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IBM C1000-059 - IBM AI Enterprise Workflow V1 Data Science Specialist

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Total 62 questions

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

A.

for skewed data

B.

for real numbers

C.

for normally distributed data

D.

for large data sets

With only limited labeled data available how might a neural network use case be realized?

A.

by assigning random labels

B.

by increasing the depth of the neural network

C.

by creating random data

D.

by using a customized pre-trained model

Which statement is true for naive Bayes?

A.

Naive Bayes can be used for regression.

B.

Let p(C1 | x) and p(C2 | x) be the conditional probabilities that x belongs to class C1 and C2 respectively, in a binary model, log p (C1 | x) – log p(C2 | x) > 0 results in predicting that x belongs to C2.

C.

Naive Bayes is a conditional probability model.

D.

Naive Bayes doesn't require any assumptions about the distribution of values associated with each class.

If the distribution of the height of American men is approximately normal, with a mean of 69 inches and a standard deviation of 2.5 inches, then roughly 68 percent of American men have heights between and .

A.

64 inches and 74 inches

B.

66.5 inches and 69 inches

C.

71.5 inches and 76.5 inches

D.

66.5 inches and 71.5 inches

Which is a preferred approach for simplifying the data transformation steps in machine learning model management and maintenance?

A.

Implement data transformation, feature extraction, feature engineering, and imputation algorithms in one single pipeline.

B.

Do not apply any data transformation or feature extraction or feature engineering steps.

C.

Leverage only deep learning algorithms.

D.

Apply a limited number of data transformation steps from a pre-defined catalog of possible operations independent of the machine learning use case.

What are two methods used to detect outliers in structured data? (Choose two.)

A.

multi-label classification

B.

isolation forest

C.

gradient descent

D.

one class Support Vector Machine (SVM)

E.

Word2Vec

What is a class of machine learning problems where the algorithm is given feedback in the form of positive or negative reward in a dynamic environment?

A.

reinforcement learning

B.

feedback-based optimization

C.

dynamic programming

D.

reward learning

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?

A.

increase the depth of the neural network

B.

insert a dropout layer in the neural network architecture

C.

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

D.

restart the training with a higher learning rate