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10+ Hindustan Unilever Interview Questions and Answers
Q1. Probability of extinction of an amoeba which has has equal chances of the following happening every minute: 1. Death 2. Idling 3.Splitting into 2 4.Splitting into 3
The probability of extinction of an amoeba with equal chances of death, idling, splitting into 2, and splitting into 3.
Calculate the probability of each event happening in a minute
Calculate the probability of the amoeba surviving in a minute
Calculate the probability of the amoeba surviving after n minutes
Use probability theory to determine the overall probability of extinction
Q2. Write DAX to calculate some values.
Calculating values using DAX in Power BI
Use SUMX function to calculate sum of values in a column
Use CALCULATE function to apply filters and conditions to calculations
Use DAX measures to create reusable calculations
Example: Calculate total sales for a specific product category
Q3. How to handle large datasets.
Handling large datasets involves optimizing storage, processing, and analysis techniques.
Use distributed computing frameworks like Hadoop or Spark to process data in parallel.
Utilize data compression techniques to reduce storage requirements.
Implement indexing and partitioning strategies to improve query performance.
Consider using cloud-based storage and computing resources for scalability.
Use sampling techniques to work with subsets of data for initial analysis.
Q4. How to use parameters.
Parameters are variables that can be passed into a function or procedure to customize its behavior.
Parameters are defined when creating a function or procedure.
They can be used to pass values into the function or procedure.
Parameters can be required or optional, with default values specified.
They help make functions more flexible and reusable.
Example: def calculate_area(length, width):
Q5. Create data model and visualizations.
Creating data model and visualizations involves structuring data and presenting it visually.
Identify the key variables and relationships in the data
Design a data model that represents the data structure
Select appropriate visualization tools and techniques
Create visualizations such as charts, graphs, and dashboards
Ensure the visualizations effectively communicate insights from the data
Q6. Power BI optimization techniques.
Power BI optimization techniques involve data model optimization, query optimization, and report design best practices.
Optimize data model by removing unnecessary columns and relationships
Use query folding to push data transformation tasks to the data source
Avoid using calculated columns and instead use measures for calculations
Limit the use of visuals that require high computational resources
Use aggregations and composite models for large datasets
Q7. Find the longest palindrome in the given string:?
The longest palindrome in a given string is the longest sequence of characters that reads the same forwards and backwards.
Iterate through each character in the string
For each character, expand outwards to find the longest palindrome centered at that character
Keep track of the longest palindrome found so far
Return the longest palindrome
Q8. print the pattern as shown Below
Print a pattern as shown below.
Use nested loops to iterate through rows and columns.
Use conditional statements to determine the pattern to be printed.
Store each row as a string in an array of strings.
Q9. Finding probability of a point making an obtuse angle inside a trapezium with one side.
Probability of a point making an obtuse angle inside a trapezium with one side.
The point must lie inside the trapezium.
The trapezium must have one side that is not parallel to the other.
Use trigonometry to calculate the angles of the trapezium.
Calculate the probability of the point making an obtuse angle using the angles of the trapezium.
Q10. joins in python
Joins in Python are used to combine data from two or more tables based on a related column between them.
Joins are used in SQL to combine rows from two or more tables based on a related column between them.
Common types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
In Python, joins can be performed using libraries like pandas or SQLalchemy.
Example: df_merged = pd.merge(df1, df2, on='common_column', how='inner')
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