WorksheetsECE420 - Finals Q1
Total questions: 50
Worksheet time: 38mins
Which of the following should be identified first before collecting IoT data?
Storage size
Business needs and objectives
Type of sensors
Network bandwidth
Determining the types of data required helps organizations:
Save money on cloud subscriptions
Collect relevant and useful information
Increase data redundancy
Eliminate the need for analysis
Analyzing data correlation allows businesses to:
Collect random data
Discover patterns between data sets
Delete outdated data
Avoid using metadata
Collaborative data integration requires teamwork between:
Engineers and suppliers
Developers and business leaders
Customers and investors
Sales and marketing staff
Addressing data drift helps ensure:
Data becomes outdated faster
Consistency in data meaning and structure
Decrease in data processing
Elimination of metadata
Which of the following is an example of a business need in IoT?
Reducing machine downtime
Installing more sensors
Collecting all available data
Upgrading to 5G
Before starting IoT data management, companies should determine:
Data transfer speed
Required storage capacity
Mobile network provider
Device color
Which ensures that the data collected is relevant to company goals?
Identifying business objectives
Using complex algorithms
Randomized data sampling
Avoiding cloud platforms
Data correlation helps reduce:
Data redundancy and unnecessary storage
Employee involvement
Device communication
Security measures
Collaboration in data integration enhances:
Unused data collection
Predictive analytics and data alignment
Hardware maintenance
Customer complaints
Asking “What are you going to do with your data?” focuses on:
Purpose of data usage
File format type
Network protocol
Device model
The question “Which types of data will be needed?” helps determine:
Required bandwidth
Relevant data sources
Device warranty
Backup schedule
How long data should be kept is known as:
Data recycling
Data retention period
Data renewal
Data filtering
Analyzing how data sets relate to one another helps understand:
Data correlation
Data encryption
Data caching
Data compression
Before implementing IoT, it’s important to assess:
Device color
Infrastructure capacity
Employee hobbies
App version
Archival data refers to:
Real-time sensor readings
Old but still useful data
Deleted system logs
Temporary cache files
The platform for managing gathered data should be:
Scalable and secure
Cheap and local
Unmanaged and simple
Offline only
“How much data are you sending to the cloud?” deals with:
Data transmission volume
Device calibration
User permissions
File naming
Recording data before and after decisions ensures:
Accountability and validation
Storage waste
Faster hardware
Less documentation
The purpose of robust data management questions is to:
Guide strategy formulation
Avoid using the cloud
Simplify device setup
Replace data analytics
Robust data management ensures data is:
Accessible, reliable, and secure
Encrypted and deleted
Duplicated across networks
Randomly generated
The main purpose of robust data management is to:
Enable effective decision-making
Increase file size
Slow down IoT systems
Replace analytics
Data drift refers to:
Intentional data deletion
Undocumented changes in data meaning or structure
Improved data accuracy
Sudden system crash
Managing data drift requires:
Ignoring data updates
Continuous monitoring and adjustment
Turning off devices
Collecting less data
If “status” values change from ON/OFF to ACTIVE/INACTIVE, this is an example of:
Data drift
Metadata
Data compression
Encryption
A strong data management system helps support:
Predictive analytics
Data corruption
Random sampling
Unauthorized access
One way to prevent disruption during data drift is:
Continuous system updates
Manual data deletion
Hardware rebooting
Ignoring changes
Which best describes “architecture” in data management?
The framework for organizing and processing data
The physical design of sensors
Office layout
Programming syntax
What happens when data drift is not managed?
Analytics become inaccurate
Storage increases
Devices run faster
Cost decreases
Which is NOT part of robust data management?
Data organization
Data security
Data encryption
Data negligence
Metadata is best defined as:
Data about data
Raw data from sensors
Encrypted files
Temporary cache
Metadata gives IoT data:
Context and meaning
Random values
Encryption only
Power consumption
An example of metadata is:
Timestamp and location of data collection
The device’s physical appearance
Power source
Manufacturer’s address
Metadata improves data management by:
Organizing and cataloging data
Deleting old data
Reducing device count
Disabling sensors
Metadata helps in:
Quick data retrieval and analysis
Hardware calibration
Network configuration
Data corruption
Without metadata, IoT data becomes:
Difficult to interpret
More secure
Automatically analyzed
Perfectly labeled
Metadata supports:
Decision-making and analytics
Data erasure
Device deactivation
Manual input
In IoT systems, metadata acts like:
A. A library catalog for data
B. A network firewall
C. A physical storage device
D. A backup generator
Metadata ensures that data is:
A. Organized and searchable
B. Hidden and locked
C. Encrypted only
D. Stored locally
One key function of metadata is to:
Enhance data discoverability
Hide system errors
Delete old records
Minimize network speed
A well-implemented data management strategy leads to:
Enhanced operational efficiency
Increased system errors
Random data collection
Higher downtime
Real-time monitoring enables:
Proactive decision-making
Data drift
Power saving
Manual recording
Proactive management means:
A. Acting before problems occur
B. Waiting for failures
C. Ignoring alerts
D. Delaying maintenance
Personalized customer experience is achieved through:
Data analytics and machine learning
Manual feedback
Paper-based reports
Guesswork
Personalization in IoT increases:
Customer loyalty
Data loss
Manual labor
Hardware cost
Improved data quality means data is:
Consistent, complete, and standardized
Encrypted only
Randomized and messy
Incomplete but fast
High data quality improves:
Discoverability and decision-making
Network downtime
Manual errors
File corruption
Operational efficiency in IoT helps:
Detect and fix issues early
Delay maintenance
Remove sensors
Increase downtime
A good data management strategy transforms decision-making from:
Reactive to proactive
Manual to random
Predictive to passive
Automatic to manual
Overall, IoT data management aims to make systems:
Smarter, faster, and more reliable
Slower but cheaper
Manual and paper-based
Complex but inefficient
