 If you are currently enrolled in the IBM Data Engineering course on Coursera and aiming to excel in the quizzes, you may be seeking additional resources such as practice questions. Below, I have provided a set of example questions along with multiple-choice options to assist you in testing your knowledge. I hope this helps and wish you the best in your studies!
Question 1: There are two main types
of data repositories – Transactional and Analytical. For high-volume day-to-day
operational data such as banking transactions, Transactional, or OLTP, systems
are the ideal choice.
a) True
b) False
Correct Answer: Scroll Down
Question 2: Which of the following is
an example of unstructured data?
a) Zipped files
b) XML
c) Spreadsheets
d) Video and Audio files
Correct Answer: Scroll Down
Question 3: Which one of these file
formats is independent of software, hardware, and operating systems, and can be
viewed the same way on any device?
a) XML
b) PDF
c) Delimited text file
d) XLSX
Correct Answer: Scroll Down
Question 4: Which data source can
return data in plain text, XML, HTML, or JSON among others?
a) XML
b) Delimited text file
c) APIs
d) PDF
Correct Answer: Scroll Down
Question 5: In the data engineer’s
ecosystem, languages are classified by type. What are shell and scripting
languages most commonly used for?
a) Automating repetitive operational tasks
b) Querying data
c) Building apps
d) Manipulating data
Correct Answer: Scroll Down
By practicing these questions, you'll be better prepared for your IBM
Data Engineering quizzes on Coursera. Remember, consistent practice and
understanding the concepts behind each question is key to success. Good luck
with your studies!
Keywords:
- IBM Data
Engineering
- Coursera
quizzes
- Data
Engineering practice questions
- Data
pipeline ETL
- Data
warehouses
- Software
development lifecycle
- Unstructured
data
- Transactional
systems
Prepare for your IBM Data Engineering quizzes on Coursera with these
practice questions and answers. Test your knowledge and enhance your
understanding of data engineering concepts.