Managing-Cloud-Security WGU Managing Cloud Security (JY02) Questions and Answers

Cloudsecurity groupstypically filter traffic based onports and protocols. By allowing or denying specific port/protocol combinations, engineers can control communication between deployments. For example, permitting HTTPS (TCP port 443) while blocking other ports enforces segmentation.

MAC addresses are not used in cloud-level segmentation because they apply to physical networks. Unique identifiers and definitions are not practical mechanisms for traffic filtering.

Using ports and protocols aligns with the principle of least privilege by ensuring that only necessary communication pathways exist. In multi-deployment or hybrid cloud setups, this reduces the attack surface and prevents lateral movement by malicious actors. Security groups thereby provide logical network segmentation without requiring physical infrastructure changes.

Once a vendor has been chosen, the onboarding phase requires confirmingcontractual details and arranging technical agreements. This includes specifying encryption standards, data transfer methods, SLAs, and compliance responsibilities. These discussions establish a clear foundation for the partnership.

Auditing and monitoring occur later, during ongoing vendor management. Evaluating requirements and policies occurs earlier, during vendor selection. Terminating a relationship is an offboarding activity, not onboarding.

Clarifying technical and contractual details at onboarding ensures a secure, compliant, and efficient partnership. It reduces risks of miscommunication and enforces accountability from the beginning.

The requirement for a username, password, and security token describesauthentication—the process of verifying the identity of a user. By requiring multiple factors (something you know + something you have), the organization is implementing multifactor authentication (MFA).

Authorization defines what resources a user can access after authentication. WAFs protect web applications, and ACLs specify rules for allowed or denied traffic, but neither validate user identity.

Authentication ensures that only legitimate users gain access to cloud resources. In hybrid environments, MFA is a strong safeguard against credential theft and phishing attacks, providing assurance that identities are genuine before authorization decisions are made.

Before transmitting sensitive financial data to the cloud, the best defense against interception threats like packet capture and man-in-the-middle attacks isencryption. Encryption protects data in transit by converting plain text into cipher text, which can only be deciphered with the correct keys.

Hashing provides integrity verification but does not secure confidentiality. Change tracking monitors modifications but does not prevent interception. Metadata labeling adds context but does not protect against on-path attackers.

Using strong encryption protocols (e.g., TLS) ensures that even if traffic is intercepted, the attacker cannot read the data. Encryption also aligns with compliance requirements such as PCI DSS, which mandates encryption for financial data during transmission. By encrypting before upload, the user ensures end-to-end confidentiality across potentially insecure networks.

Acceptance testingevaluates whether the system meets user requirements and performs as expected in real-world conditions. In this case, employees need the graphical interface to work properly for customer data entry. Acceptance testing confirms usability, accuracy, and functionality from the end user’s perspective.

Load testing measures performance under stress, regression testing checks for errors introduced by new changes, and security testing ensures system defenses. These are valuable, but they do not validate end-user satisfaction and workflow alignment.

Acceptance testing is the final validation step before production deployment. It ensures that updates deliver intended business value and user experience. By involving employees in acceptance testing, organizations ensure successful adoption of new systems.

Asandboxis a controlled, isolated environment used to safely run, observe, and analyze potentially malicious code. In cybersecurity, sandboxes allow analysts to execute malware samples without risking contamination of production systems. This enables identification of malware behavior, persistence techniques, and indicators of compromise.

Encryption protects confidentiality, but does not allow safe execution. Gateways control traffic flow, and controllers manage devices or workloads. Only a sandbox provides the dedicated containment required for malware analysis.

In cloud environments, sandboxing is often implemented at scale to analyze suspicious files or traffic automatically. This practice enhances defenses against zero-day exploits, advanced persistent threats, and polymorphic malware. By preventing malware from escaping, sandboxes provide essential forensic and detection insights without endangering the wider environment.

Bollardsare physical barriers designed to prevent vehicles from ramming into or breaching secure facilities. They are often placed at entrances, around perimeters, or in front of critical infrastructure like data centers.

Locks, cameras, and fences provide important physical security, but they cannot stop a high-speed vehicle from causing damage. Cameras record activity, fences create boundaries, and locks secure access points, but only bollards physically block or mitigate vehicle attacks.

Governmental and critical infrastructure sites commonly deploy bollards to protect against both accidental collisions and deliberate vehicle-borne attacks. Combined with layered security measures—such as surveillance and fencing—they enhance resilience against physical threats to sensitive data centers.

Backups are only valuable if they can be successfully restored when needed. Testing backups on a periodic basis is the only reliable way to validate their viability. Simply storing backups without testing may create a false sense of security, because corruption, misconfiguration, or incomplete backup sets can go unnoticed until a disaster occurs.

Industry best practices, such as those recommended by NIST and ISO 27031, emphasize regular backup testing as part of disaster recovery and business continuity planning. Testing involves restoring data to a test environment, verifying its integrity, and ensuring that applications can use the restored data as expected.

Deleting, comparing, or replacing backups might help in managing storage efficiency, but these actions do not confirm whether the backups are usable. Periodic testing ensures alignment with company policy, regulatory requirements, and internal risk management controls. It also provides confidence to management that recovery objectives, such as RTO (Recovery Time Objective) and RPO (Recovery Point Objective), can be met.

Federal agencies in the U.S. rely onNIST SP 800-37, Risk Management Framework (RMF), to manage enterprise risk. RMF provides a structured process for categorizing systems, selecting controls, implementing safeguards, assessing effectiveness, authorizing operations, and continuous monitoring.

ISO 37500 deals with outsourcing governance, SSAE 18 governs service provider audits, and COSO is a corporate governance framework but not specific to federal agencies.

NIST RMF is integrated with the Federal Information Security Modernization Act (FISMA) requirements, ensuring agencies manage cybersecurity risks consistently. Its adoption is expanding beyond government into industries seeking comprehensive, repeatable risk management processes.

The categories mentioned—relational, nonrelational, key-value, and document-oriented—refer to different types of databases. Relational databases (SQL) organize data into tables with rows and columns, nonrelational databases (NoSQL) provide flexibility for unstructured data, key-value stores map identifiers to values, and document-oriented databases manage data in formats such as JSON or BSON.

Object-based storage and volumes are alternative storage architectures but are not described by these categories. XML is a data format, not a storage type.

In the cloud, database services are offered as managed solutions, reducing the administrative burden on organizations. Properly managing database storage is critical for data governance, confidentiality, and compliance. Databases are also central to security strategies, where access control, encryption, and auditing are applied.

Thus, the correct answer is database storage, which encompasses multiple architectures that address different performance, scalability, and data management needs.

The engineers are concerned aboutportability. Vendor-specific APIs and tools create a dependency on a single provider, leading to vendor lock-in. This limits the ability to migrate services or workloads to another provider without significant rework.

Reliability and availability refer to service uptime and continuity, while scalability addresses performance under demand. Although important, none of these directly relate to cross-platform flexibility. Portability ensures that services, data, and applications can be easily moved or integrated across environments.

By adopting portable solutions—such as open standards, containerization, and multi-cloud strategies—organizations reduce long-term risks, increase negotiation power with providers, and enhance resilience.

In a cloud environment, responsibility forhardware managementshifts primarily to the cloud provider. Customers no longer manage servers, storage devices, or physical networks directly. As a result, hardware management policies are less critical for customer audits compared to data classification, procurement, or acceptable use.

Data classification remains essential to secure sensitive information. Software procurement policies are important to control licensing and compliance. Acceptable use policies govern employee behavior in cloud environments.

While organizations may still need high-level oversight of hardware through contracts and SLAs, detailed hardware policies have a reduced role. Instead, emphasis shifts to managing the shared responsibility model, ensuring cloud provider controls complement customer governance.

When sharing personal data with a trusted third party, organizations must ensure that the recipient understands and adheres to theorganization’s privacy policy and handling practices. This ensures consistent treatment of personal information across entities and aligns with consent provided by individuals.

Audit results and contractual notices are internal matters, while federal laws define obligations but do not substitute for organizational policies. By explicitly sharing policies and practices, organizations reinforce accountability and ensure compliance with privacy regulations such as GDPR, HIPAA, or CCPA.

This communication sets expectations for data use, retention, and disclosure. It also provides a defensible framework in case of regulatory inquiries, showing that due diligence was performed when transferring data to third parties.

The unplanned event described is adisruptionof service. In IT service management frameworks like ITIL, disruptions occur when an incident prevents normal service delivery. The goal of incident management is to restore service quickly and minimize impact on customers.

A bug refers to a software defect, which may cause disruptions but is not synonymous with the event itself. An error represents a fault, while change refers to deliberate modifications. Only disruption captures the unplanned nature of service unavailability.

Recognizing incidents as disruptions helps organizations apply structured processes such as escalation, root-cause analysis, and communication. It ensures resilience in cloud-based environments where uptime is a key performance indicator and customer trust is closely tied to availability.

Apipelinerefers to the structured process of moving code from development to production, encompassing implementation, review, automated testing, and deployment. In DevOps, this is known as a CI/CD pipeline (Continuous Integration/Continuous Deployment).

An iteration refers to a development cycle, a baseline represents a stable reference configuration, and a framework provides structure but not a deployment sequence. Only pipeline accurately captures the sequential, automated flow of code into production.

Pipelines enhance efficiency, consistency, and quality assurance by automating repetitive tasks, reducing human error, and ensuring that code changes are validated before reaching production. They are essential for modern cloud-native applications where rapid deployment is expected.

Cryptographic erasure is a secure data sanitization technique that relies on encryption. The process involves encrypting the data, encrypting the keys with a second layer, and then destroying the encryption keys. Without the keys, the encrypted data becomes unreadable and is effectively destroyed, even though the storage media remains intact.

One-way hashing is used for password storage, not full data destruction. Degaussing is for magnetic media, and overwriting involves physically writing new data over existing sectors.

Cryptographic erasure is widely used in cloud environments where physical media cannot be easily destroyed or reclaimed by customers. It ensures compliance with data retention and privacy regulations while maintaining environmental sustainability by allowing reuse of storage hardware.

The method described isstriping, which is a technique used in RAID configurations to improve performance and distribute risk. Striping involves splitting data into smaller segments and writing those segments across multiple disks simultaneously. For example, if a file is divided into four parts, each part is written to a separate disk in the RAID array.

This parallelism enhances input/output (I/O) performance because multiple drives can be accessed at once. It also provides resilience depending on the RAID level. While striping by itself (RAID 0) increases performance but not redundancy, when combined with mirroring or parity (e.g., RAID 5 or RAID 10), it offers both speed and fault tolerance.

The purpose of striping in the data management context is to optimize how data is stored, accessed, and protected. It is fundamentally different from archiving, mapping, or crypto-shredding, as those serve different objectives (long-term storage, logical placement, or secure deletion). Striping is central to high-performance storage systems and supports availability in mission-critical environments.

Categorizationis the process of systematically identifying and classifying files according to content and relevance. In preparation for a PCI DSS audit, it is critical to identify which files fall within scope—those that contain cardholder data or impact its security.

Normalization adjusts data format, tokenization substitutes sensitive data with tokens, and anonymization removes identifiers. While useful, none directly address the task of isolating “relevant files” for audit. Categorization ensures that files are grouped correctly, allowing auditors to focus on the proper scope and preventing unnecessary exposure of unrelated data.

This step aligns with PCI DSS requirements that limit scope to systems and data directly affecting cardholder data security. Proper categorization streamlines audits and demonstrates effective data governance.

TheOperational Excellencepillar emphasizes practices that allow organizations to develop, deploy, and operate workloads effectively. It includes monitoring operations, responding to incidents, and continuously improving processes. By embedding feedback loops, organizations enhance agility and ensure that technology supports business value.

Performance efficiency deals with using computing resources efficiently, reliability ensures system availability, and sustainability focuses on environmental responsibility. While important, these do not encompass the process-driven improvements at the heart of operational excellence.

Operational excellence ensures that organizations can adapt quickly to changes, implement automation, and drive consistent improvements across cloud workloads. It is a key principle in cloud frameworks like AWS Well-Architected, Microsoft CAF, and Google’s Reliability Engineering practices.

The scenario arises because ofmultitenancy, a core cloud characteristic where multiple customers share the same physical infrastructure. If a storage device containing multiple tenants’ data is seized as part of a case involving another customer, unrelated organizations may still be affected.

Virtualization enables resource abstraction, but multitenancy specifically introduces shared infrastructure risks. SaaS and PaaS are service models, not architectural characteristics.

Multitenancy offers efficiency and cost benefits but raises challenges for data sovereignty, legal jurisdiction, and evidentiary control. Providers mitigate these risks through encryption, logical isolation, and contractual terms. Customers must understand these implications when handling regulated or sensitive data in cloud environments.

TheDestroyphase of the cloud data life cycle is where information is permanently removed from systems. A common technique in cloud environments for this phase iscrypto-shredding(or cryptographic erasure). Rather than physically destroying the media, crypto-shredding involves deleting or revoking encryption keys used to protect the data. Once those keys are destroyed, the encrypted data becomes mathematically unrecoverable, even if the underlying storage media remains intact.

This method is particularly useful in cloud environments where storage is virtualized and hardware cannot easily be physically destroyed. Crypto-shredding provides compliance-friendly assurance that sensitive data such as personally identifiable information (PII), financial data, or healthcare records cannot be accessed after retention periods expire or contractual obligations end.

By incorporating crypto-shredding into theDestroyphase, organizations align with standards forsecure data sanitization. This ensures legal defensibility during audits and e-discovery and demonstrates proper lifecycle governance. The emphasis is on making data inaccessible while still maintaining operational efficiency and environmental responsibility.