10+ IT (Data Analytics) Interview Questions and Answers

Data analytics in banking can be used for fraud detection, customer segmentation, risk assessment, and personalized marketing.

• Fraud detection: Analyzing transaction patterns to identify suspicious activities and prevent fraudulent transactions.

• Customer segmentation: Analyzing customer data to group customers based on their behavior, preferences, and demographics for targeted marketing campaigns.

• Risk assessment: Analyzing historical data and market trends to assess credit risk...read more

As a data analyst, I can contribute to the Bank's business by providing insights and recommendations based on data analysis.

• I can help identify patterns and trends in customer behavior to improve marketing strategies.

• I can analyze financial data to identify areas for cost savings and revenue growth.

• I can develop predictive models to forecast future trends and inform business decisions.

• I can create dashboards and reports to communicate data insights to stakeholders.

• I can colla...read more

Data Flow is a Power BI feature that allows users to transform and load data from various sources into a Power BI dataset.

• Data Flow is a visual interface for building ETL (Extract, Transform, Load) processes in Power BI

• It allows users to connect to various data sources, transform data using Power Query Editor, and load the transformed data into a Power BI dataset

• Data Flow can be scheduled to refresh data on a regular basis

• Examples of data sources that can be used in Data Flow...read more

Calendar function in DAX can be used to create a date table from scratch.

• Create a new table with a column for each date component (year, month, day)

• Use the CALENDAR function to generate a range of dates

• Join the new table with the existing dataset using a common key

• Use the new date table to perform time intelligence calculations

Technical research and development can lead to amazing advancements in the field of data analytics.

• Technical research and development can lead to the creation of new tools and technologies that can improve data analytics processes.

• It can also lead to the discovery of new insights and patterns in data that were previously unknown.

• Examples of technical research and development in data analytics include the development of machine learning algorithms and the use of big data analy...read more

OLTP is for transaction processing while OLAP is for data analysis.

• OLTP is used for day-to-day operations and focuses on processing transactions in real-time.

• OLAP is used for data analysis and focuses on querying large amounts of data to gain insights.

• OLTP databases are normalized while OLAP databases are denormalized.

• OLTP databases are optimized for write operations while OLAP databases are optimized for read operations.

• Examples of OLTP systems include banking systems, airli...read more

Data security can be maintained by implementing access controls, encryption, regular backups, and monitoring.

• Implement access controls to restrict unauthorized access to data

• Use encryption to protect data in transit and at rest

• Regularly backup data to prevent loss in case of a security breach

• Monitor data access and usage to detect and prevent unauthorized activity

• Train employees on data security best practices to prevent human error

M query and DAX are both case sensitive.

• M query is case sensitive, meaning that 'Hello' and 'hello' are considered different values.

• DAX is also case sensitive, so 'SUM' and 'sum' are not the same.

• It is important to be consistent with capitalization when working with M query and DAX.

A data mart is a subset of a larger data warehouse that is designed to serve a specific business unit or department.

• Contains a subset of data from a larger data warehouse

• Designed to serve a specific business unit or department

• Provides a more focused view of data for analysis

• Can be created faster and at a lower cost than a full data warehouse

• Examples: Sales data mart, Marketing data mart

Data Analytics tools are software applications used to analyze data sets and extract insights from them.

• Tableau

• Power BI

• QlikView

• SAS

• R

• Python

• Apache Spark

• IBM Cognos Analytics

• Google Analytics

• Microsoft Excel