10.3.2 Module 10 Quiz – Take the Next Steps Exam Answers

10.3.2 Module 10 Quiz – Take the Next Steps Exam Answers Full 100% | Data Analytics Essentials 2023 2024

This is 10.3.2 Module 10 Quiz – Take the Next Steps Exam Answers Full 100%  in 2023 and 2024. It is also module 10 quiz answers in the Cisco NetAcad SkillsForAll Data Analytics Essentials course. Our experts have verified all the answers with explanations to get the 100%.

1. Who is the intended audience of your data project portfolio?

   • your family
   • social media friends
   • prospective employers
   • fellow students
   • your teacher
     

     Answers Explanation & Hints: A data project portfolio is prepared for prospective employers to showcase your experience in data analysis.

2. Which option should be considered when developing a data project portfolio?

   • it should be organized and easy to navigate
   • it should list all your school academic qualifications
   • it should contain references to all research that you have performed
   • it should contain multiple screenshots of every data project you have completed
     

     Answers Explanation & Hints: A project portfolio should present in a well-organized and easy-to-navigate format only those data projects that you want to showcase. References to every data project and all your academic qualifications are best included in your resume.

3. Which two programming languages are most useful for data analysis? (Choose two.)

   • R
   • Python
   • C
   • C++
   • Java
     

     Answers Explanation & Hints: All programming languages have some data processing features, but Python and R have been found to be among the most useful.

4. Why is the Python programming language increasingly integral to data science?

   • the availability of many libraries to perform data related tasks
   • the ease of compiling and building executable data applications
   • by default it contains all the code elements to perform complex data analysis
   • it was developed in the 21st century for 21st century data requirements
     

     Answers Explanation & Hints: Python is an interpreted, not compiled, and easy to learn programming language developed in 1991. Python uses code libraries to provide data analysis functions.

5. Which Python code library is commonly used when developing a program to provide complex data visualization?

   • NumPy
   • Pandas
   • Matplotlib
   • Scikit-learn
     

     Answers Explanation & Hints: NumPy adds support for arrays, matrices, and other mathematical functions. Pandas adds support for tables, time series, and can be used to manipulate and clean data. Scikit-learn provides machine learning algorithms. Matplotlib supports data visualization, and is capable of creating simple line plots to complicated 3D and contour plots.

6. What is a feature of Jupyter Notebooks?

   • It provides a way to run code interactively within a web browser.
   • It compiles Python code as it is entered.
   • Notebook output is displayed as a static image.
   • It requires extensive programming experience to use.
     

     Answers Explanation & Hints: Jupyter Notebooks can be used by less experienced programmers to run code interactively within a web browser where output is displayed dynamically. Python is an interpreted, not compiled, language.

7. What is a feature of project portfolios that prospective employers consider to be important when recruiting potential data analysts?

   • each portfolio clearly and uniquely presents the data projects for the potential data analyst
   • all portfolios follow the same structure and format to make task of examining each portfolio easier
   • the resume of the candidate is clearly presented on the first screen
   • the use of only one type of media, either static graphics or videos or dashboards, not a combination
     

     Answers Explanation & Hints: Prospective employers prefer a data project portfolio to uniquely present your projects in an engaging manner, with appropriate media, and separate from your full resume.

8. What is a limitation of using spreadsheets in analysing data?

   • Changing how the data is displayed in a spreadsheet may also manipulate the data.
   • Spreadsheets are complex to learn how to use.
   • Spreadsheets are not common in organizations.
   • Unlimited dataset size makes spreadsheets complex to use.
     

     Answers Explanation & Hints: A limitation of spreadsheets is that changing how the data is displayed also manipulates it. Spreadsheets cannot contain an unlimited dataset, are relatively common in organizations, and are usually not overly complex to use.

9. What is the best way to store and manage a dataset that consists of two million records?

   • a relational database
   • a spreadsheet
   • a Python data analysis program
   • a single flat database
     

     Answers Explanation & Hints: Spreadsheets typically have a maximum data record limit of much less than two million. A Python data analysis program does not store any data records within the program. Managing two million records in a flat data base would be unwieldly and complex.