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MCQs | Big Data Analytics – Predictive Analytics

Big Data Analytics | Predictive Analytics MCQs: This section contains the Multiple-Choice Questions & Answers on Big Data Analytics - Predictive Analytics with explanations.
Submitted by IncludeHelp, on January 02, 2022

1. Predictive analytics uses statistics and ____ to determine future performance.

  1. Algorithmic techniques
  2. Modeling techniques
  3. System development and design techniques
  4. None of the mentioned above

Answer: B) Modeling techniques

Explanation:

Predictive analytics uses statistics and modeling techniques to determine future performance. Predictive models help make weather forecasts, develop video games, translate voice-to-text messages, customer service decisions, and develop investment portfolios. Predictive models include decision trees, regression, and neural networks.


2. Amongst which of the following is / are the applications of Predictive Analytics,

  1. Translating voice to text for mobile phone messaging
  2. Investment portfolio development
  3. Weather forecasts
  4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

The applications of Predictive Analytics are Translating voice to text for mobile phone messaging, Investment portfolio development, Weather forecasts, Customer service and many more.


3. Organizations are turning to predictive analytics to increase their bottom line and competitive advantage.

  1. True
  2. False

Answer: A) True

Explanation:

Organizations are turning to predictive analytics to increase their bottom line and competitive advantage to growing volumes and types of data, and more interest in using data to produce valuable insights, detecting fraud, and optimizing marketing campaigns, improving operations and reducing risk.


4. Business intelligence systems do not help businesses make better decisions by collecting, storing, analyzing and reporting on past data,

  1. True
  2. False

Answer: B) False

Explanation:

Business intelligence systems do not help businesses make better decisions by collecting, storing, analyzing and reporting on past data. Business Intelligence platforms have evolved to accommodate big data and emerging technologies such as cloud computing, IoT and artificial intelligence.


5. Companies use predictive analytics models to forecast inventory, manage resources, and operate more efficiently.

  1. True
  2. False

Answer: A) True

Explanation:

Companies use predictive analytics models to forecast inventory, manage resources, and operate more efficiently. By dividing a customer base into specific groups, marketers can use predictive analytics to make forward-looking decisions to tailor content to unique audiences.


6. Organizations use data to predict when routine equipment maintenance will be required and can then schedule it before a problem or malfunction arises.

  1. True
  2. False

Answer: A) True

Explanation:

Organizations use data to predict when routine equipment maintenance will be required and can then schedule it before a problem or malfunction arises. Companies can take actions, like retargeting online ads to visitors, with data that predicts a greater likelihood of conversion and purchase intent.


7. Predictive analytics is a process harnesses ____, often massive, data sets into models.

  1. Heterogeneous
  2. Storage
  3. Network
  4. None of the mentioned above

Answer: A) Heterogeneous

Explanation:

The process harnesses heterogeneous, often massive, data sets into models that can generate clear, actionable outcomes to support achieving that goal, such as less material waste, less stocked inventory, and manufactured product that meets specifications.


8. Amongst which of the following is / are the correct workflow of predictive analytics,

  1. Import data → Clean the data → Develop a predictive model → Integrate the model
  2. Clean the data → Develop a predictive model → Import data → Integrate the model
  3. Clean the data → Develop a predictive model → Import data → Integrate the model
  4. None of the mentioned above

Answer: A) Import data → Clean the data → Develop a predictive model → Integrate the model

Explanation:

Before to do predictive analytics, we should follow some sequence. This is like Import data from varied sources, such as web archives, databases, and spreadsheets, Clean the data by removing outliers and combining data sources, develop an accurate predictive model based on the aggregated data using statistics, curve fitting tools, or machine learning and integrate the model into a load forecasting system.


9. Predictive analytics relies on capturing relationships between explanatory variables and the _____.

  1. Predicted variables
  2. Descriptive variables
  3. Prescriptive variables
  4. All of the mentioned above

Answer: A) Predicted variables

Explanation:

Predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.


10. Amongst which of the following is / are the types of predictive analytics techniques,

  1. Predictive models
  2. Descriptive models
  3. Decision models
  4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

There are three types of predictive analytics techniques: predictive models, descriptive models, and decision models. The predictive analytics method begins with defining business objectives and the datasets to be used, followed by the development of a statistical model that is trained to validate assumptions and run them against selected data to generate predictions.


11. Machine learning predictive analytics is a category of algorithm that can receive input data and use statistical analysis to predict outputs,

  1. True
  2. False

Answer: A) True

Explanation:

Machine learning predictive analytics is a category of algorithm that can receive input data and use statistical analysis to predict outputs while updating outputs as new data becomes available. This allows software applications to become more accurate in predicting outcomes without being explicitly programmed.


12. Amongst which of the following is / are true with reference to predictive modeling,

  1. A tool used in predictive analytics
  2. A process that uses data mining and statistics to develop models
  3. Examine current and historical datasets for underlying patterns
  4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

Predictive modeling, a tool used in predictive analytics, is a process that uses data mining and statistics to develop models that examine current and historical datasets for underlying patterns and predict the probability of an outcome. The predictive modeling process starts with data collection, then a statistical model is formulated, predictions are made, and the model is revised as new data becomes available.


13. Predictive modeling is used throughout a range of industries, including meteorology, archaeology, automobile insurance, and algorithmic trading,

  1. True
  2. False

Answer: A) True

Explanation:

Predictive modeling is used throughout a range of industries, including meteorology, archaeology, automobile insurance, and algorithmic trading. When deployed commercially, predictive modeling is often referred to as predictive analytics.


14. Machine learning in predictive analytics uses to enable computers to learn without being explicitly programmed by building algorithms that can receive _____ and use statistics to predict an output.

  1. Input data
  2. Output data
  3. Process data
  4. All of the mentioned above

Answer: A) Input data

Explanation:

Machine learning in predictive analytics uses to enable computers to learn without being explicitly programmed by building algorithms that can receive Input data and use statistics to predict an output.


15. Amongst which of the following is / are true with reference to Decision trees,

  1. A learning model
  2. Uses observations
  3. Develop conclusions
  4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

Decision tree is a learning model that uses observations about a specific item to develop conclusions about the item's target value.


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