Marketing Analytics Exam 1 WITH ANSWERS

Marketing Analytics Exam 1 WITH |\ |\ |\ |\ |\




ANSWERS

_______ is useful when considering categorical variables.
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Dummy coding |\ |\




New column |\




Aggregation


Normalization - CORRECT ANSWERS ✔✔Dummy coding |\ |\ |\ |\ |\




Cognitive analytics and knowledge discovery applications
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use advanced capabilities to ________ and ________ hidden
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in large volumes of data.
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Clean data & conduct price optimization
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Draw conclusions & develop insights
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Ensure a perception of seamless transitions & improve
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,computational performance |\ |\




Communicate & extract design elements - CORRECT |\ |\ |\ |\ |\ |\ |\



ANSWERS ✔✔Draw conclusions & develop insights
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The cognitive technology of ________ facilitates the
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extraction of text in a readable, natural form through
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translators and chatbots. |\ |\




Deep learning
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Computer vision |\




Machine Learning |\




Natural language processing - CORRECT ANSWERS
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✔✔Natural language processing |\ |\




Which of the following offers the ability to answer the
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question "What should happen?" in marketing analytics?
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Forecasting

,Data query |\ |\




Image recognition
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Text recognition - CORRECT ANSWERS ✔✔Forecasting
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Showing a subset of raw data is an effective way to
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communicate a story based on data. |\ |\ |\ |\ |\ |\




True or False - CORRECT ANSWERS ✔✔False
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Which of following characteristics of big data means the
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data can be converted into meaningful, quality
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information that can be used to achieve tangible business
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benefits?
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Value


Velocity |\




Volume


Variety - CORRECT ANSWERS ✔✔Value
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, Which of the following cognitive technologies extracts
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meaning from image pixels such as faces, objects, and
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scenes?


Deep learning
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Computer vision |\




Machine learning |\




Natural language processing - CORRECT ANSWERS
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✔✔Computer vision |\




Identify a difference between natural language processing
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(NLP) and computer vision.
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Computer vision technology supports virtual assistants,
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whereas NLP supports image identification.
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NLP extracts meaning from text, whereas computer vision
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extracts meaning from image pixels.
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