Question : What a data scientists can do with the clickstream data? A. It can be used to discover the usage patterns. B. It helps in finding the relationships C. It uncovers the relationships among clicks and areas of interest on a group of websites.
1. A,B 2. B,C 3. A,C 4. A,B,C
Correct Answer : 4 Explanation: First of all you need to understand what a clickstream data is. Clickstream data are groups of clicks in sequence made by a website visitor. For example you searched for Hadoop Learning Training on Google and google re-direct you to one of the free trainings on the Youtube.com which shows videos published by HadoopExam.com and you click the link of the HadoopExam training channel on the YouTube and then it will show you channel page and from there you click HadoopExam.com home page. On the Home Page you will be clicking on the Hadoop Professional Training. So there are almost 4 links/clicks involved Google Search result, YouTube Channel, HadoopExam home page and then finally training page. Which can help you to reach the final destination. So a data scientist can use these clickstream to find the search/usage patterns and then find the relationship between YouTube videos and the website user interested in so accordingly they can target the customer or user.
Question : What all are the benefits of using the BigData Projects which were not available in the Non-BigData solutions?
1. It gives very quick results whatever is the data volume.
2. It helps in increasing the data security, which were not available previously.
3. It helps in complex data processing as well as helps in making quick decision even for the real-time high volume data.
4. It helps in your organization technologist requirement.
5. It helps in reducing the marking team size
Correct Answer : 3 Explanation: BigData provides various benefits and more than one option can be correct as given in the question. As we need to select only one option, hence highly accurate answer is required. BigData solution provides solution for processing even complex data, and you can use various algorithm to make quick decision even you can process data in real time. Hence, option-2 are correct. Other advantages of the BigData are - It can help in reducing the cost, for the huge volume of data processing. This may not be true in all the cases. If you are looking for new research than it will require additional money, which in the long run may return higher value. - It helps in reducing the overall processing time for huge volume of data. Using the parallel processing, it saves lot of time. - New data products can be developed, by applying more analytics. Products can be customized as per the customer needs. - Analyzing Market Conditions: You can apply more analytics on the data and find out what is the market condition. - Maintaining the brand reputation: You can apply the analytics and find where the weaknesses of the brands is.
Question : Which of the following question statement falls under data science category? A. What happened in last six months? B. How many products have been sold in a last month? C. Where is a problem for sales? D. Which is the optimal scenario for selling this product? E. What happens, if these scenario continues?
1. A,B 2. B,C 3. C,D 4. D,E 5. A,E
Correct Answer : 4 Explanation: This question wants to check your understanding about BI and Data Science. BI was already existing and analytics team already using it. They need to improve and learn data science technique to solve some problems. If you check the option given in the question, it will confuse you. But if you have worked in BI or as a Data Scientist then it is easy to answer. First 3 option can be easily answered using reporting solution, what sales happened in last six month, what was the problem etc. But for the last two option you need to apply data science techniques like which all scenarios are optimal for product sales, you need to collect the data and applying various techniques for that. Hence, last two option can only be answered using Data Science technique. And for this you need to apply techniques like Optimization, predictive modeling, statistical analysis on structured and un-structured data.