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

Demo: 9 questions
Total 62 questions

IBM AI Enterprise Workflow V1 Data Science Specialist Questions and Answers

Question 1

Which distance is applied for multivariate outlier detection?

Options:

A.

Minkowski distance

B.

Manhattan distance

C.

Mahalanobis distance

D.

Euclidean distance

Question 2

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?

Options:

A.

reinforcement learning

B.

feedback-based optimization

C.

dynamic programming

D.

reward learning

Question 3

Which measure can be used to show business stakeholders the likelihood that a machine learning model will generate a true prediction?

Options:

A.

accuracy

B.

variance

C.

mean

D.

skewness

Question 4

What is the goal of the backpropagation algorithm?

Options:

A.

to randomize the trajectory of the neural network parameters during training

B.

to smooth the gradient of the loss function in order to avoid getting trapped in small local minimas

C.

to scale the gradient descent step in proportion to the gradient magnitude

D.

to compute the gradient of the loss function with respect to the neural network parameters

Question 5

Determine the number of bigrams and trigrams in the sentence. "Data is the new oil".

Options:

A.

3 bigrams, 3 trigrams

B.

4 bigrams, 4 trigrams

C.

3 bigrams, 4 trigrams

D.

4 bigrams, 3 trigrams

Question 6

Given the following sentence:

The dog jumps over a fence.

What would a vectorized version after common English stopword removal look like?

Options:

A.

['dog', 'fence', 'run']

B.

['fence', 'jumps']

C.

['dog', 'fence', 'jumps']

D.

['a', 'dog', 'fence', 'jumps', 'over', 'the']

Question 7

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

Options:

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

Question 8

A data scientist is exploring transaction data from a chain of stores with several locations. The data includes store number, date of sale, and purchase amount.

If the data scientist wants to compare total monthly sales between stores, which two options would be good ways to aggregate the data? (Choose two.)

Options:

A.

Find the sum of the transaction prices

B.

Select the largest transaction amount by month and store

C.

Write a GROUP BY query

D.

Plot a time series plot of transaction amounts

E.

Generate a pivot table

Question 9

Which one is the most appropriate use case for artificial intelligence (AI)?

Options:

A.

detecting objects in video streams

B.

compressing large video files

C.

aggregating sales revenue per state

D.

creating a pivot table with monthly costs

Demo: 9 questions
Total 62 questions