CitiusTech Interview Questions, Process, and Tips

I will forecast expenses by analyzing historical data, market trends, and budget projections.

• Analyze historical data to identify patterns and trends in expenses

• Consider market trends and economic indicators that may impact expenses

• Collaborate with department heads to gather budget projections and forecasts

• Use financial modeling techniques to predict future expenses based on various scenarios

• Regularly review and adjust

Major financial statements include income statement, balance sheet, and cash flow statement, which are interconnected through net income and retained earnings.

• Income statement shows revenues and expenses, resulting in net income.

• Balance sheet displays assets, liabilities, and equity, with net income affecting retained earnings.

• Cash flow statement details cash inflows and outflows, reconciling with changes in cash on th...read more

I applied to the company because of its strong reputation in the industry and its commitment to innovation and employee development.

• Reputation of the company in the industry

• Commitment to innovation

• Opportunities for employee development

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

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

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

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

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

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

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

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

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)

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

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

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

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)

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