Assessment 2
INTS4000
Internship
Generative AI Analysis
Student ID:
Student Name:
Question 1:
Copy the following prompt into ChatGPT:
Simulate a job interview for a [Job Title] by asking four job interview questions as if you are a potential employer recruiting for the following position:
[Insert Job Advertisement]
In this scenario, I am taking the role of the employee and you ask four job interview questions to screen my competence.
Sure! Let's simulate a job interview. Please provide the job title and a brief overview of the job advertisement so I can tailor the questions appropriately.
Question 2:
Copy and paste the job advertisement that you chose for Assessment 1 into the [Insert Job Advertisement] section of the prompt, change [Job Title] to the title of the job in the advertisement, and click ‘Send message’.
Can you describe a project where you utilized SQL to solve a complex data issue? What was the challenge, and what was the outcome?
Given your experience with cloud platforms like AWS, how have you leveraged cloud technologies in your previous roles to enhance data analysis processes?
Data warehousing is crucial for efficient data analysis. Can you explain your understanding of data warehousing concepts and share any specific experiences you've had in implementing these practices?
In the context of the banking or financial services industry, can you provide an example of a data-driven decision you influenced? What data did you analyze, and what impact did your analysis have on the business?
Question 3:
Simulate a job interview for a [Job Title] by answering four interview questions as if you are a potential employee. In this scenario, I am taking the role of the employer and will ask you four interview questions to screen your competence. Base your answers on the following resume:
[Insert Resume with Personal Information Removed]
[Insert Four Job Interview Questions]
I can help you simulate that! Please provide me with the job title you’d like to focus on and the four interview questions. If you have specific details about the resume you want me to use as a reference, feel free to share those as well.
Question 4:
Copy and paste your resume with the personal information removed into the [Insert Resume with Personal Information Removed] section of the prompt, change [Job Title] to the title of the job in the advertisement, copy and paste the four job interview questions from Steps 1-2 into [Insert Four Job Interview Questions] section of the prompt and click ‘Send message’.
ChatGPT content
Question 1:
Can you describe a complex data analysis project you worked on in the banking or financial services industry? What tools did you use, and what was the impact of your analysis on the business?
While I haven’t yet worked specifically in the banking or financial services industry, during my internship, I was involved in a project that simulated a financial data analysis scenario. I collected and cleaned data from various sources to ensure its integrity. Using Python, particularly libraries like pandas and NumPy, I conducted exploratory data analysis to identify trends related to customer transactions.
I created visualizations using Tableau to illustrate these findings, which helped the team present insights on customer behavior patterns to management. This analysis guided the decision-making process for targeted marketing strategies, which ultimately improved customer engagement by 15%.
This answer to the Chat-GPT prompt does not effectively match my experience and knowledge. For the development of an effective answer, I was required to provide an accurate prompt. This prompt added me to a project related to financial data analysis. However, I did not complete any kind of project associated with the financial analysis. This answer does not meet my requirements for a resume. I did not complete any type of group project related to financial data analysis. This prompt provides some lines accurate as I don’t have any work experience in the financial or banking sector.
While I haven’t yet worked specifically in the banking or financial services industry, during my internship, I was involved in a group project that simulated the health care data analysis. I cleaned and collected the data from several sources. By use of Python libraries such as Numpy, and Pandas, I conducted EDA to identify the trends related to customer health. I developed visualization by use of powerBI and Tableau to represent findings, which helped the team present insight into the customer health pattern. This analysis helps me develop of decision-making procedure for reducing the
Question 2:
Given your experience with SQL, can you explain how you would optimize a slow-running query? Please provide an example of a situation where you successfully improved query performance.
My SQL experience is more foundational, but I understand several optimization techniques. If faced with a slow-running query, I would start by analyzing the query execution plan to identify bottlenecks. Common strategies include indexing relevant columns, avoiding SELECT *, and using JOINs efficiently.
In a project during my internship, I noticed that a report was taking a long time to run due to a lack of indexing on a frequently queried column. I suggested adding an index to that column, which significantly improved query performance and reduced run time by nearly 40%.
Question 3:
How have you utilized AWS or another cloud platform in your previous roles? Can you give an example of how cloud tools helped you streamline data analysis or reporting?
I have limited direct experience with AWS, but I am eager to learn and apply cloud technologies. I participated in a university project where we used Google Cloud to store and analyze large datasets. By utilizing cloud storage, we could easily share datasets among team members and access them remotely.
For example, we used Google BigQuery for our data analysis, which allowed us to run complex queries quickly without the need for local infrastructure. This not only streamlined our workflow but also facilitated real-time collaboration among team members, which enhanced our overall productivity.
Question 4:
Data warehousing is crucial for effective data analysis. Could you explain your understanding of data warehousing concepts and how you have applied them in your work? What best practices do you follow when working with data warehouses?
I understand data warehousing as a centralized repository that allows for the efficient collection, storage, and retrieval of data for analysis. It typically involves ETL (Extract, Transform, Load) processes to integrate data from various sources into a cohesive structure.
In my internship, I learned about the importance of structuring data for analytical purposes. Although I didn’t work directly with a data warehouse, I supported the team by helping clean and organize data, ensuring it was ready for analysis.
Best practices I believe in include maintaining data quality, regularly updating and archiving data, and documenting data sources and transformations to ensure transparency and reproducibility in analysis.
Appendix
1. Can you describe a project where you utilized SQL to solve a complex data issue? What was the challenge, and what was the outcome?
ChatGPT answer
In my previous role as a Developer at Team Computers, I worked on a project that involved integrating multiple data sources into a centralized Qlik Sense application. The challenge was that the data was coming from various formats and sources, which made it difficult to analyze effectively. I utilized SQL to clean, transform, and load the data into our database, ensuring that it was properly formatted and validated. By creating complex queries to join datasets, I improved data accessibility and efficiency by around 30%. This led to more informed decision-making by stakeholders, as they could easily access the relevant information.
Expand answer:
At Team Computer Pvt Ltd, as a developer in my previous roles, I developed and maintained a project that focused on the integration of multiple data sources into a centralized Qlik Sense application. During this development of the project, I faced several challenges because the data was collected from various sources and formats, which created difficulty in analyzing it effectively. To address this type of issue in this project, it effectively utilizes SQL to transform, clean, and load the data into the database and ensure that this data is in proper format and validated. In this project, I use complex queries to join the database, which helps in improving the accessibility of data and analysis efficiently, approximately 30%. This transformation vitally improved the ability of stakeholders to develop effective decisions. This can be easily interpreted, and effectively access the relevant data.
2. Given your experience with cloud platforms like AWS, how have you leveraged cloud technologies in your previous roles to enhance data analysis processes?
ChatGPT answer
While my primary experience has been with Qlik applications, I have collaborated with teams that used AWS for data storage and analysis. I assisted in setting up ETL processes to transfer data from our on-premises databases to AWS, which significantly improved our data processing speed and scalability. The cloud environment allowed us to handle larger datasets more efficiently and provided flexible computing resources for our data analysis tasks. This transition enabled our team to perform more complex analyses and deliver insights more quickly to our business users.
Expand answer:
My primary experience has been with the Qlik application; in this, I effectively collaborated with the team that effectively utilized AWS for data analysis and storage. In this, I play an important role in setting up the ETL procedure to seamlessly transfer the data from our on-premises database to AWS, which outcomes in a significant improvement in data processing scalability and speeds. In this cloud environment, flexibility allows us to efficiently handle the large amount of data set effectively and provide flexible computing resources for complex data analysis tasks. This type of transition enables the team to efficiently deliver actionable insights to the users of the business at a faster pace, ultimately enhancing the process of decision-making.
3. Data warehousing is crucial for efficient data analysis. Can you explain your understanding of data warehousing concepts and share any specific experiences you've had in implementing these practices?
ChatGPT answer
Data warehousing involves the consolidation of data from various sources into a single repository, making it easier to analyze and generate reports. It typically involves processes like ETL (Extract, Transform, Load) to ensure data quality and consistency. In my role, I designed and implemented data models and ETL processes for our Qlik applications, which were key in creating a robust data warehouse. I focused on ensuring that the data was well-structured and accessible for reporting and analytics, which enhanced our team’s ability to derive insights quickly and efficiently.
Expand answer:
Data warehousing is an important aspect that involves data consolidation from diverse sources into a unified or single repository and makes it easier to streamline the analysis and development of reports. This aspect generally involves the procedure as ETL (Extract, Transform, and Load) to maintain or ensure consistency and data quality. During my role as a developer, I was responsible for the implementation and design of the comprehensive ETL process and data model for the Qlik applications, which play an important role in the development of scalable and robust data warehouses. During this, I focus on ensuring that data is effectively structured and it is easily accessible for analytics and reporting, which significantly improves the ability of our team to derive actionable insight efficiently.
4. In the context of the banking or financial services industry, can you provide an example of a data-driven decision you influenced? What data did you analyze, and what impact did your analysis have on the business?
ChatGPT answer
Although my direct experience in banking is limited, I applied data-driven decision-making principles while working with financial datasets in my previous role. For instance, I analyzed customer transaction data to identify spending patterns and potential fraud risks. By creating visual dashboards in Qlik, I presented my findings to management, highlighting areas where additional controls were necessary. This analysis led to the implementation of more stringent fraud detection measures, ultimately reducing fraudulent transactions by about 15%. My work contributed to enhancing the organization's overall financial security and customer trust.
Expand answer:
In the banking and finance sector, my direct experience is limited; I have frequently applied data-driven decision-making principles when working with financial datasets in my previous roles. During this, I effectively analyzed the data of customer transactions to identify the potential fraud risk and spending time. I created detailed visual dashboards using Qlik; in this, I efficiently presented my findings to the management and efficiently highlighted those areas where additional controls were required. As an outcome of this analysis, the organization implements fraud detection measures, which successfully decrease fraudulent transactions by approximately 15%. This contribution of my work not only improved the organization's financial security but also vitally bolstered customer satisfaction and trust.
