IIA IIA-CIA-PART3-3P Exam Prep
Certified Internal Auditor - Part 3, Business Analysis and Information Technology (Page 131 )

• AWS AppSync is designed for serverless applications that need to retrieve and combine data from multiple sources.
• A pipeline resolver can invoke multiple functions or data-source resolvers, including resolvers connected to different DynamoDB tables.
• The application can receive the combined result through one GraphQL request, without requiring custom aggregation logic in the application.
• This is operationally efficient because AppSync manages the API layer and resolver execution.

• Athena Federated Query is intended primarily for analytics and ad hoc querying, not low-latency application data retrieval.
• Querying DynamoDB through Athena can consume DynamoDB read capacity and may affect table performance.
• It also introduces analytical-query overhead and is not the normal choice for a microservice web application.

• Laws and regulations
• Litigation holds
• Industry or contractual obligations
• Tax, financial, health, or privacy requirements

• Business strategy and direction: May influence retention, but cannot override legal requirements.
• Storage capacity and longevity: Practical considerations, not the primary driver.
• Business ease and value analysis: May support decisions after compliance obligations are identified.

• IRR assumes that interim cash flows can be reinvested at the project’s own IRR. This may be unrealistic, especially when the IRR is very high.
• MIRR assumes reinvestment at a specified rate, usually the company’s cost of capital or another realistic reinvestment rate.

• B: MIRR does not specifically favour projects with long payback periods.
• C: MIRR and IRR can rank competing projects differently.
• D: MIRR is not always higher than IRR; it may be higher or lower depending on the cash flows and reinvestment rate.

• A: Reduce management overhead — This is generally an advantage of public cloud, not hybrid cloud. Hybrid environments can increase management complexity.
• B: Cross-platform virtualization — Not the defining advantage of hybrid cloud.
• D: Leverage IaaS and PaaS — Public clouds commonly provide both IaaS and PaaS, so this is not unique to hybrid cloud.

• Each team’s Bedrock workflow uses a separate IAM service role.
• The role’s Amazon S3 permissions can be limited to only that team’s customer folder or objects.
• This enforces least privilege and prevents one team’s Bedrock application from accessing another team’s data.
• Access is enforced by AWS permissions, not by asking users to provide a customer name in each request.

• A single role with broad S3 access does not provide team-level isolation.
• Asking teams to specify a customer name is not an access-control mechanism.
• Redacting data does not prevent unauthorized access.
• A bucket policy alone is insufficient unless it explicitly enforces the required team-role restrictions.

• A: Describes a broad purpose and direction, but is not the specific CIMA definition.
• C: “Scoping out” activities is too vague and incomplete.
• D: Combines long-term goals, strategies and policies, but describes a wider strategic-planning statement rather than the definition of strategy itself.

• 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.

• 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.

• The requirement is to obtain an assessment of the Azure environment’s design.
• The proposed solution is the Premier support plan.
• Premier can provide this type of guidance, but it is a higher-tier, more expensive option.
• The question specifically says to keep costs to a minimum. A lower tier—typically Professional Direct in the support-plan model used by this question—provides architectural/design guidance.

• Existing measures
• Relationships
• Calculated columns
• Tables and fields
• Existing transformations in the model

• SharePoint folder: Reconnects to the source files. You would likely need to recreate the model and measures.
• Power BI dataflows: Primarily provide reusable data preparation and storage, but they do not automatically provide the existing semantic model and measures.
• Excel workbook: Connects directly to the source file and would also require rebuilding the model and measures.

• 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.

sql 
CREATE PROCEDURE dbo.usp_CreateOrder 
AS 
BEGIN 
    SET NOCOUNT ON; 
    SET XACT_ABORT ON; 


    BEGIN TRY 
        BEGIN TRANSACTION; 


        INSERT INTO dbo.Orders (/ columns /) 
        VALUES (/ values /); 


        SET @OrderID = SCOPE_IDENTITY(); 


        COMMIT TRANSACTION; 
    END TRY 
    BEGIN CATCH 
        IF @@TRANCOUNT > 0 
            ROLLBACK TRANSACTION; 


        THROW; 
    END CATCH; 
END;

• BEGIN TRY contains the transactional work.
• SCOPE_IDENTITY() retrieves the identity generated by the INSERT; it belongs after the insert, not as the error-handling logic.
• COMMIT runs only if all statements succeed.
• BEGIN CATCH handles failures.
• IF @@TRANCOUNT > 0 prevents attempting a rollback when no transaction is active.
• ROLLBACK undoes partial changes.
• THROW re-raises the original error with consistent details for the application.
• SET XACT_ABORT ON ensures many run-time errors automatically terminate and roll back the transaction.

• Honeypots: Deliberately vulnerable or attractive systems designed to lure attackers. Legitimate users should not access them, so activity on a honeypot is a strong indicator of compromise.
• DNS sinkholes: Redirect requests for known malicious domains to a controlled internal system instead of allowing the connection. This can identify infected hosts attempting to contact command-and-control infrastructure.

• Tokenization: Replaces sensitive data with non-sensitive tokens; it protects data but does not primarily detect attackers.
• CI/CD: Automates software development and deployment processes.
• Threat modeling: Identifies potential threats and weaknesses before or during system design.
• Data obfuscation: Makes data harder to understand, but it is not an attacker-detection method.

• CHAP: Uses a challenge-response exchange, so the password is not directly transmitted.
• IPsec: Provides authentication, integrity, and encryption for IP communications.
• EAP: An authentication framework that supports stronger methods, such as certificates or token-based authentication.

• Ease of exploitation
• Required privileges
• User interaction
• Impact on confidentiality, integrity, and availability

• CVE: Identifies and names a specific publicly known vulnerability; it does not measure severity.
• OSINT: Refers to intelligence gathered from publicly available sources.
• SOAR: Automates and coordinates security operations, such as alert response and playbooks.

• Honeypots: Decoy systems or services made to attract attackers. Legitimate users should not interact with them, so activity there is a strong indicator of malicious behavior.
• DNS sinkholes: Redirect requests for known malicious domains to a controlled internal system. This can reveal infected hosts attempting to contact command-and-control infrastructure.

• Tokenization: Replaces sensitive data with non-sensitive tokens.
• CI/CD: Automates software development, testing, and deployment.
• Threat modeling: Identifies and analyzes potential threats before or during system design.
• Data obfuscation: Makes data harder to understand or interpret.

• Configure row-level security (RLS) using the sales region and the HR manager’s identity.
• Publish the reports and datasets through a Power BI app.
• Grant the HR managers permission to use the app, but do not add them as workspace members.
• App users can view and interact with reports while RLS limits them to their region.
• They cannot modify the report layout because they do not have workspace editing permissions.

• Adding them to the workspace gives them workspace permissions that may allow report or dataset changes, depending on the assigned role.
• Creating copies is unnecessary and creates maintenance and security problems.
• Hosting datasets and reports in different workspaces does not, by itself, provide the required controlled access.

• Power BI dataset — Reuses the existing tables, relationships, and measures. You mainly create new visualizations.
• SharePoint folder — Connects to the source files, but you would need to rebuild the model and measures.
• Power BI dataflows — Primarily provide reusable data preparation; they do not automatically provide the existing report’s measures and model.
• Excel workbook — Requires reconnecting to and modeling the source data again.

• ARP poisoning: Redirects or intercepts local network traffic; monitoring would typically show suspicious ARP changes or man-in-the-middle behavior.
• Jamming: Interferes with wireless or radio signals. It would affect connectivity, not usually show a server-specific overload.
• Kerberoasting: Attempts to obtain and crack Kerberos service-account hashes; it does not normally cause a system outage.

• Firewall
• Intrusion prevention and detection
• Antivirus or antimalware
• Web and email filtering
• Virtual private networking (VPN)
• Sometimes content filtering and centralized reporting

• Data Category Groups e categorias;
• Visibilidade dessas categorias para perfis ou permission sets.

• B e C: regras de compartilhamento não são o mecanismo apropriado para restringir resultados de busca do Knowledge por divisão.
• D: a role hierarchy não é o mecanismo principal para controlar a visibilidade de categorias do Knowledge. Além disso, a configuração correta é atribuir visibilidade a perfis ou permission sets.

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

• What information each stakeholder needs
• When and how often it should be communicated
• Which format or channel should be used
• Who is responsible for communicating it

• A: The team should not decide stakeholder communications without first checking the established plan and CEO’s needs.
• B: An executive summary may be appropriate, but choosing a solution before reviewing the plan is premature.
• D: The stakeholder engagement plan addresses how to engage stakeholders, but this is primarily a communication planning issue.

• 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.

• Limiting the number of MAC addresses allowed on a switch port
• Restricting ports to specific known MAC addresses
• Taking an action—such as dropping frames or shutting down the port—when the limit is exceeded

• Load balancer: Distributes application traffic; it does not protect switch MAC tables.
• IPS: May detect some attack traffic, but is not the primary control for switch-port MAC limits.
• NGFW: Filters network traffic at the firewall, but does not directly control MAC addresses learned by a switch.

• An active relationship is used automatically when filters are evaluated. An inactive relationship does not propagate filters unless explicitly activated in DAX, so it would not reliably enforce the required security behavior.
• Apply security filter in both directions allows RLS filters to propagate across the relationship in both directions. This ensures that bidirectional filtering respects the RLS rules applied to both tables.
• Assume referential integrity is unrelated to RLS. It is mainly a DirectQuery optimization that allows Power BI to use an inner join when matching keys are guaranteed to exist.
• The relationship must therefore be active, with security filtering enabled in both directions.

• Continuous deployment: Automates software releases; it does not specifically test input handling.
• Static code analysis: Examines source or compiled code without executing it. It can find coding flaws but does not directly stress the application with malformed inputs.
• Manual peer review: Developers inspect code or design manually. Useful, but not the best practice specifically for testing how a live product handles invalid input.

• Asynchronous inference is designed for applications that submit individual inference requests, place them in a queue, and retrieve results later. It is useful when requests may take a long time or when traffic is intermittent.
• Batch transform is designed specifically to run inference over an entire large dataset stored in Amazon S3 and produce an output dataset.

• Large dataset/offline processing ? Batch transform
• Large or slow individual requests processed later ? Asynchronous inference

• B. Deploy to a SageMaker endpoint: This is needed for SageMaker-hosted models, not for a model intended to be used directly in Amazon Bedrock.
• C. Register with SageMaker Model Registry: SageMaker Model Registry is unrelated to invoking custom models in Bedrock.
• D. Grant access in Bedrock: Access permissions may be necessary, but they do not replace the required provisioned capacity.

Correct reasoning

• Create storage5 as a General-purpose v2 (StorageV2) account.
• Configure Blob object replication from storage5 to an existing compatible storage account.
• The destination must support object replication—typically another StorageV2 or premium block blob account.
• Blob versioning must be enabled on both accounts.
• Blob change feed must be enabled on the source account.
• The accounts must meet Azure’s object-replication requirements, including being in supported regions and having compatible configuration.

1. Create a temporary Onboarding OU.
2. Move the new account into it.
3. Move the restrictive SCPs from the organization root to the Production OU.
4. Allow the account to update AWS Config.
5. Move the account into Production after onboarding.

• An SCP attached at the organization root applies to every account and OU beneath it.
• An SCP attached to the Production OU applies only to accounts in that OU.
• Therefore, moving the restrictive SCP from the root to Production temporarily exempts the onboarding account without weakening the policies for existing production accounts.
• Once onboarding is complete, moving the account into Production subjects it to the normal restrictions.

• A. IDS attack signatures: An IDS primarily detects suspicious network activity; it is not the best control for scanning uploaded documents for malware.
• C. Load balancer: Distributes traffic among servers and improves availability, but does not inspect files for malware.
• D. WAF: Protects web applications against attacks such as SQL injection and command injection. It may help secure the upload function, but malware scanning is the key requirement here.

• Leaf switches provide access ports for servers, users, and other devices.
• Spine switches provide the backbone between leaf switches.
• Every leaf switch connects to every spine switch, creating a predictable, full-mesh fabric between the two layers.
• Leaf switches do not connect directly to other leaf switches, and spine switches do not connect directly to other spine switches.

• A: Adding both a spine and leaf is unnecessary for simply adding access ports.
• B: It focuses on uplink speed, not the scalable topology.
• D: A single spine connection creates a bottleneck and a single point of failure.

• 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.

• Restrict a port to a defined number of MAC addresses
• Use statically configured or “sticky” MAC addresses
• Shut down or restrict the port when violations occur

• Load balancer: Distributes application traffic; it does not protect switch MAC tables.
• IPS: May detect suspicious traffic but is not the primary control for switch-port MAC limits.
• NGFW: Filters routed network traffic, not the switch’s CAM/MAC address table directly.

1. Open the Microsoft Entra Connect wizard.
2. Select Configure.
3. Choose Customize synchronization options.
4. Authenticate when prompted.
5. On the Domain and OU filtering page, select:

1. Complete the wizard.

• Start-ADSyncSyncCycle only starts synchronization; it does not change which OUs are included.
• Manage federation configures federation settings, which are unrelated to OU filtering.
• Add-ADSyncConnectorAttributeInclusion concerns connector attributes, not selecting additional OUs for synchronization.

• A — Use getRecordTypeInfosByDeveloperName()

apex 
  Id rtId = Schema.SObjectType.Account 
      .getRecordTypeInfosByDeveloperName() 
      .get('Business_Account') 
      .getRecordTypeId();

• C — Query the RecordType object

apex 
  RecordType rt = [ 
      SELECT Id 
      FROM RecordType 
      WHERE SObjectType = 'Account' 
      AND Name = 'Business Account' 
      LIMIT 1 
  ];

• B: An outbound SOAP call is unnecessary and inefficient.
• D: Hardcoding an ID is not dynamic, and IDs can differ between orgs.

• Digitally signing the software: Verifies authenticity and integrity. It does not hide how the software works.
• Limiting third-party libraries: May reduce dependencies and attack surface, but does not directly prevent reverse engineering.
• Using compile flags: Some compiler options can remove debugging information or optimize code, which may provide limited protection, but this is not the primary technique identified for preventing reverse engineering.

• Create a CodeCommit repository in the secondary Region.
• Use CodeBuild to run a Git mirror operation from the primary repository to the secondary repository.
• Use EventBridge to detect relevant repository events, such as a pull request being merged.
• Invoke a Lambda function, which starts the CodeBuild project.

• B: Amazon S3 is object storage, not a Git repository that developers can use as a CodeCommit remote.
• C: CodeArtifact is intended for packages and artifacts, not source-code repository mirroring.
• D: An AWS Cloud9 environment is not an appropriate automated replication mechanism and would require manual or persistent infrastructure management.

• Un AG puede tener réplicas en Windows y Linux desde SQL Server 2017; por tanto, una réplica Windows y otra Linux es posible.
• Sin embargo, las réplicas de un AG deben utilizar normalmente la misma versión principal de SQL Server. Una instancia SQL Server 2017 no puede tener directamente una réplica secundaria SQL Server 2019 como configuración normal y permanente.
• En tu escenario, SQL Server 2017 en Windows y SQL Server 2019 en Linux no se pueden añadir directamente como réplicas equivalentes de AG1. Primero habría que realizar un proceso de actualización/migración compatible, por ejemplo mediante una actualización gradual o una tecnología de migración como log shipping o distributed availability groups, según el diseño.

• Tipo de clúster y mecanismo de quorum.
• Failover normalmente manual en configuraciones multiplataforma.
• Edición y características compatibles.
• Configuración de endpoints y permisos.
• Limitaciones específicas de SQL Server en Linux.

• A: Concerns task scheduling and avoiding missed runs, not directly sizing the warehouse.
• B: The amount of data in a stream alone does not reliably determine compute needs; the SQL processing is what matters.
• D: A multi-cluster warehouse addresses concurrency, not the compute size needed for one task execution.

• Remote and global work: Access is based on the approved device, not a specific office or geographic location.
• Seamless login: Users may not need to repeatedly enter credentials on a recognized device.
• Limited equipment: It uses devices employees already have rather than requiring separate hardware.

• Geotagging: Restricts access based on location and may prevent legitimate remote work.
• Smart cards: Require issuing and managing additional physical equipment.
• Time-based logins: Limit access by time and do not provide a seamless authentication experience.

• Approve major information security policies
• Allocate funding and resources
• Set the organization’s risk appetite
• Ensure security supports business objectives

• Evaluating vendors is generally handled by security, procurement, or technical teams.
• Assessing risks is typically performed by security and risk-management professionals, with management accepting or prioritizing the risks.
• Monitoring regulatory adherence is usually handled by compliance, audit, or security functions.

• 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.

• Documented and evaluated
• Assessed for security, cost, and operational impact
• Reviewed and approved by the appropriate personnel
• Tested before implementation
• Tracked with version history and rollback procedures

• Risk management process: May help assess the issues, but it does not authorize and manage design changes.
• Product design process: Applies primarily while creating a new design.
• Design review process: Normally evaluates a design before release; this design is already released.

• Business
• People
• Governance
• Platform
• Security
• Operations

• Cloud financial management
• Portfolio management
• Data governance
• Risk management
• Compliance

• Endpoint Manager: manages hosts and sensor-related settings.
• Falcon Investigator: supports investigation and analysis, but does not necessarily grant RTR access.
• Remediation Manager: handles remediation-related actions rather than interactive host access.

• Sales process: Controls which Opportunity Stage values are available.
• Record type: Associates the appropriate sales process with a business scenario and controls available picklist values.
• Page layout: Controls which fields are displayed and whether they are required or read-only.

• Energy Market Team 1 ? Record Type 1, Sales Process 1, Page Layout 1
• Energy Market Team 2 ? Record Type 2, Sales Process 2, Page Layout 2
• Services/Installation Team ? Record Type 3, Sales Process 3, Page Layout 3

• Namespace: The namespace associated with the code where the checkpoint was set.
• Time: When the checkpoint was captured or executed.

• Exception: Exceptions are viewed through debug logs or execution results, not as the primary Checkpoints tab information.
• Debug Statement: System.debug() output belongs in the Logs tab. A checkpoint captures program state, such as variables and their values, at a specific point in execution.