P CAIC 2 26+ P2 Flashcards

(32 cards)

1
Q

What does CCPA stand for?

A

California Consumer Privacy Act

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2
Q

What is the purpose of CCPA regulations?

A

They guide the implementation of the California Consumer Privacy Act.

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3
Q

Which states have modeled their consumer privacy regulations after CCPA?

A

Colorado and Virginia

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4
Q

What is the PIPEDA?

A

Personal Information Protection and Electronic Documents Act

It is a Canadian privacy law governing private sector organizations’ collection, use, and disclosure of personal information.

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5
Q

What is APEC?

A

Asia-Pacific Economic Cooperation

It is a set of principles and practices for data protection.

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6
Q

What is a data privacy policy?

A

A legal document that details how a website or mobile app can collect and use your personal data.

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7
Q

What is a common misconception about data privacy policies?

A

They give consumers a false sense of protection.

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8
Q

What did the New York Times create to assess privacy policies?

A

An infographic that maps the readability of 150 privacy policies.

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9
Q

What nefarious activity did iHandy Ltd. engage in?

A

They illegally mined personal data from other apps on Android phones.

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10
Q

What data does the iHandy app collect?

A

Precise user location, access to contacts, SMS messages, phone calls, camera, microphone, USB storage, and phone status.

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11
Q

What do digital home assistants like Amazon Alexa capture?

A

Everything being said in your home.

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12
Q

What was the outcome of the 2018 New Hampshire judge’s order regarding Amazon Echo?

A

Amazon was ordered to turn over recordings related to a double murder case.

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13
Q

What is the significance of data in the 21st century?

A

Data is considered the new oil, driving economic growth.

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14
Q

How do companies like Google monetize personal data?

A

By analyzing data to influence consumer decisions and preferences.

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15
Q

What is one way Google enhances customer experience in Gmail?

A

Through Intelligent Reply, which analyzes emails to suggest relevant replies.

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16
Q

What is the difference between Business Intelligence (BI) and data science?

A

BI focuses on historical data reporting, while data science predicts future outcomes and prescribes actions.

17
Q

What does BI software analyze?

A

Structured operational data to generate reports and dashboards.

18
Q

What is the primary focus of data science?

A

To provide predictions about future events and recommend actions based on those predictions.

19
Q

What is the nature of the data science development process?

A

It is highly iterative, collaborative, and exploratory.

20
Q

What is the data science development process?

A

A highly iterative, collaborative process to identify variables and metrics that predict performance.

21
Q

What does the data science development process support?

A

Testing, experimenting, failing, learning, unlearning, and retrying.

22
Q

What is the nature of the data science development process?

A

It is a non-linear framework of rapid exploration, discovery, learning, testing, and refining.

23
Q

Who collaborates in the data science development process?

A

Data scientists, data engineers, business experts, and operational subject-matter experts.

24
Q

What is emphasized in the data science approach?

A

Failure as a source of learning and exploration of new techniques and algorithms.

25
What is the first step in the data science collaborative development process?
Collaboration between business subject-matter experts and the data science team.
26
What is the second step in the data science collaborative development process?
Gathering and exploring a variety of data sources to find good predictors of behaviors and performance.
27
What is the third step in the data science collaborative development process?
Data engineering and munging to prepare the data for analytic modeling.
28
What is the fourth step in the data science collaborative development process?
Creating visualizations to understand the content of the data.
29
What is the fifth step in the data science collaborative development process?
Exploring different data transformations and analytic algorithms for optimal results.
30
What is the sixth step in the data science collaborative development process?
Examining the results of the analytic model to ascertain Goodness of Fit.
31
What does Goodness of Fit measure?
The correctness of positive predictions, sensitivity, and overall model performance.
32
What happens if the model results are not good enough?
Feedback is fed back into the data gathering step, and the process starts over.