Question 1: Which of the following best describes the principal goal of data science?
A. To collect and archive exhaustive data sets from various source systems for corporate record keeping uses.
B. To mine and analyze large amounts of data in order to uncover information that can be leveraged for operational improvements and business gains.
C. To prepare data for analysts to use as part of analytics applications.
Question 2: What is the primary difference between a data scientist and a data engineer?
A. A data engineer collects data, while a data scientist prepares it for analysis.
B. A data engineer analyzes data, while a data scientist prepares it for analysis.
C. A data engineer prepares data for analysis, while a data scientist does the analysis.
Question 3: A recommended strategy for establishing a data science team is:
A. Focus only on analytics skills when hiring data scientists, as that is the most important area of expertise for the job.
B. Before establishing the team, make sure your company is ready by ensuring that the proper data management frameworks and processes are in place.
C. Set up separate teams of data scientists and data engineers to make sure each job is done as efficiently as possible.
Question 4: True or false? The number of data science jobs far outnumbers the supply of data scientists.
Question 5: Some companies are integrating aspects of the scientific method into their data science processes, such as following experimental procedures. What is one benefit of doing this?
A. Applying some scientific rigor helps verify that the information produced by data science applications is accurate.
B. Making the process more scientific means that analytics applications will always be successful.
C. Data scientists can work more independently when they’re following defined experimental processes.
Question 6: True or false? Research scientists trained in disciplines such as physics don’t make good data scientists because they often lack a business or technology background.
Question 7: All of the following are examples of effective data science techniques except:
A. Designing data science projects to find solutions to existing business issues.
B. Using metrics that match analytics goals and provide the information needed to optimize business practices.
C. Making information produced by analytic applications very high-level and academic.
D. Devoting parts of the data science process to long-term analytics projects that will eventually produce business value.
Question 8: Can smaller businesses take advantage of data science initiatives even if they can’t afford the high price tag of a dedicated data science team?
A. Yes, there are other ways for them to take advantage of data science techniques.
B. No, data science is only for large organizations with sufficient resources.
Question 9: Which phrase does DevOps accurately describe/encompass/embody?
A. DevOps is a cultural approach to improving communications between the development and operations teams in an organization
B. DevOps is the term describing someone who moderates the exchanges between development and operations
C. DevOps is the name of a job for an employee who can work as both a systems engineer and a developer
D. All of the above
Question 10: The DevOps movement is an outgrowth of which software development methodology?
C. Promise-based algorithms
D. Test-driven development and model-driven development