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Wipro Cognos Tm1 Developer Interview Questions and Answers
Wipro Cognos Tm1 Developer Interview Experiences
1 interview found
I applied via Recruitment Consulltant and was interviewed in Dec 2024. There was 1 interview round.
(2 Questions)
- Q1. How you have developed the planning and forecasting porject
- Ans.
I have developed planning and forecasting projects by collaborating with stakeholders, analyzing data, creating models, and implementing TM1 solutions.
Collaborated with stakeholders to understand business requirements
Analyzed historical data to identify trends and patterns
Created TM1 models to support planning and forecasting processes
Implemented TM1 solutions to automate budgeting and forecasting tasks
- Q2. How to you approach user access management in tm1
- Ans.
User access management in TM1 involves setting up security roles, assigning permissions, and monitoring user activity.
Create security roles based on user responsibilities
Assign appropriate permissions to each role
Regularly review and update user access to ensure compliance
Monitor user activity to detect any unauthorized access
Utilize TM1's security features such as element security and cube security
Interview questions from similar companies
Selected
I applied via Naukri.com and was interviewed before Oct 2023. There were 2 interview rounds.
Basic TI and rule. With some case study.
(2 Questions)
- Q1. Agile methodologies, how many projects worked before. Role and responsibilities.
- Ans.
I have worked on 3 Agile projects as a Cognos TM1 Developer. My role involved developing and maintaining TM1 models, collaborating with stakeholders, and ensuring project timelines were met.
Worked on 3 Agile projects as a Cognos TM1 Developer
Developed and maintained TM1 models
Collaborated with stakeholders to gather requirements
Ensured project timelines were met
- Q2. Knowledge check on TM1 rule and TI functions.
Interview Preparation Tips
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posted on 24 Aug 2024
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- Q1. All genaral questions
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(1 Question)
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Selected
I applied via Naukri.com and was interviewed before Oct 2023. There were 2 interview rounds.
Basic TI and rule. With some case study.
(2 Questions)
- Q1. Agile methodologies, how many projects worked before. Role and responsibilities.
- Ans.
I have worked on 3 Agile projects as a Cognos TM1 Developer. My role involved developing and maintaining TM1 models, collaborating with stakeholders, and ensuring project timelines were met.
Worked on 3 Agile projects as a Cognos TM1 Developer
Developed and maintained TM1 models
Collaborated with stakeholders to gather requirements
Ensured project timelines were met
- Q2. Knowledge check on TM1 rule and TI functions.
Interview Preparation Tips
(1 Question)
- Q1. What is difference between master detail and drill through defination
- Ans.
Master detail and drill through are two different techniques used in Cognos reporting.
Master detail is used to display data in a hierarchical manner, where the details are shown based on a selected master item.
Drill through allows users to navigate from summary information to detailed information by clicking on a specific data point.
Master detail is typically used for displaying related data in a structured format, whi...
Interview Preparation Tips
I applied via Approached by Company and was interviewed in Mar 2021. There were 3 interview rounds.
Interview Questionnaire
2 Questions
- Q1. Why are you looking for a job change?
- Q2. Cognos related questions
Interview Preparation Tips
I applied via campus placement at Indian Institute of Technology (IIT), Chennai and was interviewed in Dec 2016. There were 5 interview rounds.
Interview Questionnaire
8 Questions
- Q1. What do you know about the company and the profile ?
- Ans.
The company is a data-driven organization that provides cognitive solutions to businesses.
The company specializes in cognitive solutions.
They use data to provide insights to businesses.
Their focus is on helping businesses make better decisions.
They have a team of data scientists who work on developing these solutions.
- Q2. Which programming language are you familiar with ? Do you know R ?
- Ans.
Yes, I am familiar with R.
I have experience in data analysis and visualization using R.
I have used R for statistical modeling and machine learning.
I am comfortable with R packages such as ggplot2, dplyr, and tidyr.
- Q3. What is Machine Learning ?
- Ans.
Machine learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data.
Machine learning involves using algorithms to learn patterns in data
It can be supervised, unsupervised, or semi-supervised
Examples include image recognition, natural language processing, and recommendation systems
- Q4. Is it true that statistical models and Machine Learning are the same ?
- Ans.
No, statistical models and Machine Learning are not the same.
Statistical models are based on mathematical equations and assumptions, while Machine Learning uses algorithms to learn patterns from data.
Statistical models require a priori knowledge of the data distribution, while Machine Learning can handle complex and unstructured data.
Statistical models are often used for hypothesis testing and parameter estimation, whi...
- Q5. What makes you special to this profile ?
- Ans.
My expertise in machine learning and data analysis combined with my strong cognitive psychology background makes me a unique fit for this role.
Strong background in cognitive psychology
Expertise in machine learning and data analysis
Experience in developing and implementing cognitive models
Ability to translate complex data into actionable insights
Strong communication and collaboration skills
- Q6. What makes you unique for the corporate world ?
- Ans.
My unique combination of technical skills, creativity, and communication abilities make me a valuable asset for any corporate team.
Strong technical skills in data analysis and machine learning
Creative problem-solving approach to complex business challenges
Excellent communication and collaboration skills
Proven track record of delivering results and driving business growth
Ability to adapt to new technologies and learn qu
- Q7. Tell me more about ML
- Ans.
ML is a subset of AI that involves training algorithms to make predictions or decisions based on data.
ML algorithms can be supervised, unsupervised, or semi-supervised
Supervised learning involves training a model on labeled data to make predictions on new data
Unsupervised learning involves finding patterns in unlabeled data
Semi-supervised learning involves a combination of labeled and unlabeled data
Examples of ML appli...
- Q8. Folded my resume and asked what is the surface area of this ?
Interview Preparation Tips
Experience: Self-explanatory
Tips: Courses in ML, probab would be a plus.
Round: Test
Experience: It was a mix of aptitude, quant and technical questions
Duration: 1 hour
Round: Technical Interview
Experience: I first told the interviewer about what I knew about the role and IBM. Added stuff about IBM Watson. He probed further about that. I told him I didn't know the details but threw in a guess about it being similar to Amazon Web services. Luckily I was right. He asked the basic funda about ML. Explained stuff to him. About the statistical models vs ML question, I emphasized that statistical models might be the core of ML, but ML encompasses details about the whole process. Special to profile - Said I have requisite theoretical as well as practical know-how. Talked about projects provided practical knowledge. Unique for corp. world - Talked about how I am a teamplayer by being a part of a lot of teams throughout insti time. He then asked, how would you tackle a bully. I said zero tolerance to bullies in my team. Then asked how would you tackle a bully who is your senior/client - Said I will be diplomatic as there is a tradeoff between how I can minimize abuse and how much damage it will cause. But, will make sure that I start small and then push for larger changes. Then asked more about ML - Told him about Supervised, unsupervised, RL, Deep. Surface area - standard guesstimate stuff
Tips: If you have done a course in ML, you will sail through. This time it looks like they were trying to expand their team
College Name: IIT Madras
Skills evaluated in this interview
I applied via campus placement at Indian Institute of Technology (IIT), Chennai and was interviewed in Jan 2016. There were 4 interview rounds.
Interview Questionnaire
5 Questions
- Q1. What does Principal Component Analysis do?
- Ans.
Principal Component Analysis is a statistical technique used to reduce the dimensionality of a dataset while retaining important information.
PCA identifies the underlying structure in the data by finding the directions of maximum variance.
It transforms the data into a new coordinate system where the first axis has the highest variance, followed by the second, and so on.
The transformed data can be used for visualization...
- Q2. Why does it address this problem in the given way?
- Ans.
The problem is addressed in this way because it leverages advanced cognitive techniques to analyze complex data patterns.
Utilizes machine learning algorithms to identify patterns and trends in data
Incorporates natural language processing to extract insights from unstructured data
Applies deep learning techniques for image and speech recognition tasks
- Q3. (Looking at my mini-assignment)How did you implement Support Vector Machines?(the next question follows this)
- Q4. What are Kernals ?
- Ans.
Kernels are small matrices used in image processing and machine learning algorithms to perform operations on images or data.
Kernels are used in convolutional neural networks (CNNs) to extract features from images.
They are also used in image processing techniques like blurring, sharpening, and edge detection.
Kernels can be represented as matrices of numbers that are applied to the input data to produce an output.
In mach...
- Q5. What uses does it have?
- Ans.
Cognitive Data Science has various uses in fields like healthcare, finance, marketing, and research.
Healthcare: Cognitive data science can be used to analyze patient data and predict diseases.
Finance: It can be used to analyze market trends and make investment decisions.
Marketing: It can be used to analyze customer behavior and personalize marketing campaigns.
Research: It can be used to analyze large datasets and disco
Interview Preparation Tips
Experience: The test was pretty straight forward run of the mill aptitude questions
Tips: Practicing is not necessary but doing it might have a slight advantage
Duration: 1 hour
Total Questions: 30
Round: Technical Interview
Experience: 1.I explained the concept of PCA stating the assumptions and conditions on how to implement it. I explained to him what the objective function was and justifying the approach to achieve it.
2.Explained the Concept of C-SVM,
3.Stated the primary objective of using kernal methods
4.Explained how Kernal functions can be used to give complex shapes to the seperating hyperplane
Tips: The interviewer wants to know the extent of your knowledge and the ability to think in the situation. He will guide you to the answer. Think of it as more like a conceptual discussion between two people.
If you are not sure of the answer walk him through your thought process
College Name: IIT Madras
Skills evaluated in this interview
Cognitive Data Scientist Interview Questions & Answers
IBM
Avinash Arya ee15m025
posted on 4 Dec 2016
I applied via campus placement at Indian Institute of Technology (IIT), Chennai and was interviewed in Dec 2016. There were 5 interview rounds.
Interview Questionnaire
2 Questions
- Q1. WHAT DO YOU MEAN BY COGNITIVE?
- Ans.
Cognitive refers to the mental processes and abilities related to perception, learning, memory, reasoning, and problem-solving.
Cognitive refers to the mental processes and abilities of the brain.
It involves perception, learning, memory, reasoning, and problem-solving.
Cognitive science studies how these processes work and interact.
Cognitive data science applies data analysis techniques to understand and improve cognitiv...
- Q2. WHY DO YOU WANT TO WORK EVEN THOUGH YOU ARE EXTREMELY GOOD AT ELECTRICAL ENGG?
- Ans.
I am passionate about leveraging my skills in electrical engineering to solve complex problems using cognitive data science.
I have a strong interest in data analysis and machine learning, which are key components of cognitive data science.
I believe that combining my expertise in electrical engineering with cognitive data science will allow me to tackle new challenges and make a greater impact.
I am excited about the opp...
Interview Preparation Tips
Experience: APTITUDE QUESTIONS AND BUSINESS ENGLISH WRITING QUESTIONS
Tips: PREPARE WELL AND MAKE YOUR CALCULATION FAST
Duration: 2 hours
Total Questions: 50
Round: Technical Interview
Experience: COMPANY PROFILE
Tips: UNDERSTANDING ABOUT YOUR ROLE
Round: HR Interview
Experience: I WAS GIVING MY EE EXAMPLES TO CORROBORATE MY IDEAS, SO THEY WANTED TO KNOW WHETHER I AM GOING TO WORK FOR THIS OR NOT.
Tips: BE HONEST, DON'T LIE JUST FOR THE SAKE OF JOB. THE ROLE WHICH SUITS YOU MOST WILL AUTOMATICALLY COME TO YOU, JUST BEING YOURSELF. MOREOVER, YOU WILL NEVER HAVE TO MUG UP ANYTHING AND IT WOULD BE BEST TO EXPRESS YOURSELF.
Skills: Decision Making Skill, Mathematical Aptitude, Numerical Techniques
College Name: IIT Madras
Skills evaluated in this interview
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Wipro Cognos Tm1 Developer Interview Process
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