Altimetrik Data Science Intern Interview Questions and Answers

Q1. Rate yourself in plsql mview index cte merge statement
Ans. 
I rate myself highly in PL/SQL with expertise in mview, index, CTE, and merge statement.
• I have extensive knowledge and experience in writing PL/SQL code.

• I am proficient in creating and managing materialized views (mview) to improve query performance.

• I am skilled in creating and managing indexes to optimize database performance.

• I am well-versed in using Common Table Expressions (CTE) for complex queries and recursive op...read more
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Q1. Salary discussion
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Q1. How to create pipeline in adf?
Ans. 
To create a pipeline in ADF, you can use the Azure Data Factory UI or code-based approach.
• Use Azure Data Factory UI to visually create and manage pipelines

• Use code-based approach with JSON to define pipelines and activities

• Add activities such as data movement, data transformation, and data processing to the pipeline

• Set up triggers and schedules for the pipeline to run automatically
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Q2. Diffrent types of activities in pipelines
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Activities in pipelines include data extraction, transformation, loading, and monitoring.
• Data extraction: Retrieving data from various sources such as databases, APIs, and files.

• Data transformation: Cleaning, filtering, and structuring data for analysis.

• Data loading: Loading processed data into a data warehouse or database.

• Monitoring: Tracking the performance and health of the pipeline to ensure data quality and reliab
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Q3. What is use of getmetadata
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getmetadata is used to retrieve metadata information about a dataset or data source.
• getmetadata can provide information about the structure, format, and properties of the data.

• It can be used to understand the data schema, column names, data types, and any constraints or relationships.

• This information is helpful for data engineers to properly process, transform, and analyze the data.

• For example, getmetadata can be used ...read more
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Q4. Diffrent types of triggers
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Triggers in databases are special stored procedures that are automatically executed when certain events occur.
• Types of triggers include: DML triggers (for INSERT, UPDATE, DELETE operations), DDL triggers (for CREATE, ALTER, DROP operations), and logon triggers.

• Triggers can be classified as row-level triggers (executed once for each row affected by the triggering event) or statement-level triggers (executed once for eac...read more
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Q5. Diffrence between normal cluster and job cluster in databricks
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Normal cluster is used for interactive workloads while job cluster is used for batch processing in Databricks.
• Normal cluster is used for ad-hoc queries and exploratory data analysis.

• Job cluster is used for running scheduled jobs and batch processing tasks.

• Normal cluster is terminated after a period of inactivity, while job cluster is terminated after the job completes.

• Normal cluster is more cost-effective for short-liv...read more
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Q6. What is slowly changing dimensions
Ans. 
Slowly changing dimensions refer to data warehouse dimensions that change slowly over time.
• SCDs are used to track historical changes in data over time.

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

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

• Example: A customer's address changing would be a Type 2 SCD.

• Ex...read more
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Q7. Incremental load
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Q8. With use in python
Ans. 
Use Python's 'with' statement to ensure proper resource management and exception handling.
• Use 'with' statement to automatically close files after use

• Helps in managing resources like database connections

• Ensures proper cleanup even in case of exceptions
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Q9. List vs tuple in python
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List is mutable, tuple is immutable in Python.
• List can be modified after creation, tuple cannot be modified.

• List uses square brackets [], tuple uses parentheses ().

• Lists are used for collections of items that may need to be changed, tuples are used for fixed collections of items.

• Example: list_example = [1, 2, 3], tuple_example = (4, 5, 6)
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Q10. Datalake 1 vs datalake2
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Datalake 1 and Datalake 2 are both storage systems for big data, but they may differ in terms of architecture, scalability, and use cases.
• Datalake 1 may use a Hadoop-based architecture while Datalake 2 may use a cloud-based architecture like AWS S3 or Azure Data Lake Storage.

• Datalake 1 may be more suitable for on-premise data storage and processing, while Datalake 2 may offer better scalability and flexibility for clou...read more
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Q11. How to read a file in databricks
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To read a file in Databricks, you can use the Databricks File System (DBFS) or Spark APIs.
• Use dbutils.fs.ls('dbfs:/path/to/file') to list files in DBFS

• Use spark.read.format('csv').load('dbfs:/path/to/file') to read a CSV file

• Use spark.read.format('parquet').load('dbfs:/path/to/file') to read a Parquet file
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Q12. Star vs snowflake schema
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Star schema is denormalized with one central fact table surrounded by dimension tables, while snowflake schema is normalized with multiple related dimension tables.
• Star schema is easier to understand and query due to denormalization.

• Snowflake schema saves storage space by normalizing data.

• Star schema is better for data warehousing and OLAP applications.

• Snowflake schema is better for OLTP systems with complex relationsh
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Q13. Repartition vs coalesece
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repartition increases partitions while coalesce decreases partitions in Spark
• repartition shuffles data and can be used for increasing partitions for parallelism

• coalesce reduces partitions without shuffling data, useful for reducing overhead

• repartition is more expensive than coalesce as it involves data movement

• example: df.repartition(10) vs df.coalesce(5)
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Q14. Parquet file uses
Ans. 
Parquet file format is a columnar storage format used for efficient data storage and processing.
• Parquet files store data in a columnar format, which allows for efficient querying and processing of specific columns without reading the entire file.

• It supports complex nested data structures like arrays and maps.

• Parquet files are highly compressed, reducing storage space and improving query performance.

• It is commonly used ...read more
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Q1. Azure Scenario based questions
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Q2. Pyspark Coding based questions
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Q1. ADF, Databricks related question
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Q2. Spark Performance problem and scenarios
Ans. 
Spark performance problems can arise due to inefficient code, data skew, resource constraints, and improper configuration.
• Inefficient code can lead to slow performance, such as using collect() on large datasets.

• Data skew can cause uneven distribution of data across partitions, impacting processing time.

• Resource constraints like insufficient memory or CPU can result in slow Spark jobs.

• Improper configuration settings, su...read more
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Q1. What can you improve the query performance?
Ans. 
Improving query performance by optimizing indexes, using proper data types, and minimizing data retrieval.
• Optimize indexes on frequently queried columns

• Use proper data types to reduce storage space and improve query speed

• Minimize data retrieval by only selecting necessary columns

• Avoid using SELECT * in queries

• Use query execution plans to identify bottlenecks and optimize accordingly
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Q2. What id SCD type2 table?
Ans. 
SCD type2 table is used to track historical changes in data by creating new records for each change.
• Contains current and historical data

• New records are created for each change

• Includes effective start and end dates for each record

• Requires additional columns like surrogate keys and version numbers

• Used for slowly changing dimensions in data warehousing
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Q1. In round 1st they ask questions about your current project. Also the practical based questions on the modules you have worked in your recent project. Sometimes they ask to share ur screen. Overall experie...read more
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Python code and sql query they ask

Q1. Coding question of finding index of 2 nos. having total equal to target in a list, without using nested for loop? l= [2,15,5,7] t= 9 output》》[0,3]
Ans. 
Finding index of 2 numbers having total equal to target in a list without nested for loop.
• Use dictionary to store the difference between target and each element of list.

• Iterate through list and check if element is in dictionary.

• Return the indices of the two elements that add up to target.
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Q2. What is random forest, knn?
Ans. 
Random forest and KNN are machine learning algorithms used for classification and regression tasks.
• Random forest is an ensemble learning method that constructs multiple decision trees and combines their outputs to make a final prediction.

• KNN (k-nearest neighbors) is a non-parametric algorithm that classifies new data points based on the majority class of their k-nearest neighbors in the training set.

• Random forest is us...read more
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Q1. Ll coding on python dictionary
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Q2. Find unique keys in 2 dictionaries
Ans. 
To find unique keys in 2 dictionaries.
• Create a set of keys for each dictionary

• Use set operations to find the unique keys

• Return the unique keys
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Q3. Aws ec2 model deployment procedure
Ans. 
AWS EC2 model deployment involves creating an instance, installing necessary software, and deploying the model.
• Create an EC2 instance with the desired specifications

• Install necessary software and dependencies on the instance

• Upload the model and any required data to the instance

• Deploy the model using a web server or API

• Monitor the instance and model performance for optimization
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Q4. Overloading concept of oop
Ans. 
Overloading is the ability to define multiple methods with the same name but different parameters.
• Overloading allows for more flexibility in method naming and improves code readability.

• Examples include defining multiple constructors for a class with different parameter lists or defining a method that can accept different data types as input.

• Overloading is resolved at compile-time based on the number and types of argume...read more
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