Wayground logo

Free Printable Worksheets

Font size

S
M
L
XL
Worksheets

ACC5044-Week 6

Total questions: 24

Worksheet time: 24mins

Name
Class
Date
1.

What rights does the General Data Protection Regulation (GDPR) provide to EU citizens?

a)

The right of rectification, the right of portability, the right to be forgotten, and the right to restriction of profiling.

b)

The right to data transparency, the right to data security, the right to data minimization, and the right to object to data processing.

c)

The right to data access, the right to data correction, the right to data deletion, and the right to data portability.

d)

The right to data accuracy, the right to data integrity, the right to data retention, and the right to data restriction.

2.

What was the reason for the fine imposed on a leading insurer in the United Kingdom by the Financial Conduct Authority (FCA) in October 2018?

a)

Misuse of customer data

b)

Poor oversight of a third-party supplier

c)

Unauthorized data sharing

d)

Lack of data encryption

3.

What are the three main categories of data according to the image?

a)

Personal data, sensitive data, and public data

b)

Structured data, semi-structured data, and unstructured data

c)

Known knowns, known unknowns, and unknown unknowns

d)

Quantitative data, qualitative data, and mixed-method data

4.

Which category of data includes information that we are aware of and can validate, such as a person's name and address?

a)

Known knowns

b)

Known unknowns

c)

Unknown unknowns

d)

Sensitive data

5.

What are "known unknowns" in the context of data?

a)

Data that are created without our knowledge and cannot be validated

b)

Data that arise from people's activities and are recorded by various digital platforms

c)

Data that are completely anonymous and cannot be traced back to an individual

d)

Data that are used by companies to create digital profiles without consent

6.

What are "unknown unknowns" according to the text?

a)

Data that are created with our knowledge and can be easily validated

b)

Data that are generated by companies using eye tracking and gesture tracking

c)

Data that are created without our knowledge and we have few opportunities to validate

d)

Data that are publicly available and can be used by anyone

7.

How much more quickly are asset managers who embrace big data and analytics growing their revenue compared to the rest of financial services?

a)

0.5 times more quickly

b)

1.5 times more quickly

c)

2 times more quickly

d)

2.5 times more quickly

8.

What applications in asset management are mentioned in the text where AI is applied?

a)

Risk management and beta generation

b)

Risk management and alpha generation

c)

Alpha management and beta generation

d)

Data management and risk generation

9.

What is the limitation of conventional risk models as mentioned in the text?

a)

They assume markets behave in non-linear relationships

b)

They are unable to model complex risks and carry out stress tests

c)

They assume markets behave in linear relationships and can model complex risks

d)

They use AI to carry out stress tests beyond business-as-usual scenarios

10.

What is the primary goal of Customer Relationship Management (CRM)?

a)

To provide personalized experiences to customers

b)

To analyze vast amounts of data

c)

To improve business relationships with customers, assist in customer retention, and drive sales growth

d)

To predict customer behavior based on past interactions

11.

How can AI significantly augment CRM?

a)

By providing real-time approvals and reducing false positive results

b)

By executing trades in financial markets

c)

By analyzing past behaviors, preferences, and interactions to tailor communications and offers

d)

By relying on heavy historical credit data

12.

What has been a traditional issue with fraud detection methods used by banks?

a)

They are too efficient and reduce human jobs

b)

They capture a large percentage of fraud cases but have a high percentage of false positives

c)

They capture only a small percentage of fraud cases and produce a high percentage of false positives

d)

They are fully automated and do not require human intervention

13.

What is algorithmic trading also known as?

a)

CRM trading

b)

Black-box trading

c)

Fraud detection trading

d)

Credit scoring trading

14.

What is the advantage of machine-learning algorithms in fraud detection?

a)

They can execute trades with speed and efficiency

b)

They can analyze millions of data points to detect fraudulent transactions

c)

They rely on heavy historical credit data

d)

They provide personalized experiences to customers

15.

What is the main goal of text and data mining algorithms in finance?

a)

To conduct online interviews

b)

To categorize potential employees

c)

To discover relationships and find patterns in data

d)

To extract user emotions from text

16.

What is sentiment analysis also known as?

a)

Opinion mining

b)

Data extraction

c)

Trend analysis

d)

Character bucketing

17.

What is the main goal of information extraction systems?

a)

To screen job applicants

b)

To identify objects and structure relevant data into meaningful information

c)

To optimize training effects for AI systems

d)

To assess candidates' performance based on different traits

18.

Which of the following is NOT a prime source for text sentiment analysis?

a)

Blogs

b)

Social media sites

c)

E-mails

d)

Financial statements

19.

What is the main concern of natural language processing?

a)

Data classification

b)

Human-computer interaction

c)

Credit rating

d)

Loan default prediction

20.

What has caused a substantial change in the financial industry according to the text?

a)

Credit evaluation

b)

Machine learning and information technology

c)

Neural networks

d)

Money laundering

21.

What new and emerging technique is crucial to increase the efficiency of the credit rating process, considering variety of data types generated?

a)

Predictive analytics

b)

Data mining

c)

Neural networks

d)

Logistic regression models

22.

What can stock prediction technology be used for in financial markets?

a)

To make qualitative decisions

b)

To predict weather patterns

c)

To calculate interest rates

d)

To assess real estate values

23.

Why is making accurate assessments in the stock market complicated?

a)

Because of the fluctuations in the stock market

b)

Because of the constant stability in the stock market

c)

Because of the predictable nature of stocks

d)

Because of the lack of technology

24.

What has given researchers a good reason to place significant efforts into bettering the ability to predict stocks?

a)

The simplicity of the stock market

b)

The desire to reduce qualitative decisions

c)

The complications due to fluctuations in the stock market

d)

The lack of interest in financial markets