Accenture Interview Questions, Process, and Tips

Q1. Tell me about yourself
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Q2. Project Architecture
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Q3. Rate yourself out of 5 in Pyspark , Python and SQL
Ans. 
I would rate myself 4 in Pyspark, 5 in Python, and 4 in SQL.
• Strong proficiency in Python programming language

• Experience in working with Pyspark for big data processing

• Proficient in writing complex SQL queries for data manipulation

• Familiarity with optimizing queries for performance

• Hands-on experience in data engineering projects
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Q4. How to handle duplicates in python ?
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Use Python's built-in data structures like sets or dictionaries to handle duplicates.
• Use a set to remove duplicates from a list: unique_list = list(set(original_list))

• Use a dictionary to remove duplicates from a list while preserving order: unique_list = list(dict.fromkeys(original_list))
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Q5. Methods of migrating Hive metdatastore to unity catalog in Databricks ?
Ans. 
Use Databricks provided tools like databricks-connect and databricks-cli to migrate Hive metadata to Unity catalog.
• Use databricks-connect to connect to the Databricks workspace from your local development environment.

• Use databricks-cli to export the Hive metadata from the existing Hive metastore.

• Create a new Unity catalog in Databricks and import the exported metadata using databricks-cli.

• Validate the migration by chec...read more
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Q6. Read a CSV file from ADLS path ?
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To read a CSV file from an ADLS path, you can use libraries like pandas or pyspark.
• Use pandas library in Python to read a CSV file from ADLS path

• Use pyspark library in Python to read a CSV file from ADLS path

• Ensure you have the necessary permissions to access the ADLS path
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Q7. There was a table provided on coding screen and asked to write different programs and SQL queries from the table and tell the approach you are taking ? Like age greater than 30 then sum the age how would y...read more
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Q8. How many stages will create from the above code that I have written
Ans. 
The number of stages created from the code provided depends on the specific code and its functionality.
• The number of stages can vary based on the complexity of the code and the specific tasks being performed.

• Stages may include data extraction, transformation, loading, and processing.

• It is important to analyze the code and identify distinct stages to determine the total number.
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Q9. Narrow vs Wide Transformation ?
Ans. 
Narrow transformation processes one record at a time, while wide transformation processes multiple records at once.
• Narrow transformation processes one record at a time, making it easier to parallelize and optimize.

• Wide transformation processes multiple records at once, which can lead to shuffling and performance issues.

• Examples of narrow transformations include map and filter operations, while examples of wide transfor
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Q10. What are action and transformation ?
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Actions and transformations are key concepts in data engineering, involving the manipulation and processing of data.
• Actions are operations that trigger the execution of a data transformation job in a distributed computing environment.

• Transformations are functions that take an input dataset and produce an output dataset, often involving filtering, aggregating, or joining data.

• Examples of actions include 'saveAsTextFile'...read more
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Q11. What happens when we enforce the schema and when we manually define the schema in the code ?
Ans. 
Enforcing the schema ensures data consistency and validation, while manually defining the schema in code allows for more flexibility and customization.
• Enforcing the schema ensures that all data conforms to a predefined structure and format, preventing errors and inconsistencies.

• Manually defining the schema in code allows for more flexibility in handling different data types and structures.

• Enforcing the schema can be do...read more
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Q12. What all the optimisation are possible to reduce the overhead of reducing the reading part of large datasets in spark ?
Ans. 
Optimizations like partitioning, caching, and using efficient file formats can reduce overhead in reading large datasets in Spark.
• Partitioning data based on key can reduce the amount of data shuffled during joins and aggregations

• Caching frequently accessed datasets in memory can avoid recomputation

• Using efficient file formats like Parquet or ORC can reduce disk I/O and improve read performance
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Q13. Write a sql query to find the name of person who logged in last within each country from Person Table ?
Ans. 
SQL query to find the name of person who logged in last within each country from Person Table
• Use a subquery to find the max login time for each country

• Join the Person table with the subquery on country and login time to get the name of the person
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Q14. Difference between List and Tuple ?
Ans. 
List is mutable, Tuple is immutable in Python.
• List can be modified after creation, Tuple cannot be modified.

• List is defined using square brackets [], Tuple is defined using parentheses ().

• Example: list_example = [1, 2, 3], tuple_example = (4, 5, 6)
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Q15. Difference between Rank , Dense Rank and Row Number and when we are using each of them ?
Ans. 
Rank assigns a unique rank to each row, Dense Rank assigns a unique rank to each distinct row, and Row Number assigns a unique number to each row.
• Rank assigns the same rank to rows with the same value, leaving gaps in the ranking if there are ties.

• Dense Rank assigns a unique rank to each distinct row, leaving no gaps in the ranking.

• Row Number assigns a unique number to each row, without any regard for the values in the...read more
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Q16. What is List Comprehension ?
Ans. 
List comprehension is a concise way to create lists in Python by applying an expression to each item in an iterable.
• Syntax: [expression for item in iterable]

• Can include conditions: [expression for item in iterable if condition]

• Example: squares = [x**2 for x in range(10)]
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Q17. Tell me about the performance optimization done in your project ?
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Q18. Difference between the interactive cluster and job cluster ?
Ans. 
Interactive clusters allow for real-time interaction and exploration, while job clusters are used for running batch jobs.
• Interactive clusters are used for real-time data exploration and analysis.

• Job clusters are used for running batch jobs and processing large amounts of data.

• Interactive clusters are typically smaller in size and have shorter lifespans.

• Job clusters are usually larger and more powerful to handle heavy w...read more
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Q19. How to add a column in dataframe ? How to rename the column in dataframe ?
Ans. 
To add a column in a dataframe, use the 'withColumn' method. To rename a column, use the 'withColumnRenamed' method.
• To add a column, use the 'withColumn' method with the new column name and the expression to compute the values for that column.

• Example: df.withColumn('new_column', df['existing_column'] * 2)

• To rename a column, use the 'withColumnRenamed' method with the current column name and the new column name.

• Example:...read more
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Q20. Difference between Coalesce and Repartition and In which case we are using it ?
Ans. 
Coalesce is used to combine multiple small partitions into a larger one, while Repartition is used to increase or decrease the number of partitions in a DataFrame.
• Coalesce reduces the number of partitions in a DataFrame by combining small partitions into larger ones.

• Repartition increases or decreases the number of partitions in a DataFrame by shuffling the data across partitions.

• Coalesce is more efficient than Repartit...read more
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A and B can complete a piece of work in 12 and 15 days, respectively. They work together for 4 days, and then A leaves. How many more days will B take to complete the work?
If 10 men can do a piece of work in 6 days, how long will it take for 15 men to complete it?

Find the Second Largest Number
Write a program to find the second largest number in an array.

Q1. Questions from Resume and python as it was my preffered language
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Q2. What is Splunk
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Thr Aptitude questions is very genric. And not hard to solve, But there is a time limit you have to submit the test in a time limit. There is a cutoff of every section in the apti if you have cleared the cutoff you may eligible for next round.

There are 2 coding questions which we have to solve in 45 minutes timeframe. The difficulty of the questions is medium. Mostly question are on the topic Array and Strings. It is compulsory to give custom input and run the code then submit the code. If you dont run the code with your custom input the code will not submit and you were disqualified for the next round.

Q1. There are many youtube videos for this round you can watch and give it.
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Q1. Introduce yourself in detail
Ans. 
I am a recent graduate with a degree in Computer Science and a passion for software development.
• Recent graduate with a degree in Computer Science

• Passionate about software development

• Experience with programming languages like Java, Python, and C++

• Completed internships at tech companies such as Google and Microsoft
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Q2. Tell me about your projects in detail
Ans. 
I have worked on various projects including a web application for a healthcare company and a mobile app for a travel agency.
• Developed a web application using React.js for a healthcare company to manage patient records and appointments

• Created a mobile app using Flutter for a travel agency to book flights and hotels

• Implemented RESTful APIs for communication between front-end and back-end systems

• Utilized database manageme...read more
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Q3. Problems faced during project and Internship
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Q4. And then some basic HR questions.
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Q1. About basic details of day to day life
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Q2. Technical questions
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Q1. Managorial +technical
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Q2. All the scenario based questions
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