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WorksheetsBUSINESS ANALYTICS Midterm Examination
Total questions: 70
Worksheet time: 35mins
Business analytics primarily focuses on using data to support which activity?
Artistic design
Legal compliance
Decision-making
Customer service
Analytics is best described as the process of:
Storing data securely
Discovering and interpreting meaningful patterns in data
Marketing products
Automating workflows
Which question is answered by descriptive analytics?
What happened?
Why did this happen?
What might happen?
What should we do next?
Predictive analytics is mainly concerned with:
Past performance
Future outcomes
Policy enforcement
Data storage
Prescriptive analytics answers which question?
What happened last year?
What caused the problem?
What should we do?
What data do we have?
Which analytics type helps recommend actions?
Prescriptive
Diagnostic
Predictive
Descriptive
The CRISP-DM framework begins with:
Data preparation
Modeling
Business understanding
Deployment
Identifying available versus missing data occurs in which phase?
Business understanding
Data understanding
Modeling
Evaluation
The most time-consuming phase of analytics is usually:
Modeling
Evaluation
Deployment
Data preparation
A model is best defined as:
A. A database
B. A visualization
C. A simplified representation used for prediction
D. A report
Which task belongs to the modeling phase?
Data cleaning
Business problem definition
Variable selection
Data storage
Model effectiveness is assessed during:
Evaluation
Modeling
Data preparation
Business understanding
Business intelligence, decision science, and analytics all aim to:
Replace managers
Automate sales
Eliminate risk
Turn data into insights
Which is an example of a business analytics question?
Who designed the logo?
Who qualifies for a credit limit increase?
Where is the office located?
Who owns the company?
Analytics supports businesses mainly by improving:
Creativity
Guesswork
Intuition
Data-driven decisions
In finance, business analysts are best described as:
Auditors
Accountants
Interpreters of data and strategy
Regulators
Financial data analysis helps answer which question?
Who hired the staff?
Is the company profitable?
Where is the office?
What brand logo is used?
Which is NOT a purpose of financial data analysis?
Identifying trends
Comparing performance
Designing marketing logos
Supporting decisions
Identifying rising expenses is an example of:
Trend identification
Business process analysis
Risk analysis
Fraud detection
Risk analysis focuses on identifying events that could:
Increase revenue
Improve morale
Negatively affect outcomes
Enhance branding
Likelihood in risk analysis refers to:
Size of loss
Time to recover
Chance of occurrence
Cost of mitigation
Impact in risk analysis measures:
Probability
Frequency
Severity of consequences
Duration
Which step comes LAST in risk analysis?
Identifying risks
Assessing impact
Prioritizing risks
Mitigation planning
A machine breakdown is an example of:
Financial risk
Strategic risk
Operational risk
Market risk
Supplier delays affecting project timelines illustrate:
Financial risk
Operational risk
Project management risk
Credit risk
Business process analysis focuses on improving:
How work is done
Employee salaries
Company branding
Product pricing
Bottlenecks in a process refer to:
Profits
Inputs
Outputs
Points of delay or error
Which is evaluated in business process analysis?
Market share
Stock prices
Tools and technology used
Customer demographics
Fraud detection analytics is mainly used to identify:
Loyal customers
Profitable products
Suspicious activities
Marketing trends
Credit card fraud is an example of:
Operational error
Financial crime
Market risk
Pricing issue
Fraud detection relies heavily on identifying:
Employee schedules
Sales forecasts
Anomalies and unusual patterns
Annual budgets
A sudden jump from ₱500 to ₱50,000 spending signals:
Anomaly
Growth opportunity
Customer loyalty
Seasonal trend
Z-scores are used in fraud detection to:
Price assets
Manage staff
Detect abnormal behavior
Track inventory
Asset management primarily involves:
Employee training
Managing pooled investments
Selling insurance
Loan approval
Wealth management is best described as:
Product sales
Cost accounting
Holistic financial advisory
Market regulation
Data exploration helps analysts understand:
Employee behavior
Marketing slogans
Data distribution and outliers
Legal contracts
An outlier is a data point that:
Is common
Is average
Deviates significantly from others
Repeats frequently
One key benefit of data exploration is:
Eliminating all risks
Improving data quality
Increasing sales directly
Reducing taxes
Raw facts and figures are called:
Data
Knowledge
Wisdom
Information
Organized tables of data are considered:
Structured
Semi-structured
Unstructured
Visual
Emails and social media comments are examples of:
Structured data
Unstructured data
Semi-structured data
Numeric data
Videos and images fall under:
Structured data
Semi-structured data
Financial data
Unstructured data
Which is a key financial metric?
Customer age
Market share
Gross profit
Employee count
Gross profit is computed as:
Revenue – expenses
Revenue – COGS
Net profit – tax
Assets – liabilities
Net profit considers:
Only sales
Only costs
Operating expenses
Market share
Profit margin measures:
Total revenue
Total cost
Profit relative to revenue
Break-even time
Which chart best shows trends over time?
Line chart
Pie chart
Scatter plot
Bar chart
Bar charts are best used for:
Relationships
Percentages
Category comparisons
Time trends
A pie chart is most suitable for showing:
Trends
Relationships
Frequencies
Percentage share
Scatter plots are used to visualize:
Time patterns
Categories
Distributions
Relationships between variables
Good data visualization should avoid:
Clear labels
Consistent colors
Unnecessary clutter
Proper chart choice
The primary goal of visualization is to:
Decorate reports
Show all data
Impress clients
Highlight insights
Dashboards in Excel are used to:
Write code
Store raw data
Summarize key metrics visually
Clean datasets
Data exploration improves predictive modeling by:
Increasing sample size
Reducing costs
Understanding data characteristics
Automating decisions
Deleting anomalies helps in:
Increasing revenue
Improving analysis accuracy
Branding
Marketing
Data sources may include:
Only databases
Only surveys
Only financial records
Multiple internal and external sources
Revenue refers to:
Costs incurred
Net income
Total income earned
Operating expenses
COGS represents:
Marketing cost
Labor only
Direct cost of production
Tax expense
Break-even point indicates when:
Profit is maximized
Losses increase
Revenue equals costs
Sales peak
Data analytics in wealth management improves:
Office design
Advertising
Investment decision-making
Recruitment
Personalized client profiling relies on:
Data analytics
Manual review
Guesswork
Random sampling
Fraud prevention in AWM is supported by:
Interviews
Paper audits
Analytical monitoring
Visual design
Financial analytics helps firms primarily to:
Eliminate uncertainty
Remove competition
Manage risk and growth
Avoid regulation
Business analytics in finance operates in a world where decisions are made:
In milliseconds
Weekly
Daily
Monthly
Risk prioritization helps managers decide:
Who to hire
What to sell
Which risks need urgent attention
Where to advertise
An open market tariff arrangement mainly affects:
Employee wages
International trade flows
Customer loyalty
Production layout
Asset price forecasting relies heavily on:
Opinion surveys
Historical intuition
Analytical models
Random choice
Business analytics ultimately supports:
Creativity over logic
Intuition-based decisions
Strategic and operational planning
Trial-and-error
Data-driven decisions are preferable because they are:
Evidence-based
Cheaper only
Faster only
Always correct
The overall value of business analytics is best summarized as:
Data storage
Technology adoption
Reporting
Turning data into actionable insights
