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I applied via Campus Placement
Design a demand planning system for efficient forecasting and inventory management.
Utilize historical sales data to identify trends and seasonality
Incorporate external factors like market trends, promotions, and competitor activities
Implement machine learning algorithms for accurate demand forecasting
Integrate with inventory management systems for optimized stock levels
Regularly review and adjust the system based on pe
Implemented machine learning model to predict customer churn for a telecom company
Developed and trained a machine learning model using Python and scikit-learn
Utilized historical customer data to identify patterns and factors leading to churn
Evaluated model performance using metrics such as accuracy, precision, and recall
Provided actionable insights to the telecom company based on the model's predictions
Python Coding and ML concepts
Support colleague by offering assistance, understanding, and constructive feedback.
Offer emotional support and reassurance to help alleviate any stress or anxiety.
Provide constructive feedback in a respectful and non-judgmental manner.
Offer assistance in rectifying the mistake and finding a solution together.
Encourage open communication and a growth mindset to learn from the mistake.
Avoid blaming or shaming the colleag
It is difficult to estimate the exact number of sofas sold in a day in a city without specific data.
The number of sofas sold in a day can vary greatly depending on factors such as population size, economic conditions, and consumer preferences.
One way to estimate could be to look at the number of furniture stores in the city and their average daily sales.
Another approach could be to conduct a survey of a sample of furni...
ML algorithm's terminology refers to the specific vocabulary used to describe concepts, processes, and components in machine learning models.
Supervised learning: algorithms learn from labeled training data, e.g. linear regression, support vector machines
Unsupervised learning: algorithms find patterns in unlabeled data, e.g. clustering, dimensionality reduction
Feature engineering: process of selecting, transforming, and...
What people are saying about Accenture
I applied via Referral and was interviewed in Dec 2023. There were 2 interview rounds.
Boosting and bagging are ensemble learning techniques used to improve the performance of machine learning models.
Boosting focuses on improving the performance of a single model by training multiple models sequentially, where each subsequent model corrects the errors of its predecessor.
Bagging, on the other hand, involves training multiple models independently and then combining their predictions through averaging or vo...
Accenture interview questions for designations
I applied via LinkedIn and was interviewed before Dec 2023. There were 2 interview rounds.
I approach an end to end ML problem by understanding the problem, collecting data, preprocessing data, selecting a model, training the model, evaluating the model, and deploying the model.
Understand the problem and define the objective
Collect and preprocess data
Select a suitable machine learning model
Train the model using the data
Evaluate the model's performance
Deploy the model for production use
Evaluation techniques for machine learning algorithms include cross-validation, confusion matrix, ROC curve, and precision-recall curve.
Cross-validation: Splitting the data into multiple subsets for training and testing to assess model performance.
Confusion matrix: A table showing the true positive, true negative, false positive, and false negative predictions of a model.
ROC curve: Receiver Operating Characteristic cur...
Get interview-ready with Top Accenture Interview Questions
I applied via Company Website and was interviewed before Apr 2023. There were 2 interview rounds.
I applied via Approached by Company and was interviewed before Oct 2022. There were 4 interview rounds.
Devise a strategy for a coffee retail chain who is planning to enter a new market
I applied via Recruitment Consulltant and was interviewed before Oct 2022. There were 4 interview rounds.
Case study, past experience
I applied via Referral and was interviewed before Apr 2020. There were 3 interview rounds.
I applied via Approached by Company and was interviewed before May 2021. There were 4 interview rounds.
Online questions were based on scenarios to write SQL queries. Also got few questions on Python as well for which I had only limited knowledge.
The source row will be treated as an update, but the target object will be deleted.
The session level property 'Treat source row as Update' will be applied to the source row.
The target object will be deleted regardless of the update status of the source row.
This can result in data loss if the source row contains important information.
Dimension tables are used in data warehousing to provide descriptive information about the data in fact tables.
Slowly changing dimensions
Junk dimensions
Degenerate dimensions
Role-playing dimensions
Bridge dimensions
We used a relational schema in our previous project as it was suitable for the data structure and allowed for efficient querying.
Relational schema was used as it allowed for efficient querying of data
The data structure was suitable for a relational schema
We were able to easily join tables to retrieve necessary data
Examples include using SQL to query a database with multiple tables
Normalization was used to reduce data r
Joiner combines data from multiple sources based on a common key, while Lookup retrieves data from a reference table based on a matching key.
Joiner is used to combine data from two or more sources based on a common key column.
Lookup is used to retrieve data from a reference table based on a matching key column.
Joiner can perform inner, outer, left, and right joins, while Lookup can only perform an inner join.
Joiner can...
We used Git for version control in our previous project.
We created a Git repository for the project.
All team members were added as collaborators to the repository.
We followed the Git flow branching model.
We used pull requests for code review and merging.
We used tags to mark important releases.
We regularly pushed our changes to the remote repository.
We used Git commands like commit, push, pull, merge, and rebase.
We used...
I have worked on SCD Type 2 before.
SCD Type 2 is used to track historical changes in data.
It creates a new record for each change and maintains a history of changes.
It includes start and end dates for each record.
Example: Tracking changes in employee salary over time.
based on 8 interviews
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based on 49 reviews
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Gurgaon / Gurugram
4-8 Yrs
Not Disclosed
Gurgaon / Gurugram
5-10 Yrs
₹ 20-30.3456 LPA
Mumbai,
Hyderabad / Secunderabad
+15-10 Yrs
₹ 18.5-30.3456 LPA
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