IBPS Clerk Pre 2018 Speed Test: 06.12.2018

Direction: In each of the following questions, a relationship between different elements is shown in the statements. The statements are followed by two conclusions I and II. Assuming the given statements to be true, find out which of the two conclusions I and II given below is/are definitely true.  

'M$N' means 'M is not smaller than N'.
'M@N' means 'M is not greater than N'.
'M©N' means 'M is neither smaller than nor equal to N'.
'M%N' means 'M is neither greater than nor equal to N'.
'M#N' means 'M is neither smaller than nor greater than N'.

Direction: In each of the following questions, a relationship between different elements is shown in the statements. The statements are followed by two conclusions I and II. Assuming the given statements to be true, find out which of the two conclusions I and II given below is/are definitely true.  

'M$N' means 'M is not smaller than N'.
'M@N' means 'M is not greater than N'.
'M©N' means 'M is neither smaller than nor equal to N'.
'M%N' means 'M is neither greater than nor equal to N'.
'M#N' means 'M is neither smaller than nor greater than N'.

Direction: In each of the following questions, a relationship between different elements is shown in the statements. The statements are followed by two conclusions I and II. Assuming the given statements to be true, find out which of the two conclusions I and II given below is/are definitely true.  

'M$N' means 'M is not smaller than N'.
'M@N' means 'M is not greater than N'.
'M©N' means 'M is neither smaller than nor equal to N'.
'M%N' means 'M is neither greater than nor equal to N'.
'M#N' means 'M is neither smaller than nor greater than N'.

Direction: In each of the following questions, a relationship between different elements is shown in the statements. The statements are followed by two conclusions I and II. Assuming the given statements to be true, find out which of the two conclusions I and II given below is/are definitely true.  

'M$N' means 'M is not smaller than N'.
'M@N' means 'M is not greater than N'.
'M©N' means 'M is neither smaller than nor equal to N'.
'M%N' means 'M is neither greater than nor equal to N'.
'M#N' means 'M is neither smaller than nor greater than N'.

Direction: In each of the following questions, a relationship between different elements is shown in the statements. The statements are followed by two conclusions I and II. Assuming the given statements to be true, find out which of the two conclusions I and II given below is/are definitely true.  

'M$N' means 'M is not smaller than N'.
'M@N' means 'M is not greater than N'.
'M©N' means 'M is neither smaller than nor equal to N'.
'M%N' means 'M is neither greater than nor equal to N'.
'M#N' means 'M is neither smaller than nor greater than N'.

Directions: Following bar-graph shows the percentage of passed girls with respect to total passed students of two schools A and B.

Directions: Following bar-graph shows the percentage of passed girls with respect to total passed students of two schools A and B.

Directions: Following bar-graph shows the percentage of passed girls with respect to total passed students of two schools A and B.

Directions: Following bar-graph shows the percentage of passed girls with respect to total passed students of two schools A and B.

Directions: Following bar-graph shows the percentage of passed girls with respect to total passed students of two schools A and B.

Direction: In the following passage, there are blanks each of which has been numbered. These numbers correspond to the question numbers; against each question, five phrases have been suggested, one of which fills the blanks appropriately. 
Google claims that a new deep learning model designed by it and its UC San Francisco, Stanford Medicine, and The University of Chicago Medicine colleagues has predicted the 'inpatient mortality' (11) of 95 percent. Machine learning, which was previously applied to actions like traffic predictions, translations and like has, in a recent attempt, been used for healthcare by a Google team. The (12) the computer system came out to be astonishingly accurate, as they predicted if the patient will stay long in the hospital with an 86 percent accuracy and further unexpected readmissions with a 77 percent accuracy, marking 'statistical significance' in the area. In addition to making the predictions, the deep learning models were also used to recognise the patient's condition. Google gives an instance for this: "if a doctor prescribed ceftriaxone and doxycycline for a patient with an elevated temperature, fever and cough, the model could identify these as signals that the patient was being treated for pneumonia." In a blog, Google calls its program a "good listener" (13) to gather the patient's data including their ongoing treatments and notes. The new program aims to eliminate the discrepancies caused by different Electronic Health Records (EHR) found in individually customised EHR systems of hospitals. In essence, the patient data differs from one hospital to another. To solve this, the deep learning mechanism reads all the data points from the patient's EHRs and then decides which data can be used to (14). The program also identifies the specific data set which it used the make the prediction. Google protected the data used for this observation (15) including "logical separation, strict access controls, and encryption of data at rest and in transit".As of now, Google says that the entire idea is still in its early age and that the test simply suggests how machine learning can be used to improve healthcare.

Direction: In the following passage, there are blanks each of which has been numbered. These numbers correspond to the question numbers; against each question, five phrases have been suggested, one of which fills the blanks appropriately. 
Google claims that a new deep learning model designed by it and its UC San Francisco, Stanford Medicine, and The University of Chicago Medicine colleagues has predicted the 'inpatient mortality' (11) of 95 percent. Machine learning, which was previously applied to actions like traffic predictions, translations and like has, in a recent attempt, been used for healthcare by a Google team. The (12) the computer system came out to be astonishingly accurate, as they predicted if the patient will stay long in the hospital with an 86 percent accuracy and further unexpected readmissions with a 77 percent accuracy, marking 'statistical significance' in the area. In addition to making the predictions, the deep learning models were also used to recognise the patient's condition. Google gives an instance for this: "if a doctor prescribed ceftriaxone and doxycycline for a patient with an elevated temperature, fever and cough, the model could identify these as signals that the patient was being treated for pneumonia." In a blog, Google calls its program a "good listener" (13) to gather the patient's data including their ongoing treatments and notes. The new program aims to eliminate the discrepancies caused by different Electronic Health Records (EHR) found in individually customised EHR systems of hospitals. In essence, the patient data differs from one hospital to another. To solve this, the deep learning mechanism reads all the data points from the patient's EHRs and then decides which data can be used to (14). The program also identifies the specific data set which it used the make the prediction. Google protected the data used for this observation (15) including "logical separation, strict access controls, and encryption of data at rest and in transit".As of now, Google says that the entire idea is still in its early age and that the test simply suggests how machine learning can be used to improve healthcare.

Direction: In the following passage, there are blanks each of which has been numbered. These numbers correspond to the question numbers; against each question, five phrases have been suggested, one of which fills the blanks appropriately. 
Google claims that a new deep learning model designed by it and its UC San Francisco, Stanford Medicine, and The University of Chicago Medicine colleagues has predicted the 'inpatient mortality' (11) of 95 percent. Machine learning, which was previously applied to actions like traffic predictions, translations and like has, in a recent attempt, been used for healthcare by a Google team. The (12) the computer system came out to be astonishingly accurate, as they predicted if the patient will stay long in the hospital with an 86 percent accuracy and further unexpected readmissions with a 77 percent accuracy, marking 'statistical significance' in the area. In addition to making the predictions, the deep learning models were also used to recognise the patient's condition. Google gives an instance for this: "if a doctor prescribed ceftriaxone and doxycycline for a patient with an elevated temperature, fever and cough, the model could identify these as signals that the patient was being treated for pneumonia." In a blog, Google calls its program a "good listener" (13) to gather the patient's data including their ongoing treatments and notes. The new program aims to eliminate the discrepancies caused by different Electronic Health Records (EHR) found in individually customised EHR systems of hospitals. In essence, the patient data differs from one hospital to another. To solve this, the deep learning mechanism reads all the data points from the patient's EHRs and then decides which data can be used to (14). The program also identifies the specific data set which it used the make the prediction. Google protected the data used for this observation (15) including "logical separation, strict access controls, and encryption of data at rest and in transit".As of now, Google says that the entire idea is still in its early age and that the test simply suggests how machine learning can be used to improve healthcare.

Direction: In the following passage, there are blanks each of which has been numbered. These numbers correspond to the question numbers; against each question, five phrases have been suggested, one of which fills the blanks appropriately. 
Google claims that a new deep learning model designed by it and its UC San Francisco, Stanford Medicine, and The University of Chicago Medicine colleagues has predicted the 'inpatient mortality' (11) of 95 percent. Machine learning, which was previously applied to actions like traffic predictions, translations and like has, in a recent attempt, been used for healthcare by a Google team. The (12) the computer system came out to be astonishingly accurate, as they predicted if the patient will stay long in the hospital with an 86 percent accuracy and further unexpected readmissions with a 77 percent accuracy, marking 'statistical significance' in the area. In addition to making the predictions, the deep learning models were also used to recognise the patient's condition. Google gives an instance for this: "if a doctor prescribed ceftriaxone and doxycycline for a patient with an elevated temperature, fever and cough, the model could identify these as signals that the patient was being treated for pneumonia." In a blog, Google calls its program a "good listener" (13) to gather the patient's data including their ongoing treatments and notes. The new program aims to eliminate the discrepancies caused by different Electronic Health Records (EHR) found in individually customised EHR systems of hospitals. In essence, the patient data differs from one hospital to another. To solve this, the deep learning mechanism reads all the data points from the patient's EHRs and then decides which data can be used to (14). The program also identifies the specific data set which it used the make the prediction. Google protected the data used for this observation (15) including "logical separation, strict access controls, and encryption of data at rest and in transit".As of now, Google says that the entire idea is still in its early age and that the test simply suggests how machine learning can be used to improve healthcare.

Direction: In the following passage, there are blanks each of which has been numbered. These numbers correspond to the question numbers; against each question, five phrases have been suggested, one of which fills the blanks appropriately. 
Google claims that a new deep learning model designed by it and its UC San Francisco, Stanford Medicine, and The University of Chicago Medicine colleagues has predicted the 'inpatient mortality' (11) of 95 percent. Machine learning, which was previously applied to actions like traffic predictions, translations and like has, in a recent attempt, been used for healthcare by a Google team. The (12) the computer system came out to be astonishingly accurate, as they predicted if the patient will stay long in the hospital with an 86 percent accuracy and further unexpected readmissions with a 77 percent accuracy, marking 'statistical significance' in the area. In addition to making the predictions, the deep learning models were also used to recognise the patient's condition. Google gives an instance for this: "if a doctor prescribed ceftriaxone and doxycycline for a patient with an elevated temperature, fever and cough, the model could identify these as signals that the patient was being treated for pneumonia." In a blog, Google calls its program a "good listener" (13) to gather the patient's data including their ongoing treatments and notes. The new program aims to eliminate the discrepancies caused by different Electronic Health Records (EHR) found in individually customised EHR systems of hospitals. In essence, the patient data differs from one hospital to another. To solve this, the deep learning mechanism reads all the data points from the patient's EHRs and then decides which data can be used to (14). The program also identifies the specific data set which it used the make the prediction. Google protected the data used for this observation (15) including "logical separation, strict access controls, and encryption of data at rest and in transit".As of now, Google says that the entire idea is still in its early age and that the test simply suggests how machine learning can be used to improve healthcare.