ISC CISSP-ISSMP Exam Prep
CISSP-ISSMP Information Systems Security Management Professional (Page 23 )

• An operational audit evaluates day-to-day security operations and controls. Since John’s issue is about a user on a secure wireless network and how the security measures are functioning in real time (configurations, authentication, monitoring, access controls), an operational audit focuses on those live procedures to verify they’re working as intended.

• Independent audit (external party) is for formal compliance or assurance, not for immediate problem resolution.
• Non-operational audit deals with strategic or non-daily aspects, not the current functioning of security controls.
• Dependent audit is not a standard ISC/IT term and isn’t applicable here.

• Perform internal checks of the wireless security setup (SSID, encryption, authentication method), client configurations, and relevant logs to diagnose the connectivity issue and ensure operations align with policy.

• The Clark-Wilson model is an integrity-focused model that requires all access to constrained data items to go through well-formed transactions and integrity checks, i.e., through applications. This mediation by transformation procedures (TP) and integrity verification procedures (IVP) ensures data integrity because users never modify data directly.
• In contrast:

• A: Configure security groups — Customers define inbound and outbound rules for resources such as Amazon EC2 instances.
• B: Classify company assets — Customers decide how their data and assets should be classified and protected.

• C: Amazon S3 is a regional service; customers select a Region, not individual Availability Zones for buckets.
• D: AWS manages the infrastructure and patching for Amazon DynamoDB.
• E: AWS manages the infrastructure used by AWS Lambda; customers do not select EC2 instances for Lambda functions.

• MFA adds an additional authentication factor, such as a phone notification or verification code.
• It helps prevent unauthorized sign-ins, even if a password is compromised.
• It does not encrypt, store, or protect credentials used by deployment processes.

• SQL injection
• Cross-site scripting (XSS)
• Malicious file uploads
• Common web-application exploits

• Layer 4 firewall: Filters primarily by IP addresses, ports, and transport protocols; it lacks detailed web-application inspection.
• NGFW: Provides advanced network filtering and may include application awareness, but a WAF is more specialized for protecting web applications.
• UTM: Combines several security functions in one device, but it is not specifically focused on web-application attacks.

• Azure creates a corresponding service principal in Microsoft Entra ID.
• The identity’s lifecycle is tied to the Azure resource.
• If the resource is deleted, Azure automatically deletes the identity.
• Only the associated resource can use that identity to request tokens.
• You can grant the identity permissions to access services such as Key Vault or Storage without storing credentials in code.

• B — Reviewing the strategy: Usually performed by a security steering committee or other governance body.
• C — Communicating the strategy: The security manager may help communicate it, but communication is not the primary governance responsibility tested here.
• D — Approving the strategy: Approval belongs to executive management or the appropriate governing authority, not the person who develops it.

• Internal hostnames and subdomains
• Network structure and naming conventions
• Servers and services
• Relationships between systems

• Determining ISP-assigned address space: Usually involves external registration and DNS information, not internal host discovery.
• Bypassing DNS sinkholing: Would require evidence that malicious DNS requests avoided redirection.
• Initial access: DNS queries alone do not show that the attacker gained access.
• Exfiltrating data: DNS tunneling can do this, but the snippet would typically show encoded or unusually long DNS queries.

• Replaces the verbal message — this describes non-verbal communication being used instead of words.
• Substitutes for verbal communication — similar; it means communicating without speech.
• Confuses the verbal message — this occurs when non-verbal signals contradict or obscure the spoken message.

• IaaS: você gerencia a VM e o sistema operacional.
• PaaS: o Azure gerencia a infraestrutura; você se concentra no aplicativo.

• Online payment fraud
• Account takeover
• Fake account creation
• Suspicious transactions

• Amazon SageMaker — charges apply for compute, storage, and other resources used.
• AWS Config — generally charges for configuration recording and rule evaluations.
• Amazon CloudWatch — has a limited free tier, but many metrics, logs, alarms, and API usage can incur charges.

• Azure Machine Learning is the overall Azure service.
• Machine Learning designer is the visual, drag-and-drop interface within Azure Machine Learning.
• Microsoft may refer to the broader product as Azure Machine Learning studio, while “Designer” remains the specific visual workflow tool.

• Azure Logic Apps: Workflow and integration automation.
• Azure Batch: Large-scale parallel or batch compute.
• Azure Cosmos DB: Globally distributed NoSQL database.

• state preserves the client’s original context, such as the specific idea URL the user requested.
• The portal sends that value in the OAuth authorization request.
• Salesforce returns it after authorization, allowing the portal to redirect the user to the original idea rather than the Ideas home page.

• Maintain head office control: Transfer pricing generally supports decentralisation rather than direct head-office control.
• Establish centralised decision making: This conflicts with divisional autonomy.
• Develop a top-down culture: Transfer pricing is intended to support empowered divisions and goal congruence, not impose purely top-down management.

• 255.255.255.128 = /25: 128 total, 126 usable
• 255.255.255.192 = /26: 64 total, 62 usable
• 255.255.255.224 = /27: 32 total, 30 usable
• 255.255.255.240 = /28: 16 total, 14 usable

• One for the network address
• One for the broadcast address

• 8 total addresses
• 6 usable addresses
• Mask: 255.255.255.248

• If the hashes match, the software has likely not changed.
• If they differ, the software was modified or corrupted.

• Encryption: Protects confidentiality by making data unreadable; it does not primarily verify that software was unchanged.
• Baselines: Establish expected configurations or states, but hashing is the specific method for checking file integrity.
• Tokenization: Replaces sensitive data with non-sensitive tokens and is unrelated to software integrity.

• File1.png — Yes: User1 can apply Label1 with the Microsoft Purview Information Protection client. The client supports labeling image files such as .png in this scenario.

• Folder2 — No: Folder2 is the root folder. The client does not allow labeling the root folder itself. Although the client can label ordinary folders, selecting one applies the label to existing files and subfolders—not automatically to future files.

• File1.docx — Yes: .docx is a supported Microsoft Word file type, so User1 can apply Label1 to it.

• The message came from the claimed sender (authentication)
• The message was not altered in transit (integrity)
• The sender cannot easily deny having signed and sent it (non-repudiation)

• Compliance standards: May be a reason in some organizations, but it is not the primary security benefit.
• Increase delivery rates: Digital signatures do not generally improve email delivery.
• Block phishing attacks: Signatures can help recipients verify legitimate messages, but they do not block phishing by themselves.

• Accept: Acknowledging the risk without taking additional action.
• Transfer: Shifting responsibility or financial impact to another party, such as through insurance or outsourcing.
• Mitigate: Reducing the likelihood or impact of the risk through controls.
• Avoid: Eliminating the activity or condition that creates the risk.

1. Get Data > Folder

1. Expand the Attributes column

1. Remove the Content column

• Folder connector: lists files and their metadata, but also includes a Content binary column.
• Combine Files: processes and combines the actual CSV contents, which is explicitly disallowed here.

• The current and previous versions
• Which clauses were changed
• When each revision occurred
• Potentially, who made the changes

• Version validation: Verifies a version’s accuracy or integrity but does not primarily track revisions.
• Version changes: Not the standard name for a formal document-management process.
• Version updates: Describes updating a document, not the complete process of managing and tracking its revisions.

• Microservices: An application-design approach that divides an application into smaller services. It does not inherently reduce the number of operating systems.
• Virtualization: Creates virtual machines, but each VM generally requires its own complete operating system. This improves isolation but does not minimize OS instances as effectively.
• Infrastructure as code: Uses code to automate infrastructure deployment and configuration. It improves consistency but does not determine how many operating systems are used.

1. Identify the fact table

• Records something that happened, such as a sale, test, appointment, or measurement
• Contains foreign keys to other tables
• Contains measures or numeric results
• Has many rows

2. Identify a dimension table

• Describe people, products, dates, locations, or departments
• Contain descriptive columns
• Are referenced by the fact table
• Have fewer rows than the fact table

3. Identify the surrogate key

• System-generated
• Unique within the dimension
• Often named PatientKey, CustomerKey, or similar
• Usually an integer
• Not meaningful outside the model

• Physical: Protects facilities or equipment, such as locks and security cameras.
• Technical: Uses technology, such as firewalls, encryption, or access controls.
• Operational: Covers day-to-day actions and processes, such as training, backups, or incident response.
• Managerial: Includes policies, standards, risk assessments, and procedures.

• BCP (Business Continuity Plan): Ensures the organization can continue its broader business operations during and after a disruption. It is broader than the IT-focused recovery required here.
• Communication plan: Defines how information is shared with employees, the public, and other stakeholders; it does not restore systems.
• IRP (Incident Response Plan): Handles and contains security incidents, such as malware or unauthorized access. A tornado is a disaster, not primarily a cybersecurity incident.

1. Find the subnet-mask increment in the octet where the prefix ends.
2. Identify the network address by finding the subnet boundary at or below the given IP.
3. The first usable address is network address + 1.
4. The broadcast address is one address before the next subnet.
5. The last usable address is broadcast - 1.

• 172.28.228.144/18

• 172.28.228.144/23

• Existing expected ROI: 15%
• New project ROI: 14%
• Cost of capital: 12%

• Project return = 14%
• Cost of capital = 12%
• It earns 2% more than the cost of capital, so it should increase shareholder value.

• Assign endpoints to a group based on the result of one policy.
• Use that group as the scope of another policy.
• This effectively passes the endpoints from one policy’s processing flow to another.

• A — Correct: Identify the data required by Salesforce, its dependencies, and how it should be validated. Verification scripts help confirm record counts, relationships, field mappings, and data integrity after migration.
• D — Correct: Use a full sandbox and perform trial migrations using realistic—or appropriately masked—legacy data. This exposes transformation, volume, dependency, and integration issues before production cutover.
• B — Less appropriate: Phased user migration and parallel operation primarily address user adoption and rollout risk, not the technical risks of data migration.
• C — Less appropriate: Although sandbox testing and integration testing are valuable, the option emphasizes test-data generation rather than validating the migration with representative legacy data. “A full sandbox for all systems” may also be impractical.

• Valor maior: mais aderência às instruções e aos detalhes descritos.
• Valor menor: mais liberdade criativa e resultados potencialmente mais variados.
• Valor excessivamente alto: pode causar artefatos ou imagens menos naturais.

• Aumentar o número de passos de geração: pode melhorar qualidade e refinamento, mas não é o principal controle de especificidade do prompt.
• MASK_IMAGE_BLACK: relaciona-se ao uso de máscara em geração ou edição de imagens, não à especificidade textual.
• Prompt strength: é mais associado à força do prompt em processos como image-to-image; nesta questão, o parâmetro diretamente ligado à aderência textual é o CFG scale.

• Check whether the data is accurate, complete, and properly prepared.
• Confirm that the pattern is not caused by an outlier, data-quality problem, or modeling issue.
• If the data is valid, treat the result as a potentially valuable new business insight.

• A: A data scientist may help, but expert validation and data-quality checking come first.
• C: Filtering out inconvenient data risks hiding a valid insight.
• D: Einstein’s result should not be accepted without verification simply because it produced the finding.

• Employees can work from any location, including remote sites and offices worldwide.
• Login can be relatively seamless after the device is registered.
• It does not require distributing additional physical authentication equipment.

• Geotagging: Identifies location, but location alone is not a strong authentication method and may prevent legitimate remote access.
• Smart cards: Provide strong authentication but require card readers and physical cards, increasing equipment and management requirements.
• Time-based logins: Typically involve one-time codes that users must enter, making the experience less seamless and potentially requiring a token or authenticator app.

• Future costs — Only costs occurring in the future can be affected by the investment decision. Past costs are sunk and irrelevant.
• Incremental costs — These are additional costs caused by choosing the investment. They are relevant because they differ between alternatives.
• Cash flows — Capital investment decisions are based on future cash inflows and outflows, usually discounted to present value.

• Committed costs have already been agreed or incurred and generally cannot be changed by the decision.
• Unavoidable costs will arise regardless of which option is selected, so they do not affect the choice.

• Threat perspective: Focuses on possible attacks, but may seem technical or hypothetical.
• Compliance perspective: Can motivate action, but compliance alone does not demonstrate the broader business value of governance.
• Policy perspective: Policies are important, but they are a means of implementing governance, not its primary business justification.

apex 
for (Account updatedAccount : Trigger.new) { 
    AccountTriggerHelper.notinyxternalSystem(updatedAccount.Id); 
}

• Too many callouts: The callout occurs asynchronously in the future method, and each future transaction can perform its permitted callouts.
• Too many SOQL queries: The SOQL query is inside the future execution, not repeatedly in the trigger transaction.
• Callout from triggers not supported: Direct callouts from triggers are prohibited, but the trigger delegates the callout to asynchronous Apex, which is the correct pattern.

• Initial Intrusion: The compromised VPN account and stolen phishing credentials describe how the attacker first gained access, but that happened months earlier.
• Persistence: The scheduled tasks and recurring C2 connections show how the attacker maintained and automated continued access.
• Search and Exfiltration: This would involve locating and transferring data. The scenario does not primarily describe data discovery or theft.
• Cleanup: Cleanup involves removing evidence or covering tracks. Obfuscated scripts may make detection harder, but the main behavior here is maintaining access.

• Correct exam answer: A — HKEY_LOCAL_MACHINE
• The relevant location is associated with HKEY_LOCAL_MACHINE\SAM.
• The SAM does not store passwords in plain text; it stores password hashes.

• HKEY_CURRENT_USER contains settings for the currently logged-in user, not the account-password database.
• HKEY_USERS contains user profiles and settings loaded for users.
• HKEY_CURRENT_CONFIGURATION contains hardware/profile configuration information.

• You can configure different pre-shared keys for different device groups.
• All cameras could use one key.
• All scanners could use another key.
• Each key can be associated with an appropriate role or access policy.

• Database data: Aggregates are used to retrieve structured data, commonly from entities and database sources.
• Screen scope: A Screen Aggregate belongs to the screen where it is defined and is not directly reusable as a screen-level aggregate elsewhere.
• Asynchronous and parallel execution: Multiple Screen Aggregates can run asynchronously and in parallel, helping the screen load data efficiently.

• Code suggestions: Inline completions as you write code, such as lines, functions, or blocks.
• Chat: An interactive interface for asking questions, explaining code, generating code, and helping with debugging.

• Knowledge Base: Not listed as a standard Copilot Individual IDE feature in this context.
• Pull Request Diff Analysis: More closely associated with GitHub-based code review or pull-request features, not the core Individual IDE extension capabilities.

• A — .gitignore: Controls Git tracking; it does not configure Copilot exclusions.
• B — copilot.ignore: This is not the standard GitHub Copilot Business exclusion mechanism.
• D — Renaming files: Adding _no_copilot has no recognized effect.

• Domínios personalizados.
• Armazenamento suficiente.
• Instâncias dedicadas.
• Balanceamento de carga.
• Menor custo entre os planos que atendem a tudo.

• Server provisioning
• Operating systems and runtime infrastructure
• Scaling
• Much of the maintenance and patching

• Thin clients reduce endpoint hardware requirements but do not specifically streamline code deployment.
• Private cloud provides dedicated cloud infrastructure but usually requires more management and expense.
• Virtual machines improve resource utilization, but administrators still manage the VMs, operating systems, and deployments.

• A. IDS attack signatures: Primarily detects suspicious network activity, not necessarily malware embedded in uploaded files.
• C. Load balancer: Improves availability and distributes traffic; it does not inspect documents for malware.
• D. WAF: Protects web applications from attacks such as SQL injection and command injection, but its primary role is not scanning uploaded PDFs for malware.

• Prevention costs: Avoiding defects, such as staff training and improved product design. The company has invested in these.
• Appraisal costs: Detecting defects before products reach customers, such as inspection and testing. The planned batch sampling has not been implemented.
• Internal failure costs: Correcting defects found before delivery.
• External failure costs: Dealing with defects after delivery, such as refunds, replacements, complaints and lost sales.

• Regression predicts continuous numeric outcomes, such as house price, temperature, or sales amount.
• Linear regression assumes the target can be estimated from a linear relationship between the input features and the target.
• In Azure Machine Learning, you use a labeled dataset containing:

• Classification predicts a category or class, such as “spam” or “not spam.”
• Clustering groups similar records without a known label; it is an unsupervised learning technique.

• Azure Cosmos DB is a globally distributed NoSQL database service. It stores and retrieves application data, but it does not provide the complete environment for building, testing, and deploying predictive analytics models.
• Azure Machine Learning is the appropriate service for this requirement. It supports creating experiments, training and testing models, and deploying them for predictions.
• Azure Machine Learning Studio provides the browser-based interface for these tasks.