MCQ Quiz

Question 1 of 28

Which statement about fintech is least likely accurate in this module?

Early forms included data processing and automation of routine tasks.
Later applications advanced into machine-learning logic that can learn how to complete tasks over time.
For this module, fintech refers only to firms that build the technology and not to the innovation in financial services itself.

Question 2 of 28

Assertion (A): Fintech is directly relevant to quantitative investment analysis partly because massive alternative datasets can be integrated into the investment decision-making process.
Reason (R): Non-traditional sources such as social media and sensor networks can help generate alpha and reduce losses when analyzed alongside other data.

Both A and R are true and R is the correct explanation of A
Both A and R are true but R is not the correct explanation of A
A is true but R is false
A is false but R is true

Question 3 of 28

Consider the following:
I. Volume refers to datasets containing many millions or even billions of data points.
II. Variety refers to the use of structured, semistructured, and unstructured formats.
III. Velocity refers to the credibility and reliability of data sources when predictions are made.
How many of the above statements are most accurate?

Only one
Only two
All the three

Question 4 of 28

Which of the following is least likely a database or language pairing described in the module?

NoSQL for unstructured data
SQLite as an embedded database common in mobile apps
SQL as a database for unstructured data that cannot be summarized in traditional tables

Question 5 of 28

NLP is most likely a useful application in investment management because it can:

convert only structured numerical tables into forecasts
analyze large volumes of text and audio to identify trends and shifts in sentiment
replace the need to source, cleanse, and organize data before analysis

Question 6 of 28

Which of the following is least likely to describe how fintech changed financial services according to the module?

It produced new systems for investment advice and financial planning.
It remained confined to rule-based execution and did not enter decision-making applications.
It challenged traditional business models of incumbent providers.

Question 7 of 28

Consider the following:
I. In supervised learning, inputs and outputs are labeled for the algorithm.
II. In unsupervised learning, the algorithm is given data and seeks to describe the data and their structure.
III. Deep learning can use supervised or unsupervised machine learning approaches.
How many of the above statements are most accurate?

Only one
Only two
All the three

Question 8 of 28

Assertion (A): When Big Data is used for inference or prediction, veracity becomes an important consideration.
Reason (R): Veracity refers to the speed and frequency with which data are recorded and transmitted.

Both A and R are true and R is the correct explanation of A
Both A and R are true but R is not the correct explanation of A
A is true but R is false
A is false but R is true

Question 9 of 28

Which dataset is least likely to be classified as unstructured in the module?

Social media posts
SQL tables
Video messages

Question 10 of 28

An analyst uses AI to sort through company filings, annual reports, and earnings calls to determine which information is most important. This use of fintech is most likely justified in the module because AI may:

identify complex, non-linear relationships in extremely large datasets
eliminate the need for human judgment about data quality and technique choice
work best only when the underlying information arrives at quarterly intervals

Question 11 of 28

Massive alternative datasets from sources such as social media and sensor networks are most likely relevant to a portfolio manager because they can:

replace all traditional corporate and market data
help generate alpha and reduce losses when integrated into investment decision making
be used only after investment decisions are completed

Question 12 of 28

Assertion (A): NLP can detect shifts in analyst sentiment before a recommendation change.
Reason (R): NLP includes tasks such as translation, speech recognition, text mining, sentiment analysis, and topic analysis.

Both A and R are true and R is the correct explanation of A
Both A and R are true but R is not the correct explanation of A
A is true but R is false
A is false but R is true

Question 13 of 28

Assertion (A): In this module, fintech excludes innovation in the design and delivery of financial services and refers only to the business sector of firms that build financial technology.
Reason (R): In common usage, fintech can also refer to companies involved in developing new technologies and to the business sector that includes such companies.

Both A and R are true and R is the correct explanation of A
Both A and R are true but R is not the correct explanation of A
A is true but R is false
A is false but R is true

Question 14 of 28

Consider the following:
I. Early forms of fintech included data processing and the automation of routine tasks.
II. Systems executing decisions according to specified rules and instructions followed those early forms.
III. In this module, fintech refers only to companies that develop new technologies and not to the innovation itself.
How many of the above statements are most accurate?

Only one
Only two
All the three

Question 15 of 28

Consider the following:
I. Text analytics includes automated information retrieval from different, unrelated sources to aid decision making.
II. NLP can detect shifts in analyst sentiment before a recommendation change by evaluating nuanced commentary.
III. NLP can analyze policy maker communications to identify trending or waning topics of interest.
How many of the above statements are most accurate?

Only one
Only two
All the three

Question 16 of 28

Assertion (A): Deep learning can use supervised or unsupervised machine learning approaches.
Reason (R): Neural networks have existed since 1958 and have been used in applications such as forecasting and pattern recognition.

Both A and R are true and R is the correct explanation of A
Both A and R are true but R is not the correct explanation of A
A is true but R is false
A is false but R is true

Question 17 of 28

Assertion (A): Low-latency systems are essential for automated trading applications that make decisions based on real-time prices and market events.
Reason (R): Low-latency systems operate on networks that communicate high volumes of data with minimal delay.

Both A and R are true and R is the correct explanation of A
Both A and R are true but R is not the correct explanation of A
A is true but R is false
A is false but R is true

Question 18 of 28

Assertion (A): NLP can be used in compliance functions to review employee voice and electronic communications for adherence to company or regulatory policy.
Reason (R): NLP lies outside text analytics and focuses only on structured numerical data.

Both A and R are true and R is the correct explanation of A
Both A and R are true but R is not the correct explanation of A
A is true but R is false
A is false but R is true

Question 19 of 28

For automated trading applications that act on real-time prices and market events, the most likely data-capture requirement is:

high-latency systems because they reduce data volume
low-latency systems because they communicate high volumes of data with minimal delay
batch systems because real-time access is unnecessary

Question 20 of 28

Which statement about visualization techniques is most likely accurate?

A tag cloud emphasizes words by frequency, whereas a mind map emphasizes relationships among concepts.
A mind map emphasizes word frequency, whereas a tag cloud maps conceptual relationships.
Both tag clouds and mind maps are designed only for structured numerical tables.

Question 21 of 28

Which of the following is most likely an area of fintech development that the module identifies as directly relevant to quantitative investment analysis?

Integrating massive traditional and alternative datasets into investment decision making
Replacing public securities markets with peer-to-peer consumer lending platforms
Expanding retail payment apps as a stand-alone end in itself

Question 22 of 28

Which of the following is most likely an unsupervised learning application described in the module?

Forecasting whether local stock market performance will be up, down, or flat the next business day
Modeling labeled inputs and outputs to forecast future stock returns
Grouping companies into peer groups based on characteristics rather than standard sector or country groupings

Question 23 of 28

When Big Data is used for inference or prediction, investment professionals are most likely concerned with veracity because it addresses:

the credibility and reliability of data sources
the speed and frequency of data recording and transmission
whether the data can be arranged in rows and columns

Question 24 of 28

Which of the following is least likely part of data curation?

Detecting bad or inaccurate data
Making adjustments for missing data when appropriate
Deciding how data will be formatted and displayed graphically

Question 25 of 28

Consider the following:
I. Traditional data include trade prices and volumes generated in financial markets.
II. Alternative data arise only from governments and companies.
III. The Internet of Things refers to corporate exhaust produced by supply chains.
How many of the above statements are most accurate?

Only one
Only two
All the three

Question 26 of 28

An ML model fits the training data extremely precisely, treats noise as true parameters, and predicts poorly on a new dataset. The model is most likely:

underfitted
overfitted
unsupervised

Question 27 of 28

Consider the following:
I. Low-latency systems are essential for automated trading applications that act on real-time prices and market events.
II. Search refers to how data move from the source or storage location to the analytical tool.
III. Data visualization for non-traditional unstructured data can include heat maps, tree diagrams, and network graphs.
How many of the above statements are most accurate?

Only one
Only two
All the three

Question 28 of 28

Consider the following:
I. Growing amounts of traditional and alternative data can be integrated into a portfolio manager's investment decision-making process.
II. AI-based analytical tools may be better suited than traditional methods to identify complex, non-linear relationships in extremely large datasets.
III. The module identifies analysis of large datasets and advanced analytical tools as areas of fintech development directly relevant to quantitative analysis.
How many of the above statements are most accurate?

Only one
Only two
All the three

Introduction to Big Data Techniques

First Principles Thinking: defining criterion

First Principles Thinking: governing relation

First Principles Thinking: classification rule

First Principles Thinking: decision rule

First Principles Thinking: decision rule

First Principles Thinking: defining criterion

First Principles Thinking: classification rule

First Principles Thinking: decision rule

First Principles Thinking: decision rule

First Principles Thinking: defining criterion

First Principles Thinking: defining criterion

First Principles Thinking: explanatory fit

First Principles Thinking: definition boundary

First Principles Thinking: classification rule

First Principles Thinking: classification rule

First Principles Thinking: decision rule

First Principles Thinking: governing relation

First Principles Thinking: scope rule

First Principles Thinking: decision rule

First Principles Thinking: decision rule

First Principles Thinking: decision rule

First Principles Thinking: decision rule

First Principles Thinking: decision rule

First Principles Thinking: decision rule

First Principles Thinking: classification rule

First Principles Thinking: decision rule

First Principles Thinking: classification rule

First Principles Thinking: classification rule