IBM C1000-059 Exam Practice Test Instant Access

Question 1

What is an example of a supervised machine learning algorithm that can be applied to a continuous numeric response variable?

Alinear regression

Bk-means

Clocal outlier factor (LOF)

Dnaive Bayes

Answer : A

Question 2

When should median value be used instead of mean value for imputing missing data?

Afor skewed data

Bfor real numbers

Cfor normally distributed data

Dfor large data sets

Answer : D

Question 3

Which fine-tuning technique does not optimize the hyperparameters of a machine learning model?

Agrid search

Bpopulation based training

Crandom search

Dhyperband

Answer : D

Question 4

What is the meaning of "deep" in deep learning?

ATo go deep into the loss function landscape.

BThe higher the number of machine learning algorithms that can be applied, the deeper is the learning.

CA kind of deeper understanding achieved by any approach taken.

DIt indicates the many layers contributing to a model of the data.

Answer : D

Question 5

A new test to diagnose a disease is evaluated on 1152 people, and 106 people have the disease, and 1046 people do not have the disease. The test results are summarized below:

In this sample, how many cases are false positives and false negatives?

A33 false positives and 81 false negatives

B81 false positives and 73 false negatives

C73 false positives and 81 false negatives

D81 false positives and 33 false negatives

Answer : A

Question 6

What are two key characteristics of cloud architecture that could benefit AI applications? (Choose two.)

Aconstant attention needed for maintenance and support of the cloud platform

Bcapable of managing and handling dynamic workloads with automatic recovery from failures

Chybrid clouds enable the deployment of distributed large neural networks

Dsupport for common business oriented language (COBOL) applications

Ethe hardware requirement can be scaled up as per the demand

Answer : B, E

Question 7

A neural network is trained for a classification task. During training, you monitor the loss function for the train dataset and the validation dataset, along with the accuracy for the validation dataset. The goal is to get an accuracy of 95%.

From the graph, what modification would be appropriate to improve the performance of the model?

Aincrease the depth of the neural network

Binsert a dropout layer in the neural network architecture

Cincrease the proportion of the train dataset by moving examples from the validation dataset to the train dataset

Drestart the training with a higher learning rate

Answer : D

IBM AI Enterprise Workflow V1 Data Science Specialist C1000-059 Exam Practice Test