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The most likely concern with a one-feature, machine-learning model is high error due to:

A.

bias

B.

dimensionality

C.

variance

D.

probability

A statistician notices gaps in data associated with age-related illnesses and wants to further aggregate these observations. Which of the following is the best technique to achieve this goal?

A.

Label encoding

B.

Linearization

C.

Binning

D.

Imputing

Which of the following distributions would be best to use for hypothesis testing on a data set with 20 observations?

A.

Power law

B.

Normal

C.

Uniform

D.

Student's t-

A data scientist wants to predict a person's travel destination. The options are:

    Branson, Missouri, United States

    Mount Kilimanjaro, Tanzania

    Disneyland Paris, Paris, France

    Sydney Opera House, Sydney, Australia

Which of the following models would best fit this use case?

A.

Linear discriminant analysis

B.

k-means modeling

C.

Latent semantic analysis

D.

Principal component analysis

Which of the following methods should a data scientist use just before switching to a potential replacement model?

A.

A/B testing

B.

Performance monitoring

C.

CI/CD

D.

Containerization

The following graphic shows the results of an unsupervised, machine-learning clustering model:

k is the number of clusters, and n is the processing time required to run the model. Which of the following is the best value of k to optimize both accuracy and processing requirements?

A.

2

B.

10

C.

15

D.

20

Which of the following best describes the minimization of the residual term in a LASSO linear regression?

A.

|e|

B.

e

C.

0

D.

An analyst is examining data from an array of temperature sensors and sees that one sensor consistently returns values that are much higher than the values from the other sensors. Which of the following terms best describes this type of error?

A.

Synthetic

B.

Systematic

C.

Heteroskedastic

D.

Idiosyncratic

Which of the following modeling tools is appropriate for solving a scheduling problem?

A.

One-armed bandit

B.

Constrained optimization

C.

Decision tree

D.

Gradient descent

Which of the following describes the appropriate use case for PCA?

A.

Dimensionality reduction

B.

Classification

C.

Regression

D.

Recommendation