Question
Statements: No lessons are topics. All
topics are headings. Some headings are introductions. All introductions are conclusions. Conclusions: I. Some lessons are introductions. II. Some conclusions are headings. III. Some headings are not conclusions. IV. Some lessons are not conclusions. In each of the questions below are given four statements followed by four conclusions numbered I, II and III and IV. You have to take the given statements to be true even if they seem to be at variance with commonly known facts. Read all the conclusions and then decide which of given conclusions logically follows from the given statements disregarding commonly known facts .Solution
No lessons are topics (E) + All topics are headings (A) = Some lessons are not headings (O), so further we can derive anything from it, hence conclusion I doesn’t follows and IV also doesn’t follows. Some headings are introductions.(I) + All introductions are conclusions.= Some headings are conclusions (A) ⟹  conversion ⟹  Some conclusions are headings. Hence conclusion II will follow but conclusion III doesn’t follows. Alternate Method:
Which of the following forecasting methods is most suitable for data with a linear trend but no seasonality?
Which layer in the OSI model is responsible for end-to-end communication and error-free delivery of data between hosts?
Which data transformation technique would be best for converting categorical variables, such as “Gender” (Male, Female), into a format usable in mac...
In defining KPIs for a marketing campaign, what is the most critical aspect a data analyst should ensure?
In file systems, which allocation method results in the maximum random access performance?
In developing a fraud detection model for online transactions, a data analyst should use a technique capable of identifying anomalous patterns. Which of...
A bank notices unusual activity in a customer’s account, such as multiple large withdrawals in a short period. What is the most appropriate technique...
Which technique is most appropriate to handle skewed numerical data in a dataset?
You are tasked with analyzing sales data from multiple sources for a quarterly report. The raw data contains missing values and duplicate records. What ...
Which of the following Excel functions is most appropriate for dynamically summarizing data from multiple tables by matching a key value?