E20-065 Sample Questions Answers

Processing can be broken into smaller pieces

Processing a large number of small files

Processing in real time is required

Processing a small subset of data

Selection, orientation, and mark

Size, separation, and orientation

Position, size, and orientation

Position, texture, and selection

Used in term frequency analysis

Include words such as "a", "an", and "the"

Meaningful words requiring a parser to stop and examine them

Don't occur often in text

The node is adjacent to a few nodes, each of each has high Page Ranks.

The node has the only edge connecting its community to the rest of the graph.

The node can be easily bypassed by communications taking other shorter paths.

The node acts as the hub of the graph.

Logistic function applied to a linear combination of the input and outcome variables

Linear regression applied to a combination of input variables

Linear regression applied to a combination of input and outcome variables

Logistic function applied to a linear combination of the input variables

Eigenvector centrality and betwenness

Clique size and total number of nodes in the network

Number of edges in the network and centrality measure of the cliques

Clique size and betweenness centrality

Community clustering

Modularity

Homophily

Strongly tied network

A marketing firm wants to estimate the personal income of a group of potential customers.

Using inputs such as age, education, marital status, and credit card expenditures, a data scientist is building a model that will estimate a person's

income

A logistic distribution company wants to minimize the distance traveled by its delivery trucks.

A data scientist is building a model to determine the optimal route for each of tis trucks

To improve the initial routing of a loan application, a financial institution plans to classify a loan application as Approve, Reject, or Possibly_Approve. Based on the company's historical loan application data, a data scientist is building a model to assign one of these three outcomes to each submitted application.

A manufacturer plans to determine the optimal number of workers to employ in an assembly line process. Utilizing the observed distributions of the task durations of each process step, a data scientist is building a model to mimic the interactions and dependencies between each stage in the manufacturing process.

High TF score and high DF score

High TF score and high IDF score

High TF score and low IDF score

Low TF score and low DF score

Part of regular lattice

Weak tie

Part of a clique

Strong tie

Ensure model Inputs align with reality

Use different seed values to regenerate results

For rare event models, minimize number of trials

A complex model is better than a simple model

It must be easily commanded

Any type of data can be processed

Hadoop is required

Data should be processed quickly

Structured data, real-time processing

Unstructured data; batch processing

Unstructured data; real-time processing

Structured data; batch processing

1

2

3

6

C-E

E-F

B-C

G-l

0

5

10

15