Responsible and Ethical AI certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Responsible and Ethical AI exam and earn Responsible and Ethical AI certificate.
Table of Contents
Question 1
Why is responsible AI practice important to an organization?
A. Responsible AI practice can help drive revenue.
B. Responsible AI practice can help build trust with customers and stakeholders.
C. Responsible AI practice can improve communication efficiency.
D. Responsible AI practice can help improve operational efficiency.
Answer
B. Responsible AI practice can help build trust with customers and stakeholders.
Explanation
Trust is fundamental for AI adoption and long-term business success.
Question 2
To build intelligence in computer machines, you would utilize:
A. All other answers are correct
B. Learning Algorithms
C. Human Intelligence to Analyze results
D. DATA
Answer
A. All other answers are correct
Explanation
All three components work together to build intelligent systems.
Question 3
AI systems are about making conclusions from large amounts of ______ in diverse forms.
A. Computers
B. Tools
C. Coding Libraries
D. Data
Answer
D. Data
Explanation
AI systems analyze data to identify patterns and make conclusions.
Question 4
Data challenges commonly faced by enterprises include:
A. Data governance issues
B. Data authentication problems
C. Managing social media data
D. Firewall security concerns
Answer
A. Data governance issues
Explanation
Data governance encompasses the most critical enterprise data challenges.
Question 5
Enterprise data can be in one or more of the following formats (select all that apply):
A. Graph data
B. Diagonal data
C. Structured
D. Text data
Answer
A. Graph data
C. Structured
D. Text data
Explanation
Graph data represents relationships and is increasingly important for enterprises.
Organized data in databases and spreadsheets is a common enterprise format.
Unstructured text like emails and documents is a major enterprise data type.
Question 6
Noise in data is:
A. Machine noise
B. System-generated noise
C. Random error or variance in a measured variable
D. Data from the surrounding noise
Answer
C. Random error or variance in a measured variable
Explanation
Data noise refers to random variations or errors that don’t represent true patterns.
Question 7
During the data exploration process, should outliers always be removed?
A. It depends on context
B. No
C. Yes
Answer
A. It depends on context
Explanation
Outlier treatment depends on whether they’re errors, anomalies of interest, or valid extreme values.
Question 8
What is the primary benefit of using data lakes as a data source?
A. Strong indexing and joins
B. High transaction consistency
C. Only supports structured data
D. Schema-on-read flexibility for diverse data types
Answer
D. Schema-on-read flexibility for diverse data types
Explanation
Data lakes allow storing raw data without predefined schemas, applying structure when reading.
Question 9
Which of the following are common sources of enterprise data? (Select all that apply)
A. NoSQL Databases (e.g., MongoDB, Cassandra)
B. Traditional Relational Databases (e.g., Oracle, MySQL)
C. Data Lakes (e.g., S3, Hadoop)
D. Data Virtualization Platforms
Answer
A. NoSQL Databases (e.g., MongoDB, Cassandra)
B. Traditional Relational Databases (e.g., Oracle, MySQL)
C. Data Lakes (e.g., S3, Hadoop)
D. Data Virtualization Platforms
Explanation
NoSQL databases like MongoDB handle diverse enterprise data requirements.
RDBMS like Oracle and MySQL are fundamental enterprise data sources.
Data lakes like S3 and Hadoop store vast amounts of enterprise data.
These platforms provide unified access to distributed enterprise data sources.
Question 10
What is a major ethical concern when multiple systems access and process the same personal data?
A. Better advertisement reach
B. Enhanced system speed
*C. Data privacy and consent violations
D. Increased data compression
Answer
Explanation
Multiple systems accessing data raises serious privacy and consent management issues.
Question 11
Which of the following are ethical risks when personal data is collected across multiple platforms and applications? (Select all that apply)
A. Improved user experience
*B. Consent management challenges
C. Reduced surveillance
*D. Data ownership ambiguity
*E. Inference of sensitive personal information
Answer
Explanation
Managing consent across multiple platforms is complex and often leads to violations.
Multiple platforms collecting data creates confusion about who owns and controls the information.
Combining data from multiple sources can reveal sensitive information users never intended to share.