as
graphs. They can capture relational dependencies and propagate information across
nodes and edges in a graph. Graph neural networks have been successfully applied to
tasks such as social network analysis, recommendation systems, and molecular chemistry.
03. What is the concept of autoencoders in deep learning?
Answer- Autoencoders are neural networks trained to reconstruct their input data. They consist of
an encoder network that maps the input data to a lower-dimensional representation
(latent space) and a decoder network that reconstructs the input from the latent space.
Autoencoders are used for dimensionality reduction, feature learning, and anomaly
Detection.
04.What is the concept of learning rate in deep learning?
Answer- The learning rate determines the step size at which the weights are updated during
training. Choosing an appropriate learning rate is important, as a small value may result
in slow convergence, while a large value may cause unstable training or overshooting
the optimal solution.
————————————————————
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Date: 06-03-2025
Company name: IBM
Role: Data Scientist
01. How to create empty tables with the same structure as another table?
Answer- 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.
02.Explain Correlation and Covariance?
Answer- Correlation is used for measuring and also for estimating the quantitative relationship between two variables. Correlation measures how strongly two variables are related. Examples like, income and expenditure, demand and supply, etc.
Covariance is a simple way to measure the correlation between two variables. The problem with covariance is that they are hard to compare without normalization.
03. What’s the difference between a generative and discriminative model?
Answer- A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
04.When does regularization become necessary in Machine Learning?
Answer- Regularization is necessary whenever the model begins to overfit/ underfit. It is a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and reduce cost term. It helps to reduce model complexity so that the model can become better at predicting (generalizing).
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Company name: IBM
Role: Data Scientist
01. How to create empty tables with the same structure as another table?
Answer- 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.
02.Explain Correlation and Covariance?
Answer- Correlation is used for measuring and also for estimating the quantitative relationship between two variables. Correlation measures how strongly two variables are related. Examples like, income and expenditure, demand and supply, etc.
Covariance is a simple way to measure the correlation between two variables. The problem with covariance is that they are hard to compare without normalization.
03. What’s the difference between a generative and discriminative model?
Answer- A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
04.When does regularization become necessary in Machine Learning?
Answer- Regularization is necessary whenever the model begins to overfit/ underfit. It is a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and reduce cost term. It helps to reduce model complexity so that the model can become better at predicting (generalizing).
————————————————————
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Date: 11-03-2025
Company name: Blinkit
Role: Data Scientist
01. What is Vectorization in Python pandas?
Answer- The procedure of executing operations on the full array is known as vectorization. This is intended to limit the number of iterations that the methods do. Pandas have a series of vectorized methods, such as string functions and aggregations, that are optimized for use with series and dataframes.
02.What do you understand by the F1 score?
Answer- 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.
03. Does SQL support programming language features?
Answer- It is true that SQL is a language, but it does not support programming as it is not a programming language, it is a command language. We do not have conditional statements in SQL like for loops or if..else, we only have commands which we can use to query, update, delete, etc. data in the database. SQL allows us to manipulate data in a database.
04. What is a trigger?
Answer- Trigger is a statement that a system executes automatically when there is any modification to the database. In a trigger, we first specify when the trigger is to be executed and then the action to be performed when the trigger executes. Triggers are used to specify certain integrity constraints and referential constraints that cannot be specified using the constraint mechanism of SQL.
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Company name: Blinkit
Role: Data Scientist
01. What is Vectorization in Python pandas?
Answer- The procedure of executing operations on the full array is known as vectorization. This is intended to limit the number of iterations that the methods do. Pandas have a series of vectorized methods, such as string functions and aggregations, that are optimized for use with series and dataframes.
02.What do you understand by the F1 score?
Answer- 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.
03. Does SQL support programming language features?
Answer- It is true that SQL is a language, but it does not support programming as it is not a programming language, it is a command language. We do not have conditional statements in SQL like for loops or if..else, we only have commands which we can use to query, update, delete, etc. data in the database. SQL allows us to manipulate data in a database.
04. What is a trigger?
Answer- Trigger is a statement that a system executes automatically when there is any modification to the database. In a trigger, we first specify when the trigger is to be executed and then the action to be performed when the trigger executes. Triggers are used to specify certain integrity constraints and referential constraints that cannot be specified using the constraint mechanism of SQL.
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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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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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Role: Data Protection Analyst
Exp: 1+ Years
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Role: Data Scientist Computer Vision
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Company : SkillFied Mentor
Role: Data Analyst Intern
Exp: Fresher
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Exp: Fresher
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Exp: 1+ Years
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Exp: 2+ years
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Exp: 2+ Years
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Exp: 2+ Years
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Date: 02-04-2025
Company name: Accenture
Role: Data Scientist
01. What is a UNIQUE constraint?
Answer- The UNIQUE Constraint prevents identical values in a column from appearing in two records. The UNIQUE constraint guarantees that every value in a column is unique.
02.What is schema in SQL Server?
Answer- A schema is a visual representation of the database that is logical. It builds and specifies the relationships among the database’s numerous entities. It refers to the several kinds of constraints that may be applied to a database. It also describes the various data kinds. It may also be used on Tables and Views. Schemas come in a variety of shapes and sizes. Star schema and Snowflake schema are two of the most popular. The entities in a star schema are represented in a star form, whereas those in a snowflake schema are shown in a snowflake shape. Any database architecture is built on the foundation of schemas.
03. What constraints come under the primary key constraint?
Answer- The PRIMARY KEY constraint uniquely identifies each record in a table. Primary keys must contain UNIQUE values, and cannot contain NULL values. A table can have only ONE primary key; and in the table, this primary key can consist of single or multiple columns (fields). Hence the unique constraint and the not null constraint come under the primary key by default.
04. Explain Grant and Revoke in SQL.
Answer- SQL Grant command is specifically used to provide privileges to database objects for a user. This command also allows users to grant permissions to other users too. Revoke command withdraw user privileges on database objects if any granted. It does operations opposite to the Grant command. When a privilege is revoked from a particular user U, then the privileges granted to all other users by user U will be revoked.
————————————————————
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Company name: Accenture
Role: Data Scientist
01. What is a UNIQUE constraint?
Answer- The UNIQUE Constraint prevents identical values in a column from appearing in two records. The UNIQUE constraint guarantees that every value in a column is unique.
02.What is schema in SQL Server?
Answer- A schema is a visual representation of the database that is logical. It builds and specifies the relationships among the database’s numerous entities. It refers to the several kinds of constraints that may be applied to a database. It also describes the various data kinds. It may also be used on Tables and Views. Schemas come in a variety of shapes and sizes. Star schema and Snowflake schema are two of the most popular. The entities in a star schema are represented in a star form, whereas those in a snowflake schema are shown in a snowflake shape. Any database architecture is built on the foundation of schemas.
03. What constraints come under the primary key constraint?
Answer- The PRIMARY KEY constraint uniquely identifies each record in a table. Primary keys must contain UNIQUE values, and cannot contain NULL values. A table can have only ONE primary key; and in the table, this primary key can consist of single or multiple columns (fields). Hence the unique constraint and the not null constraint come under the primary key by default.
04. Explain Grant and Revoke in SQL.
Answer- SQL Grant command is specifically used to provide privileges to database objects for a user. This command also allows users to grant permissions to other users too. Revoke command withdraw user privileges on database objects if any granted. It does operations opposite to the Grant command. When a privilege is revoked from a particular user U, then the privileges granted to all other users by user U will be revoked.
————————————————————
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🚀 CloudyML x Cognizance x IIT Roorkee—where future tech enthusiasts explored data science, AI, Digital Marketing and career growth like never before! 💡📈🤖
As the Education Partner for IIT Roorkee’s annual tech fest Cognizance, CloudyML got the chance to connect with students and offer exciting upskilling opportunities in the data field. 📚💼✨
🚀 CloudyML x Cognizance x IIT Roorkee—where future tech enthusiasts explored data science, AI, Digital Marketing and career growth like never before! 💡📈🤖
As the Education Partner for IIT Roorkee’s annual tech fest Cognizance, CloudyML got the chance to connect with students and offer exciting upskilling opportunities in the data field. 📚💼✨
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CloudyML as Education Partner at Cognizance '25 | IIT Roorkee Highlights #iitroorkee #cloudyml
CloudyML x Cognizance x IIT Roorkee—where future tech enthusiasts explored data science, AI, Digital Marketing and career growth like never before! Music Use...
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