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Free CIPT Exam Braindumps
Certified Information Privacy Technologist (CIPT) Questions 66–70 (page: 15)

Updated On: 1-Sep-2026
Page 15 of 66
PDF with 325 Questions

SCENARIO
You have just been hired by Ancillary.com, a seller of accessories for everything under the sun, including waterproof stickers for pool floats and decorative bands and cases for sunglasses. The company sells cell phone cases, e-cigarette cases, wine spouts, hanging air fresheners for homes and automobiles, book ends, kitchen implements, visors and shields for computer screens, passport holders, gardening tools and lawn ornaments, and catalogs full of health and beauty products. The list seems endless. As the CEO likes to say, Ancillary offers, without doubt, the widest assortment of low-price consumer products from a single company anywhere.

Ancillary's operations are similarly diverse. The company originated with a team of sales consultants selling home and beauty products at small parties in the homes of customers, and this base business is still thriving. However, the company now sells online through retail sites designated for industries and demographics, sites such as "My Cool Ride" for automobile-related products or "Zoomer" for gear aimed toward young adults. The company organization includes a plethora of divisions, units and outrigger operations, as Ancillary has been built along a decentered model rewarding individual initiative and flexibility, while also acquiring key assets. The retail sites seem to all function differently, and you wonder about their compliance with regulations and industry standards. Providing tech support to these sites is also a challenge, partly due to a variety of logins and authentication protocols.

You have been asked to lead three important new projects at Ancillary:

The first is the personal data management and security component of a multi-faceted initiative to unify the company's culture. For this project, you are considering using a series of third- party servers to provide company data and approved applications to employees.

The second project involves providing point of sales technology for the home sales force, allowing them to move beyond paper checks and manual credit card imprinting.

Finally, you are charged with developing privacy protections for a single web store housing all the company's product lines as well as products from affiliates. This new omnibus site will be known, aptly, as "Under the Sun." The Director of Marketing wants the site not only to sell Ancillary's products, but to link to additional products from other retailers through paid advertisements. You need to brief the executive team of security concerns posed by this approach.

What technology is under consideration in the first project in this scenario?

  1. Server driven controls.
  2. Cloud computing
  3. Data on demand
  4. MAC filtering

Answer(s): B

Explanation:

Answer(s): B – Cloud computing
Why this is the correct choice
The scenario describes using series of third-party servers to deliver company data and approved applications to employees. This is the classic definition of cloud computing —leveraging external, shared computing resources (IaaS, SaaS, or PaaS) that are accessed over a network rather than being hosted locally. Cloud solutions enable a unified cultural initiative by giving a consistent, centrally managed environment that can be accessed from any location, supporting the company’s decentralized structure while still providing control and security policies. It aligns with modern privacy-by-design and data-minimization principles required for a CIPT-level implementation, as cloud providers typically offer built-in encryption, identity-and-access-management, and audit logging.
Why the other options are less suitable

A: Server driven controls – This refers to controls enforced directly on on-premises servers; it does not capture the external, third-party hosting model described. C. Data on demand – This term generally denotes a service-level concept (e.g., on-demand data retrieval) but is not a recognized architectural paradigm for delivering an entire application suite via external servers. D. MAC filtering – This is a network-layer security technique that restricts access based on MAC addresses; it is unrelated to the overall model of delivering data and applications through third-party servers.


Reference:

NIST Definition of Cloud Computing – https://csrc.nist.gov/publications/detail/sp/800-145/final Cloud Security Alliance – Security Guidance for Critical Areas of Focus in Cloud Computing – https://cloudsecurityalliance.org/research/ccia-security-guidance-v4.0/ (PDF)



SCENARIO
You have just been hired by Ancillary.com, a seller of accessories for everything under the sun, including waterproof stickers for pool floats and decorative bands and cases for sunglasses. The company sells cell phone cases, e-cigarette cases, wine spouts, hanging air fresheners for homes and automobiles, book ends, kitchen implements, visors and shields for computer screens, passport holders, gardening tools and lawn ornaments, and catalogs full of health and beauty products. The list seems endless. As the CEO likes to say, Ancillary offers, without doubt, the widest assortment of low-price consumer products from a single company anywhere.

Ancillary's operations are similarly diverse. The company originated with a team of sales consultants selling home and beauty products at small parties in the homes of customers, and this base business is still thriving. However, the company now sells online through retail sites designated for industries and demographics, sites such as "My Cool Ride" for automobile-related products or "Zoomer" for gear aimed toward young adults. The company organization includes a plethora of divisions, units and outrigger operations, as Ancillary has been built along a decentered model rewarding individual initiative and flexibility, while also acquiring key assets. The retail sites seem to all function differently, and you wonder about their compliance with regulations and industry standards. Providing tech support to these sites is also a challenge, partly due to a variety of logins and authentication protocols.

You have been asked to lead three important new projects at Ancillary:

The first is the personal data management and security component of a multi-faceted initiative to unify the company's culture. For this project, you are considering using a series of third- party servers to provide company data and approved applications to employees.

The second project involves providing point of sales technology for the home sales force, allowing them to move beyond paper checks and manual credit card imprinting.

Finally, you are charged with developing privacy protections for a single web store housing all the company's product lines as well as products from affiliates. This new omnibus site will be known, aptly, as "Under the Sun." The Director of Marketing wants the site not only to sell Ancillary's products, but to link to additional products from other retailers through paid advertisements. You need to brief the executive team of security concerns posed by this approach.

Which should be used to allow the home sales force to accept payments using smartphones?

  1. Field transfer protocol.
  2. Cross-current translation.
  3. Near-field communication
  4. Radio Frequency Identification

Answer(s): C

Explanation:

Why Near-Field Communication (NFC) is the optimal choice
NFC provides short-range, two-way wireless communication (typically < 10 cm) that is native to modern smartphones, enabling secure, contact-less payment without the need for additional hardware attachments. The NFC stack can be paired with tokenization and dynamic cryptograms, delivering EMV-compliant transaction security that meets PCI-DSS requirements for mobile point-of-sale (POS) environments. Implementation is straightforward: a mobile app can expose an NFC reader API, allowing sales consultants to present the device to a payment terminal and complete a transaction in seconds, facilitating a seamless shift from paper checks and card-imprinters to smartphone-based payments.
Why the other options are unsuitable
Field Transfer Protocol – Generally refers to data-exchange mechanisms for device-to-device communication (e.g., Bluetooth) and lacks the built-in security and payment-tokenization frameworks required for financial transactions. Cross-Current Translation – Not a recognized standard or technology in mobile payments; the term does not describe a concrete communication method, making it impractical for integration. Radio Frequency Identification (RFID) – While RFID enables contact-less identification, most RFID implementations lack the cryptographic safeguards and standardized payment protocols (e.g., EMV) needed for secure monetary exchanges, and they are often used for inventory or access control rather than point-of-sale payments.
Conclusion Deploying NFC-based payment acceptance aligns with industry-standard mobile wallet architectures, supports secure tokenized transactions, and leverages the existing hardware capabilities of employee smartphones, making it the most appropriate solution for Ancillary’s home sales force.


Reference:

EMVCo, “EMV Contactless and Mobile Payment Specification” – https://www.emvco.com/resource-detail/emv-contactless-and-mobile-specification NFC Forum, “Specification for NFC Integrated Circuit and Platform” – https://www.nfc-forum.org/specifications-and-standards/specification-for-nfc-integrated-circuit-and-platform/



What is the best way to protect privacy on a geographic information system?

  1. Limiting the data provided to the system.
  2. Using a wireless encryption protocol.
  3. Scrambling location information.
  4. Using a firewall.

Answer(s): A

Explanation:

A 100
The privacy protecting technique that directly addresses geolocation sensitivity in GIS is spatial obfuscation through coordinate scrambling (e.g., adding random offsets or aggregating to larger units). This method reduces the risk that exact coordinates can be linked to individuals, which is a core concern for privacy-preserving spatial data. Limiting data (B) is a governance measure but does not inherently secure the locational information once it is stored; wireless encryption (D) protects data in transit, not the stored GIS layers; firewall (E) similarly protects network traffic but not the intrinsic data attributes. Scrambling location information directly mitigates the primary privacy threat of re-identification from precise coordinates.
Why other options are less suitable
B – Limiting data provided : helpful for minimization but does not anonymize the remaining spatial data; it only reduces quantity. C – Scrambling location information : correctly anonymizes the geographic coordinates, preventing direct linkage to individuals. D – Using a wireless encryption protocol : secures transmission but does not anonymize stored GIS layers; the data at rest remains identifiable. E – Using a firewall : protects network perimeters but offers no protection for the intrinsic privacy of the GIS data itself.


Reference:

NIST Privacy Framework – Geospatial Data Privacy: https://csrc.nist.gov/projects/privacy-framework/geospatial ISO/IEC 27001:2022 – Information security controls for spatial data: https://www.iso.org/standard/75155.html



In the realm of artificial intelligence, how has deep learning enabled greater implementation of machine learning?

  1. By using hand-coded classifiers like edge detection filters so that a program can identify where an object starts and stops.
  2. By increasing the size of neural networks and running massive amounts of data through the network to train it.
  3. By using algorithmic approaches such as decision tree learning and inductive logic programming.
  4. By hand coding software routines with a specific set of instructions to accomplish a task.

Answer(s): B

Explanation:

Answer(s): B – By increasing the size of neural networks and running massive amounts of data through the network to train it.
Why B is the best choice
Deep learning is defined by large-scale neural architectures (deep multilayer perceptrons, convolutional networks, etc.) that are trained computationally efficiently once sufficient data and hardware are available. Training these networks requires feeding them massive datasets, often billions of examples, and performing iterative weight updates across many epochs. This scale-up enables far richer feature representations than shallow methods. The result is a self-adjusting parameter space that automatically discovers hierarchical patterns (edges → shapes → objects) without manual feature engineering.
Why the other options are inferior

A: Hand-coded edge-detection filters are classic computer-vision primitives that belong to feature engineering approaches, a hallmark of traditional machine learning, not deep learning. C. Decision trees and inductive logic programming are algorithmic techniques that rely on explicit logical rules and shallow models; they do not involve neural network scaling. D. Hand-coded software routines are deterministic procedural code, opposite to the data-driven, adaptive nature of deep learning.


Reference:

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/ He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/1512.03385



Which of the following is an example of the privacy risks associated with the Internet of Things (loT)?

  1. A group of hackers infiltrate a power grid and cause a major blackout.
  2. An insurance company raises a person's rates based on driving habits gathered from a connected car.
  3. A website stores a cookie on a user's hard drive so the website can recognize the user on subsequent visits.
  4. A water district fines an individual after a meter reading reveals excess water use during drought conditions.

Answer(s): B

Explanation:

Why option B is the correct example of an IoT-related privacy risk
Context of IoT data collection – Connected cars continuously stream telemetry (speed, location, braking patterns, etc.) to the manufacturer or third-party services. Use of data for secondary purposes – Insurance firms can ingest this stream to build driver-behaviour models and adjust premiums, linking personally identifiable information (PII) to a commercial pricing decision without explicit, informed consent. Potential for discrimination or unfair treatment – Profile-based pricing may penalize certain driving styles that correlate with demographics, socioeconomic status, or health conditions, raising fairness and bias concerns. Regulatory relevance – Under GDPR and similar frameworks, such data is considered “personal data” when it can be used to single out an individual. Processing it for unrelated purposes without a lawful basis constitutes a privacy breach.
Why the other options are less suitable as IoT-specific privacy risks

A: Hackers causing a power-grid blackout – While a serious security incident, the primary risk described is availability and integrity of critical infrastructure, not the misuse of personal data.
C: Website storing a cookie – Cookies are a generic web-tracking mechanism; they are not exclusive to IoT devices and do not involve sensor data or connected-device ecosystems. D. Water district fine for excess use – The enforcement action is a regulatory response to consumption data, which is typically public-utility data rather than data originating from a personal, consumer-owned IoT device that raises privacy-specific concerns (e.g., profiling for commercial decisions).
Thus, the scenario in B best exemplifies a privacy risk intrinsic to IoT environments.


Reference:

European Union Agency for Cybersecurity (ENISA). IoT Privacy and Security – Guidance for Manufacturers and Users. https://www.enisa.europa.eu/publications/iot-privacy-and-security International Association of Privacy Professionals (IAPP). Certified Information Privacy Technologist (CIPT) Body of Knowledge – Module 4: Privacy in Emerging Technologies. https://iapp.org/c Knowledge-base/CIPT/ (accessed 2025)



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