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1. Which of the following is/are characteristics of Big Data? a) Value b) Variety c) Velocity d) Volume e) Vanity 2. Which of the following is/are types of analytics? 6) Descriptive b) Diagnostic C) Predictive d) Prescriptive c) Nonc of the above 2 3. Which of the following statements is/are TRUE about Normalization in data preparation? a) It detects and resolves issues such as corrupt records, records with missing values, records with bad formatting, b) It is required when data from different sources uses different units or scales or have different abbreviations for the same things c) It deals with transforming the data from one raw format to another d) It may be useful when we want to process only the data that meets certain rules, Filtering can also be useful to reject bad records with incorrect or out-of- range values. e) None of the above 4. Which of the following is/are NOT Data Analytics Project Lifecycle phase? a) Operationalize b) Discovery c) Communicate Results d) Negotiation e) Comparison 5. Which of the following is the final phase of Data Analytics Project Lifecycle? a) Operationalize b) Discovery c) Communicate Results d) Negotiation e) Comparison 6. Which of the following task(s) belong to the discovery" phase of Data Analytics Project Lifecycle? a) Interviewing the Analytics Sponsor b) Developing Initial Hypotheses c) Identifying Potential Data Sources d) Assessing data quality and structure e) Learning the Business Domain 7. Which of the following task(s) belong to the operationalize" phase of Data Analytics Project Lifecycle? a) Leverage and manage costs with sponsors b) Team members communicates the benefits of the project more broadly and sets up a pilot project to deploy the work in a controlled way c) Risk is managed effectively by undertaking small scope, pilot deployment before a wide-scale roll-out. d) Test the model in a live setting, consider running the model in a production environment for a discrete set of products or a single line of business. e) Monitor model accuracy and retrain the model if necessary 8. Which of the following statements is are TRUE about the principle of divide-and- conquer in computer science? a) partition a large problem into smaller independent sub-problems b) divide common space and conquer common enemies c) partition a common CPU and occupy same memory space d) find the common denominator e) conquer the crown apps 9. Which of the following is/are valid reason(s) for the need of MapReduce? a) In supporting parallel and distributed computing, MapReduce provides an abstraction that hides many system-level details from the programmer. b) In MapReduce, developer can focus on what computations need to be performed, as opposed to how those computations are actually carried out or how to get the data to the processes that depend on them. c) Reduce the coding size d) Mapping code to code e) Map the fastest route to destination and reduce the size of RAM 10. MapReduce has its roots in functional programming, eg.. LISP. Which of the following statement(s) is/are TRUE about MapReduce's functional languages? a) A key feature of functional languages is the concept of higher-order functions, or functions that can accept other functions as arguments. b) Two common built-in higher order functions are map and fold, which are used in combination c) Given a list, map takes a functionfas an argument and applies f to all elements in a list. (The function takes a single argument.) d) The application of function to each item in a list (or more generally, to elements in a large dataset) can be parallelized, e) None of the above