IBM Data Engineer Interview Questions, Process, and Tips

Q1. 1.ABOUT hdfs ,Spark context, databrciks 2.about projects 3. SQL queries 4. python question
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Q2. 1. about hdfs 2.sql 3.pyhon
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Python and sql coding examination

Q1. Bigquery concepts partitoning clustering unnest
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Q2. Optimisation techniques
Ans. 
Optimisation techniques are methods used to improve the efficiency and performance of data processing tasks.
• Use indexing to speed up data retrieval

• Implement parallel processing to distribute workloads

• Utilize caching to store frequently accessed data

• Optimize algorithms for better performance

• Use data compression techniques to reduce storage space
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Q3. Sql query on cte and joins
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Q4. Spark basics and file formats and coalesce and repartition
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Q1. Why ibm and what is your expectation?
Ans. 
I chose IBM for its reputation in data engineering and my expectation is to work with cutting-edge technology and contribute to innovative projects.
• IBM is a leader in the tech industry with a strong reputation for data engineering solutions

• I believe IBM offers opportunities to work with cutting-edge technology and innovative projects

• I expect to collaborate with talented professionals and contribute to impactful data en
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Q2. Spark basics hdfs basics why only spark what is hdfs and why is it called distributed why bigquery whats speciality of bigquery various optimisations qliksense dashboarding complex sql query involving cte ...read more
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Simple Aptitude Test

Coding was of Easy Dsa

Topics were given and you had to provide responses one by one

Q1. Technicals were basic concepts
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Q1. About IICS basic
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Q2. About IICS scenarios
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Q1. What are the components we use in graphs to remove duplicates
Ans. 
Components used in graphs to remove duplicates include HashSet and HashMap.
• Use HashSet to store unique elements

• Use HashMap to store key-value pairs with unique keys

• Iterate through the graph and add elements to HashSet or HashMap to remove duplicates
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Q2. How many graphs build yet?
Ans. 
I have built 10 graphs so far, including network graphs, bar graphs, and pie charts.
• I have built 10 graphs in total

• I have experience building network graphs, bar graphs, and pie charts

• I have used tools like matplotlib and seaborn for graph building
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Q1. How you deal with escalaltions
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I address escalations by identifying the root cause, communicating effectively, collaborating with stakeholders, and finding a resolution.
• Identify the root cause of the escalation to understand the issue thoroughly

• Communicate effectively with all parties involved to ensure clarity and transparency

• Collaborate with stakeholders to gather necessary information and work towards a resolution

• Find a resolution that addresses ...read more
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Q2. Strengths and weakness
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Q1. Sql related questions
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Q2. Data management
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Q1. Data project life cycle
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Q2. Day to day tasks
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Day to day tasks involve data collection, processing, analysis, and maintenance to ensure data quality and availability.
• Collecting and storing data from various sources

• Cleaning and preprocessing data for analysis

• Developing and maintaining data pipelines

• Analyzing data to extract insights and trends

• Collaborating with data scientists and analysts to support their work
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