Oracle Data Annotation Engineer Interview Questions and Answers

Basic Python, SQL, and Bash questions

Q1. SQL questions with operations that include changing from string to array. Tip: stick to RDBMS-specific dialects only (like Postgres), I used Spark SQL
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Q2. Simple Python questions with a follow-up to optimise it
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Q3. Bash script-based questions, are pretty basic.
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Q4. Data pipeline design and best practices.
Ans. 
Data pipeline design involves creating a system to efficiently collect, process, and analyze data.
• Understand the data sources and requirements before designing the pipeline.

• Use tools like Apache Kafka, Apache NiFi, or AWS Glue for data ingestion and processing.

• Implement data validation and error handling mechanisms to ensure data quality.

• Consider scalability and performance optimization while designing the pipeline.

• Doc...read more
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Q1. Easy to medium Leetcode-based question. With moderate difficulty.
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Q2. Simple Python-based question with optimisation.
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Q3. Design specific questions based on Data pipelines.
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Q1. SQL-based question with moderate difficulty.
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Q2. Python-based questions, follow questions with some optimisations.
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Q3. Bash-script based round.
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It is nice to do work

It helps in understanding the 5

It helps a lot in company

Q1. How to do job in company?
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Q2. What I should do?
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Q3. How to do work?
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Q4. What should I do
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Q5. What is the goal?
Ans. 
The goal of a Data Analyst is to analyze data to extract valuable insights and make data-driven decisions.
• Identify trends and patterns in data

• Create visualizations to communicate findings

• Provide actionable recommendations based on data analysis
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Aptitude test for selection.

Q1. What is your understanding of data analytics?
Ans. 
Data analytics involves analyzing raw data to draw conclusions and make informed decisions.
• Data analytics involves collecting, processing, and analyzing data to identify trends and patterns.

• It helps businesses make data-driven decisions and improve performance.

• Techniques used in data analytics include data mining, machine learning, and statistical analysis.

• Examples of data analytics in action include predicting custome...read more
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Q2. What is the significance of data analytics in analyzing and visualizing data?
Ans. 
Data analytics is crucial for extracting insights and patterns from data, and visualizing them in a meaningful way.
• Data analytics helps in identifying trends, patterns, and correlations within large datasets.

• It enables businesses to make informed decisions based on data-driven insights.

• Visualizations such as charts, graphs, and dashboards make complex data easier to understand and interpret.

• Data analytics can uncover h...read more
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It was well designed

Q1. Basic Questions about data warehousing
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Q2. Dbt Scenarios
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Q1. Basic ml related questions,past project discussion
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Q1. AI, ML related, statistical analysis of the graph.
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Q1. Work culture, check
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Q1. Work culture, project discussion, scenario based questions
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Q1. Basic HR questions, why sap salary discussions
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Q1. What are different data structures?
Ans. 
Data structures are ways to organize and store data in a computer system.
• Arrays: a collection of elements stored in contiguous memory locations

• Linked Lists: a sequence of elements where each element points to the next one

• Stacks: a Last In First Out (LIFO) data structure

• Queues: a First In First Out (FIFO) data structure

• Trees: hierarchical data structures with a root node and child nodes

• Graphs: a collection of nodes conn
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Q2. What are different types of databases
Ans. 
Different types of databases include relational, NoSQL, graph, and time-series databases.
• Relational databases: store data in tables with rows and columns (e.g. MySQL, PostgreSQL)

• NoSQL databases: flexible, schema-less databases (e.g. MongoDB, Cassandra)

• Graph databases: store data in nodes and edges to represent relationships (e.g. Neo4j)

• Time-series databases: optimized for storing and querying time-stamped data (e.g. In
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Q1. Cache vs persist
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Cache is used for temporary storage of data in memory, while persist is used for saving data to disk for long-term storage.
• Cache is typically faster as it stores data in memory for quick access.

• Persist saves data to disk for durability and long-term storage.

• Cache is often used for temporary data that can be recomputed if lost, while persist is used for important data that needs to be retained.

• Examples: Using cache for ...read more
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Q2. Broadcast join and other optimization techniques
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Q1. Sql window function
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Q2. Spark architecture, dataframe vs rdd
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Q3. Python reverse sentence
Ans. 
Reverse a sentence using Python
• Split the sentence into words using split() method

• Reverse the list of words using list slicing

• Join the reversed list of words back into a sentence using join() method
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Q4. Join based on nulls in sql
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Q1. I am a data entry operator
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Q2. I am a Data entry operator .
Ans. I am a data entry operator.
Answered by Mohammad Ali Khan
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Q3. I am Excel entry operator
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Q4. Hindi typing speed 16
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Q5. English typing speed 20
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Q6. Fesser no experience
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Q7. Data entry operator
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