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Practice Free C1000-059 IBM AI Enterprise Workflow V1 Data Science Specialist Exam Questions Answers With Explanation

We at Crack4sure are committed to giving students who are preparing for the IBM C1000-059 Exam the most current and reliable questions . To help people study, we've made some of our IBM AI Enterprise Workflow V1 Data Science Specialist exam materials available for free to everyone. You can take the Free C1000-059 Practice Test as many times as you want. The answers to the practice questions are given, and each answer is explained.

Question # 6

What is a class of machine learning problems where the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs?

A.

unsupervised learning

B.

mentoring

C.

reinforcement learning

D.

supervised learning

Question # 7

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

Question # 8

What are three elements that are typically part of a machine learning pipeline in scikit-learn or pyspark? (Choose three.)

A.

model building

B.

data preprocessing

C.

model prediction

D.

business understanding

E.

use case selection

F.

data exploration

Question # 9

What is used to scale large positive values during data cleaning?

A.

division by random numbers

B.

square

C.

logarithm

D.

subtract median

Question # 10

With the help of AI algorithms, which type of analytics can help organizations make decisions based on facts and probability-weighted projections?

A.

prescriptive analytics

B.

cognitive analytics

C.

predictive analytics

D.

descriptive analytics

Question # 11

What is the goal of the backpropagation algorithm?

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 # 12

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.)

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 # 13

Which test is applied to determine the relationship between two categorical variables?

A.

paired t-test

B.

chi squared test

C.

z test

D.

t-test

Question # 14

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

Question # 15

Which of the following entity extraction techniques would be best for the extraction of telephone numbers from a text document?

A.

complex pattern-based

B.

regex

C.

statistical

D.

dictionary

Question # 16

In a hyperparameter search, whether a single model is trained or a lot of models are trained in parallel is largely determined by?

A.

The number of hyperparameters you have to tune.

B.

The presence of local minima in your neural network.

C.

The amount of computational power you can access.

D.

Whether you use batch or mini-batch optimization.

Question # 17

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

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

Question # 18

Which is a technique that automates the handling of categorical variables?

A.

binary encoding

B.

decoding

C.

autoencoding

D.

one-hot encoding

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