Tredence Interview Questions, Process, and Tips

Q1. What is Data Warehousing
Ans. 
Data warehousing is the process of collecting, storing, and managing data from various sources for analysis and reporting.
• Data warehousing involves extracting data from multiple sources and consolidating it into a single repository.

• It is used for creating a centralized database for analysis and reporting purposes.

• Data warehousing helps in making informed business decisions by providing a comprehensive view of the organ...read more
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Q2. Explain Data Marts
Ans. 
Data marts are subsets of data warehouses that focus on specific business areas or departments.
• Data marts are smaller, more focused data warehouses

• They are designed to serve the needs of a specific business unit or department

• Data marts are often created to provide faster access to relevant data for decision-making

• Examples include sales data mart, marketing data mart, and finance data mart
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Q1. Project Management Experience
Ans. 
I have 5 years of project management experience in various industries.
• Managed multiple projects simultaneously

• Created and maintained project schedules

• Led cross-functional teams to successful project completion

• Implemented project management best practices

• Communicated project updates to stakeholders regularly
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Q2. Salient parts of SOW
Ans. 
The salient parts of a Statement of Work (SOW) outline the project scope, deliverables, timeline, budget, and responsibilities.
• Project scope: Clearly define the objectives and requirements of the project.

• Deliverables: Specify the tangible outcomes or products that will be delivered.

• Timeline: Establish the project timeline with key milestones and deadlines.

• Budget: Outline the financial resources allocated for the projec...read more
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Q1. Copy activity in ADF
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Q2. Delta table, unity catalog , delta live table in Azure databricks
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Q1. Copy activity, Lookup, get metadata, if else, for each activity in ADF
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Q2. Conceptual ETL Questions like coalesce and repartition, cache, persist etc.
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Around 60 questions were asked including aptitude and basic coding in 2:30 hrs.

Q1. Basic questions on python, sql and project related discussion.
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Q2. Rate yourself in sql and python
Ans. 
I rate myself highly in SQL and Python.
• I have extensive experience in writing complex SQL queries and optimizing database performance.

• I am proficient in Python and have used it for data analysis, automation, and web scraping.

• I have worked on various projects where I utilized both SQL and Python together to extract, transform, and load data.

• I am familiar with popular SQL databases like MySQL, PostgreSQL, and Oracle, as ...read more
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Q3. Tell me about your projects
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Basic aptitude, verbal, and coding tests with one program to run and execute. The remaining questions are multiple-choice questions and one guestimate question.

Q1. Explain me your experience
Ans. 
I have 5 years of experience in product management, leading cross-functional teams to launch successful products.
• Led cross-functional teams to launch 3 new products in the past year

• Managed product roadmap and prioritized features based on customer feedback

• Collaborated with engineering, design, and marketing teams to ensure product success
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Q2. What yours product management principles
Ans. 
My product management principles are customer-centricity, data-driven decision making, continuous iteration, and cross-functional collaboration.
• Customer-centricity: Always prioritize the needs and wants of the customers when making product decisions.

• Data-driven decision making: Utilize data and analytics to inform product strategy and prioritize features.

• Continuous iteration: Embrace an agile approach to product develo...read more
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Q1. What do you know about tredence youtube recommendation system - explain how it recommend videos ,factors it consider to do so mainly from resume & hobby as usual hr question
Ans. 
Tredence YouTube recommendation system uses algorithms to suggest videos based on user preferences and behavior.
• Tredence YouTube recommendation system uses machine learning algorithms to analyze user data such as watch history, likes, and dislikes.

• It considers factors like video content, user demographics, and engagement metrics to recommend relevant videos.

• For example, if a user frequently watches cooking videos, the ...read more
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Q2. Tell me about yourself
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Q3. About analyst - what they do
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Q4. Why do you want to join this company
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Q5. About projects in your resume
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