Sigmoid Interview Questions, Process, and Tips

Q1. Reverse K group linked list.
Ans. 
Reverse K group linked list
• Iterate through the linked list in groups of size K

• Reverse each group of K nodes

• Connect the reversed groups back together
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Q1. Nearest Left Greater element.
Ans. 
Find the nearest element on the left side of an array that is greater than the current element.
• Iterate through the array from right to left.

• Use a stack to keep track of elements that are greater than the current element.

• Pop elements from the stack until finding a greater element or the stack is empty.

• If a greater element is found, that is the nearest left greater element.

• If the stack is empty, there is no nearest left
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Q2. Validate binary search tree.
Ans. 
Validate if a binary tree is a binary search tree
• In-order traversal should result in a sorted array

• Check if each node's value is within the correct range

• Recursively validate left and right subtrees
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Q1. Given a non-decreasing array, how can I determine the indices of an element X within it? If the element is not present, the output should be [-1, -1]. For example, for the array [1,2,3,3,5,5,7,8] and X=5, ...read more
Ans. 
Find indices of an element in a non-decreasing array
• Iterate through the array and keep track of the indices where the element X is found

• Return the list of indices or [-1, -1] if element X is not found

• Handle edge cases like empty array or X not present in the array
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Q2. From the employee table, how can I return the employee_name, department, age, and salary of the employee who is older than 35 and has the highest salary within each department?
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Q1. What is scd in dw?
Ans. 
SCD stands for Slowly Changing Dimension in Data Warehousing.
• SCD is a technique used in data warehousing to track changes to dimension data over time.

• There are different types of SCDs - Type 1, Type 2, and Type 3.

• Type 1 SCD overwrites old data with new data, Type 2 creates new records for changes, and Type 3 maintains both old and new values in separate columns.

• Example: In a customer dimension table, if a customer chan...read more
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Q2. Inferschema in pyspark when reading file
Ans. 
inferschema in pyspark is used to automatically infer the schema of a file when reading it.
• inferschema is a parameter in pyspark that can be set to true when reading a file to automatically infer the schema based on the data

• It is useful when the schema of the file is not known beforehand

• Example: df = spark.read.csv('file.csv', header=True, inferSchema=True)
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Q3. Triplet program
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Q4. Rank vs dense rank
Ans. 
Rank assigns unique ranks to each distinct value, while dense rank assigns ranks without gaps.
• Rank function assigns unique ranks to each distinct value in a result set.

• Dense rank function assigns ranks to rows in a result set without any gaps between the ranks.

• Rank function may skip ranks if there are ties in values, while dense rank will not skip ranks.
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Q5. Some numbers gave how many rows for inner join,left,right,full join
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Q6. Read parquet file and remove duplicates
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Q7. Optimizing techniques in spark
Ans. 
Optimizing techniques in Spark involve partitioning, caching, and tuning resources for efficient data processing.
• Use partitioning to distribute data evenly across nodes for parallel processing

• Cache frequently accessed data in memory to avoid recomputation

• Tune resources such as memory allocation and parallelism settings for optimal performance
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Q8. Repartition vs coalesce
Ans. 
Repartition is used to increase the number of partitions in a DataFrame, while coalesce is used to decrease the number of partitions.
• Repartition involves shuffling data across the network, which can be expensive in terms of performance and resources.

• Coalesce is a more efficient operation as it minimizes data movement by only merging existing partitions.

• Repartition is typically used when there is a need for more paralle...read more
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Q9. Normalization in db and types
Ans. 
Normalization in databases is the process of organizing data in a database to reduce redundancy and improve data integrity.
• Normalization is used to eliminate redundant data and ensure data integrity.

• It involves breaking down a table into smaller tables and defining relationships between them.

• There are different normal forms such as 1NF, 2NF, 3NF, and BCNF.

• Normalization helps in reducing data redundancy and improving qu...read more
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Q10. Transformation vs action
Ans. 
Transformation involves changing the data structure, while action involves performing a computation on the data.
• Transformation changes the data structure without executing any computation

• Action performs a computation on the data and triggers the execution

• Examples of transformation include map, filter, and reduce in Spark or Pandas

• Examples of action include count, collect, and saveAsTextFile in Spark
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Q1. How does dropout help in neural networks?
Ans. 
Dropout helps prevent overfitting in neural networks by randomly setting a fraction of input units to zero during training.
• Dropout helps in preventing overfitting by reducing the interdependence between neurons

• It acts as a regularization technique by randomly setting a fraction of input units to zero during training

• Dropout forces the network to learn redundant representations, making it more robust and generalizable

• It ...read more
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Q2. How does xgboost deal with nan values?
Ans. 
XGBoost can handle missing values (NaN) by assigning them to a default direction during tree construction.
• XGBoost treats NaN values as missing values and learns the best direction to go at each node to handle them

• During tree construction, XGBoost assigns NaN values to the default direction based on the training data statistics

• XGBoost can handle missing values in both input features and target variables
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Q3. What would you do if you see your model not performing well on a time series prediction specifically on peaks and troughs?
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Q4. How would you deal with datasets having lots of categories
Ans. 
Utilize feature engineering techniques like one-hot encoding or target encoding to handle datasets with many categories.
• Use feature engineering techniques like one-hot encoding to convert categorical variables into numerical values

• Consider using target encoding to encode categorical variables based on the target variable

• Apply dimensionality reduction techniques like PCA or LDA to reduce the number of features

• Use tree-b...read more
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Case study involved creating a churn model with an imbalanced dataset. It contained a lot of missing values in numerical features which were correlated, Also the scaling was highly skewed. Categorical data contained a lot of low frequency categories. They wanted a final model performance on a test dataset on chosen KPIs (I chose F1-score).

Q1. Questions on the case study - assumptions made, why did you choose a particular KPI? What was the loss function? How did you deal with class imbalance, nan values , high number of categories? Did you perfo...read more
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Q2. Questions related to past experience - I told him about my last project which was based on computer vision. Interviewer asked a lot of clarifying questions and inquired about the process
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Q1. Asked about previous projects and the business impact.
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Q2. What challenges have you faced managing a team?
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Q3. Asked about a hypothetical scenario, how would you help the customer with that
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Q4. What would you do if someone in your team is not performing well
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Q1. Salary Discussion
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Q2. Previous employment history
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Q1. Pyspark, sql, Python.
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Q1. Python, dsa,PySpark
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Q1. Simple questions
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1 hour aptitude test

Q1. What is one hot encoding
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Q1. What is your long term goal
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Some python qustions on list, dict, array, string. SQL questions on window function.