Question 1

Typical responsibilities of technical data professionals (Select all):

• Transform raw data into useful information ✅

• Create business intelligence dashboards ✅

• Explore datasets ✅

• Build models and make predictions ✅

Explanation:
All listed tasks fall under the work of data analysts, BI developers, and data scientists.

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Question 2

Fill in the blank: Data professionals come together during _____ to create a solution using technology.

• hackathons ✅

• industry conferences ❌

• expos ❌

• networking luncheons ❌

Explanation:
Hackathons are events where teams rapidly develop tech-based solutions to real problems.

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Question 3

A national identification number is an example of:

• digital identification ❌

• identity analytics ❌

• personally identifiable information ✅

• mapped data ❌

Explanation:
A national ID directly identifies a person → PII.

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Question 4

Collecting information from enough people to represent the population describes:

• Affiliating ❌

• A/B testing ❌

• Aliasing ❌

• Aggregating ✅

Explanation:
Aggregating means combining data from many individuals to represent the broader population.

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Question 5

A good sample represents:

• The outliers ❌

• The entire population ✅

• A portion of the population ❌

• Half the population ❌

Explanation:
A proper sample is representative of the full population, not just a subset.

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Question 6

Common ways to maintain privacy when working with data (Select all):

• Data anonymization ✅

• Data shuffling ✅

• Data aggregation ✅

• Removing last names ❌ (insufficient alone)

Explanation:
Privacy involves removing linkages, shuffling identifiers, and aggregating details.

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Question 7

Who is responsible for socially beneficial, ethical, and unbiased data practices?

• Only BI professionals ❌

• All data professionals ✅

• Only project managers ❌

• Only IT professionals ❌

Explanation:
Ethics in data is the responsibility of everyone who handles data.

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Question 8

A data professional examines personal beliefs to prevent influence on data:

• Avoiding subtle biases in data work ✅

• Establishing data security procedures ❌

• Generating data from communication ❌

• Protecting privacy ❌

Explanation:
This scenario describes awareness and mitigation of unconscious bias.

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Question 9

Examples of using data to solve a problem (Select all):

• Airline employee uses ML to predict flight demand ✅

• Restaurant president changes suppliers against customer data ❌

• Power plant technician uses sensor data to find vulnerabilities ✅

• Executive refuses to expand based on tradition, not data ❌

Explanation:
Only decisions driven by data qualify.

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Question 10

NOT a core skill of a data professional:

• Interpersonal skills ❌

• Always being correct ✅

• Active listening ❌

• Critical thinking ❌

Explanation:
No one can always be correct. Data professionals rely on iterative learning, not perfection.

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🧾 Summary Table

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