EMC E20-065 Advanced Analytics Specialist Exam for Data Scientists Exam Practice Test

Use WordNet

Remove common words in a natural language

Reduce the high dimensionality in text

Use a set of heuristics

Encryption

Replication

Namenode federation

Snapshots

One with a few. non-random input variables

One that has a closed-form solution

One with numerous, non-random Input-variables

One that compares "what-if scenarios

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

Visualization Planning

Data Preparation

Visualization Building

Discovery

Use self-explanatory colors so a legend is unnecessary

Maximize use of color to make a more lasting impression

Use high contrast colors such as red and blue

Minimize use of color except for emphasis

Low clustering coefficients, high network diameters

High clustering coefficients, small network diameters

Low clustering coefficients; small network diameters

High clustering coefficients; high network diameters

Provides and explanatory model

Distinguishes categorical from continuous variables

Support for unstructured data

Resiliency to complex, non-linear variable interactions

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.