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AI Tech Trainee Module 1

Total questions: 11

Worksheet time: 6mins

Name
Class
Date
1.

Which of the following factor(s) contributed to the recent boost in AI technology ?

a)

Improved computing power

b)

Invention of new statistical methods

c)

Big data

d)

Bill Gates‘ 50 bn investment into tech start-ups

2.

Which statement is true?

a)

Machine Learning can replace Artificial Neural Networks in future

b)

AI is machine learning programmed by a computer

c)

A Machine Learning model is trained based on prior data

d)

Supervised ML is applied when clustering unstructured data

3.

Object detection in images is typically done by... (1 answer)

a)

calculating correlation of pixels

b)

Identifying pre-trained concepts using a neural network

c)

Determining distance of objects to the frame of the image

d)

Replacing colored pixels with ordinal variables

4.

Input to your model is a series of temperatures in °C in last 365 days. What level of measurement is that variable?

a)

Ratio

b)

Nominal values

c)

Interval

d)

Continuous, ordinal number

5.

Which of the following values are possibly correlation coefficients ?

a)

- 0.5

b)

0.1

c)

1.1

d)

0.2536542

6.

The correlation coefficient is 0. This means there is no relationship between the two variables.

a)

True

b)

False

7.

Which are the foremost used programming languages for doing machine learning?

a)

R

b)

JavaScript

c)

SQL

d)

Python

8.

What are python libraries?

a)

Sub-programs that allow Python to communicate with different operating systems

b)

Pieces of documentation on how to use different Python modules

c)

Sets of useful functions or chunks of code that you can reuse

d)

Modules that allow you to integrate IDEs in your work

9.

Which of the following Python libraries are commonly used in data science?

a)

Pandas

b)

NumPy

c)

Matplotlib

d)

Scikit-learn

10.

Which steps are typically a part of an EDA?

Check all that apply.

a)

Missing data analysis

b)

Hypothesis testing

c)

Calculating descriptive statistics

d)

Dimensionality reduction

11.

You have a variable for which 40% of the values are missing. What is the worst option for dealing with that variable ?

a)

Drop all rows from the data frame that is missing data

b)

Impute the missing values using mean

c)

Replace missing values with median of available values

d)

Apply a linear regression