CAT Past Year Question Paper with Solution - 2018 Slot 2 CAT Notes | EduRev

CAT Mock Test Series 2020

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 Page 1


CAT 2018 Paper SLOT 2 [SOLVED]           
The complexity of modern problems often precludes any one person from fully understanding 
them. Factors contributing to rising obesity levels, for example, include transportation systems 
and infrastructure, media, convenience foods, changing social norms, human biology and 
psychological factors. . . . The multidimensional or layered character of complex problems also 
undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is 
no best person. When putting together an oncological research team, a biotech company such as 
Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire 
people whose resumes score highest according to some performance criteria. Instead, they would 
seek diversity. They would build a team of people who bring diverse knowledge bases, tools and 
analytic skills. . . . 
Believers in a meritocracy might grant that teams ought to be diverse but then argue that 
meritocratic principles should apply within each category. Thus the team should consist of the 
‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. 
That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria 
applied to individuals will produce the best team. Each of these domains possesses such depth 
and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers 
were published last year covering various techniques, domains of enquiry and levels of analysis, 
ranging from molecules and synapses up through networks of neurons. Given that complexity, 
any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors 
in the 50-metre butterfly, must fail. What could be true is that given a specific task and the 
composition of a particular team, one scientist would be more likely to contribute than another. 
Optimal hiring depends on context. Optimal teams will be diverse. 
Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas 
tend to rank as high-impact. It can also be found in the structure of the so-called random decision 
forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of 
decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? 
A weighted majority rules. Random forests can serve many ends. They can identify bank fraud 
and diseases, recommend ceiling fans and predict online dating behaviour. When building a 
forest, you do not select the best trees as they tend to make similar classifications. You want 
diversity. Programmers achieve that diversity by training each tree on different data, a technique 
Page 2


CAT 2018 Paper SLOT 2 [SOLVED]           
The complexity of modern problems often precludes any one person from fully understanding 
them. Factors contributing to rising obesity levels, for example, include transportation systems 
and infrastructure, media, convenience foods, changing social norms, human biology and 
psychological factors. . . . The multidimensional or layered character of complex problems also 
undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is 
no best person. When putting together an oncological research team, a biotech company such as 
Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire 
people whose resumes score highest according to some performance criteria. Instead, they would 
seek diversity. They would build a team of people who bring diverse knowledge bases, tools and 
analytic skills. . . . 
Believers in a meritocracy might grant that teams ought to be diverse but then argue that 
meritocratic principles should apply within each category. Thus the team should consist of the 
‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. 
That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria 
applied to individuals will produce the best team. Each of these domains possesses such depth 
and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers 
were published last year covering various techniques, domains of enquiry and levels of analysis, 
ranging from molecules and synapses up through networks of neurons. Given that complexity, 
any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors 
in the 50-metre butterfly, must fail. What could be true is that given a specific task and the 
composition of a particular team, one scientist would be more likely to contribute than another. 
Optimal hiring depends on context. Optimal teams will be diverse. 
Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas 
tend to rank as high-impact. It can also be found in the structure of the so-called random decision 
forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of 
decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? 
A weighted majority rules. Random forests can serve many ends. They can identify bank fraud 
and diseases, recommend ceiling fans and predict online dating behaviour. When building a 
forest, you do not select the best trees as they tend to make similar classifications. You want 
diversity. Programmers achieve that diversity by training each tree on different data, a technique 
CAT 2018 Paper SLOT 2 [SOLVED]           
known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases 
– those that the current forest gets wrong. This ensures even more diversity and accurate forests. 
Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and 
even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. 
Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to 
breakthroughs. 
 
Q 1: Which of the following conditions, if true, would invalidate the passage’s main argument? 
1. If top-scorers possessed multidisciplinary knowledge that enabled them to look at a 
problem from several perspectives. 
2. If assessment tests were made more extensive and rigorous. 
3. If it were proven that teams characterised by diversity end up being conflicted about 
problems and take a long time to arrive at a solution. 
4. If a new machine-learning algorithm were developed that proved to be more effective 
than the random decision forest. 
 
Q 2: The author critiques meritocracy for all the following reasons EXCEPT that: 
1. an ideal team comprises of best individuals from diverse fields of knowledge. 
2. modern problems are multifaceted and require varied skill-sets to be solved. 
3. criteria designed to assess merit are insufficient to test expertise in any field of 
knowledge. 
4. diversity and context-specificity are important for making major advances in any field. 
 
Q 3: Which of the following conditions would weaken the efficacy of a random decision forest? 
1. If a large number of decision trees in the ensemble were trained on data derived from 
easy cases. 
2. If the types of decision trees in each ensemble of the forest were doubled. 
Page 3


CAT 2018 Paper SLOT 2 [SOLVED]           
The complexity of modern problems often precludes any one person from fully understanding 
them. Factors contributing to rising obesity levels, for example, include transportation systems 
and infrastructure, media, convenience foods, changing social norms, human biology and 
psychological factors. . . . The multidimensional or layered character of complex problems also 
undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is 
no best person. When putting together an oncological research team, a biotech company such as 
Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire 
people whose resumes score highest according to some performance criteria. Instead, they would 
seek diversity. They would build a team of people who bring diverse knowledge bases, tools and 
analytic skills. . . . 
Believers in a meritocracy might grant that teams ought to be diverse but then argue that 
meritocratic principles should apply within each category. Thus the team should consist of the 
‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. 
That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria 
applied to individuals will produce the best team. Each of these domains possesses such depth 
and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers 
were published last year covering various techniques, domains of enquiry and levels of analysis, 
ranging from molecules and synapses up through networks of neurons. Given that complexity, 
any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors 
in the 50-metre butterfly, must fail. What could be true is that given a specific task and the 
composition of a particular team, one scientist would be more likely to contribute than another. 
Optimal hiring depends on context. Optimal teams will be diverse. 
Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas 
tend to rank as high-impact. It can also be found in the structure of the so-called random decision 
forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of 
decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? 
A weighted majority rules. Random forests can serve many ends. They can identify bank fraud 
and diseases, recommend ceiling fans and predict online dating behaviour. When building a 
forest, you do not select the best trees as they tend to make similar classifications. You want 
diversity. Programmers achieve that diversity by training each tree on different data, a technique 
CAT 2018 Paper SLOT 2 [SOLVED]           
known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases 
– those that the current forest gets wrong. This ensures even more diversity and accurate forests. 
Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and 
even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. 
Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to 
breakthroughs. 
 
Q 1: Which of the following conditions, if true, would invalidate the passage’s main argument? 
1. If top-scorers possessed multidisciplinary knowledge that enabled them to look at a 
problem from several perspectives. 
2. If assessment tests were made more extensive and rigorous. 
3. If it were proven that teams characterised by diversity end up being conflicted about 
problems and take a long time to arrive at a solution. 
4. If a new machine-learning algorithm were developed that proved to be more effective 
than the random decision forest. 
 
Q 2: The author critiques meritocracy for all the following reasons EXCEPT that: 
1. an ideal team comprises of best individuals from diverse fields of knowledge. 
2. modern problems are multifaceted and require varied skill-sets to be solved. 
3. criteria designed to assess merit are insufficient to test expertise in any field of 
knowledge. 
4. diversity and context-specificity are important for making major advances in any field. 
 
Q 3: Which of the following conditions would weaken the efficacy of a random decision forest? 
1. If a large number of decision trees in the ensemble were trained on data derived from 
easy cases. 
2. If the types of decision trees in each ensemble of the forest were doubled. 
CAT 2018 Paper SLOT 2 [SOLVED]           
3. If a large number of decision trees in the ensemble were trained on data derived from 
easy and hard cases. 
4. If the types of ensembles of decision trees in the forest were doubled. 
 
Q 4: On the basis of the passage, which of the following teams is likely to be most effective in 
solving the problem of rising obesity levels? 
1. A team comprised of nutritionists, psychologists, urban planners and media personnel, 
who have each scored a distinction in their respective subject tests. 
2. A team comprised of nutritionists, psychologists, urban planners and media personnel, 
who have each performed well in their respective subject tests. 
3. A specialised team of nutritionists from various countries, who are also trained in the 
machine-learning algorithm of random decision forest. 
4. A specialised team of top nutritionists from various countries, who also possess some 
knowledge of psychology. 
 
Q 5: Which of the following best describes the purpose of the example of neuroscience? 
1. Unlike other fields of knowledge, neuroscience is an exceptionally complex field, making 
a meaningful assessment of neuroscientists impossible. 
2. In narrow fields of knowledge, a meaningful assessment of expertise has always been 
possible. 
3. Neuroscience is an advanced field of science because of its connections with other 
branches of science like oncology and biostatistics. 
4. In the modern age, every field of knowledge is so vast that a meaningful assessment of 
merit is impossible. 
Grove snails as a whole are distributed all over Europe, but a specific variety of the snail, with a 
distinctive white-lipped shell, is found exclusively in Ireland and in the Pyrenees mountains that 
lie on the border between France and Spain. The researchers sampled a total of 423 snail 
specimens from 36 sites distributed across Europe, with an emphasis on gathering large numbers 
Page 4


CAT 2018 Paper SLOT 2 [SOLVED]           
The complexity of modern problems often precludes any one person from fully understanding 
them. Factors contributing to rising obesity levels, for example, include transportation systems 
and infrastructure, media, convenience foods, changing social norms, human biology and 
psychological factors. . . . The multidimensional or layered character of complex problems also 
undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is 
no best person. When putting together an oncological research team, a biotech company such as 
Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire 
people whose resumes score highest according to some performance criteria. Instead, they would 
seek diversity. They would build a team of people who bring diverse knowledge bases, tools and 
analytic skills. . . . 
Believers in a meritocracy might grant that teams ought to be diverse but then argue that 
meritocratic principles should apply within each category. Thus the team should consist of the 
‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. 
That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria 
applied to individuals will produce the best team. Each of these domains possesses such depth 
and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers 
were published last year covering various techniques, domains of enquiry and levels of analysis, 
ranging from molecules and synapses up through networks of neurons. Given that complexity, 
any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors 
in the 50-metre butterfly, must fail. What could be true is that given a specific task and the 
composition of a particular team, one scientist would be more likely to contribute than another. 
Optimal hiring depends on context. Optimal teams will be diverse. 
Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas 
tend to rank as high-impact. It can also be found in the structure of the so-called random decision 
forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of 
decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? 
A weighted majority rules. Random forests can serve many ends. They can identify bank fraud 
and diseases, recommend ceiling fans and predict online dating behaviour. When building a 
forest, you do not select the best trees as they tend to make similar classifications. You want 
diversity. Programmers achieve that diversity by training each tree on different data, a technique 
CAT 2018 Paper SLOT 2 [SOLVED]           
known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases 
– those that the current forest gets wrong. This ensures even more diversity and accurate forests. 
Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and 
even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. 
Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to 
breakthroughs. 
 
Q 1: Which of the following conditions, if true, would invalidate the passage’s main argument? 
1. If top-scorers possessed multidisciplinary knowledge that enabled them to look at a 
problem from several perspectives. 
2. If assessment tests were made more extensive and rigorous. 
3. If it were proven that teams characterised by diversity end up being conflicted about 
problems and take a long time to arrive at a solution. 
4. If a new machine-learning algorithm were developed that proved to be more effective 
than the random decision forest. 
 
Q 2: The author critiques meritocracy for all the following reasons EXCEPT that: 
1. an ideal team comprises of best individuals from diverse fields of knowledge. 
2. modern problems are multifaceted and require varied skill-sets to be solved. 
3. criteria designed to assess merit are insufficient to test expertise in any field of 
knowledge. 
4. diversity and context-specificity are important for making major advances in any field. 
 
Q 3: Which of the following conditions would weaken the efficacy of a random decision forest? 
1. If a large number of decision trees in the ensemble were trained on data derived from 
easy cases. 
2. If the types of decision trees in each ensemble of the forest were doubled. 
CAT 2018 Paper SLOT 2 [SOLVED]           
3. If a large number of decision trees in the ensemble were trained on data derived from 
easy and hard cases. 
4. If the types of ensembles of decision trees in the forest were doubled. 
 
Q 4: On the basis of the passage, which of the following teams is likely to be most effective in 
solving the problem of rising obesity levels? 
1. A team comprised of nutritionists, psychologists, urban planners and media personnel, 
who have each scored a distinction in their respective subject tests. 
2. A team comprised of nutritionists, psychologists, urban planners and media personnel, 
who have each performed well in their respective subject tests. 
3. A specialised team of nutritionists from various countries, who are also trained in the 
machine-learning algorithm of random decision forest. 
4. A specialised team of top nutritionists from various countries, who also possess some 
knowledge of psychology. 
 
Q 5: Which of the following best describes the purpose of the example of neuroscience? 
1. Unlike other fields of knowledge, neuroscience is an exceptionally complex field, making 
a meaningful assessment of neuroscientists impossible. 
2. In narrow fields of knowledge, a meaningful assessment of expertise has always been 
possible. 
3. Neuroscience is an advanced field of science because of its connections with other 
branches of science like oncology and biostatistics. 
4. In the modern age, every field of knowledge is so vast that a meaningful assessment of 
merit is impossible. 
Grove snails as a whole are distributed all over Europe, but a specific variety of the snail, with a 
distinctive white-lipped shell, is found exclusively in Ireland and in the Pyrenees mountains that 
lie on the border between France and Spain. The researchers sampled a total of 423 snail 
specimens from 36 sites distributed across Europe, with an emphasis on gathering large numbers 
CAT 2018 Paper SLOT 2 [SOLVED]           
of the white-lipped variety. When they sequenced genes from the mitochondrial DNA of each of 
these snails and used algorithms to analyze the genetic diversity between them, they found that. . 
. a distinct lineage (the snails with the white-lipped shells) was indeed endemic to the two very 
specific and distant places in question. 
Explaining this is tricky. Previously, some had speculated that the strange distributions of 
creatures such as the white-lipped grove snails could be explained by convergent evolution—in 
which two populations evolve the same trait by coincidence—but the underlying genetic 
similarities between the two groups rules that out. Alternately, some scientists had suggested that 
the white-lipped variety had simply spread over the whole continent, then been wiped out 
everywhere besides Ireland and the Pyrenees, but the researchers say their sampling and 
subsequent DNA analysis eliminate that possibility too. “If the snails naturally colonized Ireland, 
you would expect to find some of the same genetic type in other areas of Europe, especially 
Britain. We just don’t find them,” Davidson, the lead author, said in a press statement. 
Moreover, if they’d gradually spread across the continent, there would be some genetic variation 
within the white-lipped type, because evolution would introduce variety over the thousands of 
years it would have taken them to spread from the Pyrenees to Ireland. That variation doesn’t 
exist, at least in the genes sampled. This means that rather than the organism gradually 
expanding its range, large populations instead were somehow moved en mass to the other 
location within the space of a few dozen generations, ensuring a lack of genetic variety. 
“There is a very clear pattern, which is difficult to explain except by involving humans,” 
Davidson said. Humans, after all, colonized Ireland roughly 9,000 years ago, and the oldest fossil 
evidence of grove snails in Ireland dates to roughly the same era. Additionally, there is 
archaeological evidence of early sea trade between the ancient peoples of Spain and Ireland via 
the Atlantic and even evidence that humans routinely ate these types of snails before the advent 
of agriculture, as their burnt shells have been found in Stone Age trash heaps. 
The simplest explanation, then? Boats. These snails may have inadvertently traveled on the floor 
of the small, coast-hugging skiffs these early humans used for travel, or they may have been 
intentionally carried to Ireland by the seafarers as a food source. “The highways of the past were 
rivers and the ocean–as the river that flanks the Pyrenees was an ancient trade route to the 
Page 5


CAT 2018 Paper SLOT 2 [SOLVED]           
The complexity of modern problems often precludes any one person from fully understanding 
them. Factors contributing to rising obesity levels, for example, include transportation systems 
and infrastructure, media, convenience foods, changing social norms, human biology and 
psychological factors. . . . The multidimensional or layered character of complex problems also 
undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is 
no best person. When putting together an oncological research team, a biotech company such as 
Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire 
people whose resumes score highest according to some performance criteria. Instead, they would 
seek diversity. They would build a team of people who bring diverse knowledge bases, tools and 
analytic skills. . . . 
Believers in a meritocracy might grant that teams ought to be diverse but then argue that 
meritocratic principles should apply within each category. Thus the team should consist of the 
‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. 
That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria 
applied to individuals will produce the best team. Each of these domains possesses such depth 
and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers 
were published last year covering various techniques, domains of enquiry and levels of analysis, 
ranging from molecules and synapses up through networks of neurons. Given that complexity, 
any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors 
in the 50-metre butterfly, must fail. What could be true is that given a specific task and the 
composition of a particular team, one scientist would be more likely to contribute than another. 
Optimal hiring depends on context. Optimal teams will be diverse. 
Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas 
tend to rank as high-impact. It can also be found in the structure of the so-called random decision 
forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of 
decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? 
A weighted majority rules. Random forests can serve many ends. They can identify bank fraud 
and diseases, recommend ceiling fans and predict online dating behaviour. When building a 
forest, you do not select the best trees as they tend to make similar classifications. You want 
diversity. Programmers achieve that diversity by training each tree on different data, a technique 
CAT 2018 Paper SLOT 2 [SOLVED]           
known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases 
– those that the current forest gets wrong. This ensures even more diversity and accurate forests. 
Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and 
even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. 
Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to 
breakthroughs. 
 
Q 1: Which of the following conditions, if true, would invalidate the passage’s main argument? 
1. If top-scorers possessed multidisciplinary knowledge that enabled them to look at a 
problem from several perspectives. 
2. If assessment tests were made more extensive and rigorous. 
3. If it were proven that teams characterised by diversity end up being conflicted about 
problems and take a long time to arrive at a solution. 
4. If a new machine-learning algorithm were developed that proved to be more effective 
than the random decision forest. 
 
Q 2: The author critiques meritocracy for all the following reasons EXCEPT that: 
1. an ideal team comprises of best individuals from diverse fields of knowledge. 
2. modern problems are multifaceted and require varied skill-sets to be solved. 
3. criteria designed to assess merit are insufficient to test expertise in any field of 
knowledge. 
4. diversity and context-specificity are important for making major advances in any field. 
 
Q 3: Which of the following conditions would weaken the efficacy of a random decision forest? 
1. If a large number of decision trees in the ensemble were trained on data derived from 
easy cases. 
2. If the types of decision trees in each ensemble of the forest were doubled. 
CAT 2018 Paper SLOT 2 [SOLVED]           
3. If a large number of decision trees in the ensemble were trained on data derived from 
easy and hard cases. 
4. If the types of ensembles of decision trees in the forest were doubled. 
 
Q 4: On the basis of the passage, which of the following teams is likely to be most effective in 
solving the problem of rising obesity levels? 
1. A team comprised of nutritionists, psychologists, urban planners and media personnel, 
who have each scored a distinction in their respective subject tests. 
2. A team comprised of nutritionists, psychologists, urban planners and media personnel, 
who have each performed well in their respective subject tests. 
3. A specialised team of nutritionists from various countries, who are also trained in the 
machine-learning algorithm of random decision forest. 
4. A specialised team of top nutritionists from various countries, who also possess some 
knowledge of psychology. 
 
Q 5: Which of the following best describes the purpose of the example of neuroscience? 
1. Unlike other fields of knowledge, neuroscience is an exceptionally complex field, making 
a meaningful assessment of neuroscientists impossible. 
2. In narrow fields of knowledge, a meaningful assessment of expertise has always been 
possible. 
3. Neuroscience is an advanced field of science because of its connections with other 
branches of science like oncology and biostatistics. 
4. In the modern age, every field of knowledge is so vast that a meaningful assessment of 
merit is impossible. 
Grove snails as a whole are distributed all over Europe, but a specific variety of the snail, with a 
distinctive white-lipped shell, is found exclusively in Ireland and in the Pyrenees mountains that 
lie on the border between France and Spain. The researchers sampled a total of 423 snail 
specimens from 36 sites distributed across Europe, with an emphasis on gathering large numbers 
CAT 2018 Paper SLOT 2 [SOLVED]           
of the white-lipped variety. When they sequenced genes from the mitochondrial DNA of each of 
these snails and used algorithms to analyze the genetic diversity between them, they found that. . 
. a distinct lineage (the snails with the white-lipped shells) was indeed endemic to the two very 
specific and distant places in question. 
Explaining this is tricky. Previously, some had speculated that the strange distributions of 
creatures such as the white-lipped grove snails could be explained by convergent evolution—in 
which two populations evolve the same trait by coincidence—but the underlying genetic 
similarities between the two groups rules that out. Alternately, some scientists had suggested that 
the white-lipped variety had simply spread over the whole continent, then been wiped out 
everywhere besides Ireland and the Pyrenees, but the researchers say their sampling and 
subsequent DNA analysis eliminate that possibility too. “If the snails naturally colonized Ireland, 
you would expect to find some of the same genetic type in other areas of Europe, especially 
Britain. We just don’t find them,” Davidson, the lead author, said in a press statement. 
Moreover, if they’d gradually spread across the continent, there would be some genetic variation 
within the white-lipped type, because evolution would introduce variety over the thousands of 
years it would have taken them to spread from the Pyrenees to Ireland. That variation doesn’t 
exist, at least in the genes sampled. This means that rather than the organism gradually 
expanding its range, large populations instead were somehow moved en mass to the other 
location within the space of a few dozen generations, ensuring a lack of genetic variety. 
“There is a very clear pattern, which is difficult to explain except by involving humans,” 
Davidson said. Humans, after all, colonized Ireland roughly 9,000 years ago, and the oldest fossil 
evidence of grove snails in Ireland dates to roughly the same era. Additionally, there is 
archaeological evidence of early sea trade between the ancient peoples of Spain and Ireland via 
the Atlantic and even evidence that humans routinely ate these types of snails before the advent 
of agriculture, as their burnt shells have been found in Stone Age trash heaps. 
The simplest explanation, then? Boats. These snails may have inadvertently traveled on the floor 
of the small, coast-hugging skiffs these early humans used for travel, or they may have been 
intentionally carried to Ireland by the seafarers as a food source. “The highways of the past were 
rivers and the ocean–as the river that flanks the Pyrenees was an ancient trade route to the 
CAT 2018 Paper SLOT 2 [SOLVED]           
Atlantic, what we’re actually seeing might be the long lasting legacy of snails that hitched a 
ride…as humans travelled from the South of France to Ireland 8,000 years ago,” Davidson said. 
 
Q 6: All of the following evidence supports the passage’s explanation of sea travel/trade 
EXCEPT: 
1. archaeological evidence of early sea trade between the ancient peoples of Spain and 
Ireland via the Atlantic Ocean. 
2. the oldest fossil evidence of white-lipped grove snails in Ireland dates back to roughly 
9,000 years ago, the time when humans colonised Ireland. 
3. absence of genetic variation within the white-lipped grove snails of Ireland and the 
Pyrenees, whose genes were sampled. 
4. the coincidental existence of similar traits in the white-lipped grove snails of Ireland and 
the Pyrenees because of convergent evolution. 
 
Q 7: In paragraph 4, the evidence that “humans routinely ate these types of snails before the 
advent of agriculture” can be used to conclude that: 
1. 9,000 years ago, during the Stone Age, humans traveled from the South of France to 
Ireland via the Atlantic Ocean. 
2. white-lipped grove snails may have inadvertently traveled from the Pyrenees to Ireland 
on the floor of the small, coast-hugging skiffs that early seafarers used for travel. 
3. the seafarers who traveled from the Pyrenees to Ireland might have carried white-lipped 
grove snails with them as edibles. 
4. rivers and oceans in the Stone Age facilitated trade in white-lipped grove snails. 
 
Q 8: Which one of the following makes the author eliminate convergent evolution as a probable 
explanation for why white-lipped grove snails are found in Ireland and the Pyrenees? 
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