AI and Big Data Training Course Assessment

Thank you for attending the AI and Big Data Training Course. We hope you found the course valuable and that it sparked some interest in the exciting world of AI and machine learning.

You will be presented with a short assessment to test your knowledge of the course material. The assessment consists of 20 questions. Please provide an answer for each question. Good luck!

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1.

Artificial Intelligence is the study of:

Intelligence

Machine learning

Intelligent agents

Robots

2.

Which one of the following is NOT part of the definition of an intelligent agent:

Observing the environment through sensors and acting on the environment

Working towards achieving a goal

Learning and representing knowledge to achieve its goals.

Communicating with other agents.

3.

Big data refers to:

Data processed in the cloud

Data that is too large or too complex for traditional data processing application software

Data used in machine learning

Data processed by big mainframe computers

4.

Data with a separately defined data model or structure is called:

Structured data

Semi-structured data

Unstructured data

Relational data

5.

Data-driven organisations view data as:

A by-product of operations

A resource

A critical business asset

A liability

6.

A customer can rate service received as one of the following: Very bad, bad, adequate, good, excellent. This is an example of:

Nominal data

Ordinal data

Discrete data

Continuous data

7.

You have a set of images that will serve as input to train a machine learning model. You would like to apply transformations on the images, such as rotations and scaling to create a larger dataset. This is called:

Data encoding

Data imputation

Data standardization

Data augmentation

8.

You have a set of photographs of different objects. For every photograph, you have a label describing the object in the photograph. Which of the following machine learning algorithms should you use:

Reinforcement learning

Unsupervised learning

Semi-supervised learning

Supervised learning

9.

Which of the following is an example of an unsupervised learning problem:

You collected a large number of emails and for each have labelled them as spam or not spam. You want to build a spam classifier based on this dataset.

You acquired a dataset with different attributes of houses (sizes, number of bedrooms, location, etc.) as well as the price the house sold for. You would like to train a machine learning algorithm to predict the selling price of a new house on the market.

You captured data about different customers that visited your website and would like to segment them into different customer segments.

You would like to build a system that can play an online strategy game by optimizing the strategy through playing millions of games.

10.

You are trying to predict a continuous variable on a dataset with features and corresponding target variables. This is an example of a:

Regression problem

Classification problem

Clustering problem

Dimensionality reduction problem

11.

You have trained and optimized a machine learning model. You would like to do a final evaluation of the performance of the model. You apply the evaluation to the:

Training Set

Validation Set

Test Set

Feature Set

12.

You have trained a machine learning model. It performs well on the training data set, but not on the validation data set. This is likely due to:

Overfitting

Underfitting

Generalisation

The curse of dimensionality

13.

You trained a binary classifier to predict whether an email is spam or not. The classifier outputs True for spam and False for not spam. Your classifier made a mistake by classifying a normal email as spam. This is called a:

True positive

False positive

True negative

False negative

14.

Learning using a multilayered neural networks on vast amounts of data is most appropriately called:

Artificial intelligence

Machine learning

Deep learning

Data science

15.

Which of the following is a popular Python library used for deep learning:

Numpy

Scikit-learn

Matplotlib

TensorFlow

16.

The standardization and streamlining of machine learning life cycle management is called:

MLOps

AIOps

DevOps

DataOps

17.

Which of the following people would typically have the responsibility of applying exploratory data analysis and building machine learning models in an organisation:

Data engineers

Data scientists

MLOps engineers

Data analysts

18.

In modern data infrastructures, unstructured data that is streamed into the organisation is usually stored in a:

Database

Data warehouse

Data mart

Data lake

19.

You are creating curated datasets for use in machine learning models in your organisation. This is most appropriately stored in a:

Data lake

Feature store

Model store

Evaluation store

20.

You have trained a machine learning model to predict credit scores for loan applicants. The model seems to exhibit some unfair behaviour based on the gender of the applicant. This is an example of:

Bias in AI systems

Lack of model explainability

Fake data

Weaponization of AI