Signzy Technologies Data Operations Engineer Interview Questions and Answers

Q1. Introduce Yourself
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I am a data analyst with a background in statistics and experience in analyzing large datasets to drive business decisions.
• Graduated with a degree in Statistics

• Proficient in data analysis tools such as Python, R, and SQL

• Experience in creating visualizations to communicate insights

• Strong problem-solving skills
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Q2. Skills you have
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Q1. ML questions related to previous projects
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It was basically Arrays and String

It was given based on Docker

Q1. Asked about myself
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They are asking about SQL queries and BI dashboard knowledge

Q1. 1. Tell about yourself? 2. What is your Project? 3. WHat challenges faced in Data Engineering role 4. BI dashboard development experience
Ans. 
I am a data engineer with experience in BI dashboard development and overcoming challenges in the field.
• I have worked on various data engineering projects, including building data pipelines and optimizing data storage.

• One of my notable projects involved developing a real-time data processing system for a retail company, which improved their inventory management and sales forecasting.

• In my previous role, I faced challen...read more
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Q2. 1. What is Data models 2. What are the activities used in ADF 3. What is IR?
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Answers to questions related to Data Models, ADF activities, and IR.
• Data models are the representation of data structures and relationships between them.

• ADF activities include data movement, data transformation, and control activities.

• IR stands for Integration Runtime, which is a compute infrastructure used to provide data integration capabilities across different network environments.
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6 mcqs and 2 sql questions

Q1. Difference between having and where in sql
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In SQL, 'HAVING' is used with GROUP BY to filter groups, while 'WHERE' is used to filter rows before grouping.
• HAVING is used with GROUP BY to filter groups based on aggregate functions

• WHERE is used to filter rows before grouping

• HAVING is used after GROUP BY in the query, while WHERE is used before GROUP BY

• Example: SELECT department, AVG(salary) FROM employees GROUP BY department HAVING AVG(salary) > 50000;

• Example: S
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Q2. Sql joins and it types
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Q3. Window functions and subqueries
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Q4. Sql scenario question based on joins
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Q5. Kpi's and metrics
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Q1. Business problem related to case study
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They are asking about SQL queries and BI dashboard knowledge

Q1. 1. Tell about yourself? 2. What is your Project? 3. WHat challenges faced in Data Engineering role 4. BI dashboard development experience
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Q2. 1. What is Data models 2. What are the activities used in ADF 3. What is IR?
Ans. 
Answers to questions related to Data Engineer role
• Data models are the logical representation of data objects and their relationships

• ADF activities include data movement, data transformation, and control activities

• IR stands for Integration Runtime, which is a compute infrastructure used to provide data integration capabilities
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HackerEarth based assessment.
1. 8 qs were stats & prob based multiples choices
2. 2 were medium level sql questions.

Q1. Started with basic sql question like diff btw nosql & sql, join etc
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Q2. Table were given in google sheet & had to write basic level queries on them eg. cte's, agg function, lead & lag func
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Q1. The interviewer was not serious at all, asked silly quizzes instead of business case study, KPI & metrics
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Q2. Prepare for logical reasoning
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Q1. How to productinize Data Pipelines
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To productinize Data Pipelines, one must automate, monitor, and scale the pipeline for efficient and reliable data processing.
• Automate the data pipeline using tools like Apache Airflow or Kubernetes

• Monitor the pipeline for errors, latency, and data quality issues using monitoring tools like Prometheus or Grafana

• Scale the pipeline by optimizing code, using distributed computing frameworks like Spark, and leveraging clou...read more
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Q2. How does big query works
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BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data.
• BigQuery uses a distributed architecture to process and analyze large datasets quickly.

• It allows users to run SQL-like queries on datasets stored in Google Cloud Storage.

• BigQuery automatically scales to handle large amounts of data and can be integrated with other Google Cloud services.

• It supports real-time data...read more
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