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Mindsprint
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I applied via Company Website and was interviewed before Aug 2022. There were 2 interview rounds.
Master data fields and tables store key information about entities in a system.
Fields typically include attributes like name, address, contact information, etc.
Tables store records of entities with unique identifiers and corresponding data.
Examples of master data tables include customer, product, employee, etc.
Vendor customer Materila views refer to the perspectives and opinions of the vendor's customers on the materials provided by the vendor.
Vendor customer Materila views can vary based on the quality, reliability, and cost-effectiveness of the materials supplied by the vendor.
Customers may appreciate materials that meet their specific requirements and standards, leading to positive views of the vendor.
On the other hand, i...
MRP stands for Material Requirements Planning, which is a system used to manage and plan the materials needed for production. BOM stands for Bill of Materials, which is a list of all the components needed to manufacture a product.
MRP helps in determining the quantity and timing of materials needed for production
BOM lists all the raw materials, components, and sub-assemblies required to manufacture a product
MRP and BOM ...
Top trending discussions
I applied via Naukri.com and was interviewed before Aug 2020. There were 4 interview rounds.
Some mcq questions and coding question. Both where easy to medium level. Prepare combination/permutation/Time complexity etc
A question related to Binary search and some other follow ups.
I applied via Job Portal and was interviewed before Feb 2023. There was 1 interview round.
I applied via Referral and was interviewed before Dec 2023. There were 2 interview rounds.
I applied via Job Fair and was interviewed in Sep 2022. There were 3 interview rounds.
Supervised machine learning is a type of ML where the algorithm learns from labeled data. Random forest is used over normal decision tree for better accuracy and to avoid overfitting.
Supervised ML learns from labeled data to make predictions on new data
Random forest is an ensemble learning method that uses multiple decision trees to improve accuracy
Random forest is preferred over normal decision tree to avoid overfitti...
Case study on a successful marketing campaign for a new product launch
Identified target audience and their needs
Developed a unique selling proposition
Created a multi-channel marketing plan
Implemented the plan and tracked results
Adjusted strategy based on data analysis
Achieved high sales and positive customer feedback
Retail case study , company trends salw.. Total sale. Profit charts and reports
I appeared for an interview before Mar 2024.
Technical Discussion
Technical Discussion with Coding Test
I worked as a project manager at a consulting firm, leading teams to deliver strategic solutions for clients across various industries.
Managed a team of 10 consultants to deliver a market entry strategy for a tech startup.
Conducted data analysis to identify key trends, resulting in a 20% increase in client revenue.
Facilitated workshops with clients to align project goals and expectations, enhancing client satisfaction.
...
I applied via Naukri.com and was interviewed before Nov 2023. There were 2 interview rounds.
Create a business requirement document
Strengths include strong analytical skills and attention to detail. Weaknesses may include difficulty with public speaking and time management.
Strengths: strong analytical skills
Strengths: attention to detail
Weaknesses: difficulty with public speaking
Weaknesses: time management
I applied via Campus Placement and was interviewed before Sep 2023. There was 1 interview round.
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
Random forest is a type of ensemble learning method.
It builds multiple decision trees during training.
Each tree is built using a subset of the training data and a random subset of features.
The final prediction is made by averaging the predictions of all the individual trees.
Random...
Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.
Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
Boosting is used to improve the accuracy of models and reduce bias and variance.
I applied via Naukri.com and was interviewed in Jun 2021. There were 3 interview rounds.
based on 1 interview
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