ISACA AAIA Exam Prep
ISACA Advanced in AI Audit (Page 53 )

• A. Adjust resolution: Incorrect because an incompatible resolution usually causes no display, distortion, or an unsupported-mode message—not intermittent flickering.
• B. Swap the HDMI cable: Correct. Replacing the common, easily tested component is the appropriate troubleshooting step.
• C. Replace the projector: Incorrect because the issue also occurs with a monitor.
• D. Configure refresh rate: A mismatched refresh rate can cause visual problems, but the repeated flicker across displays points more strongly to the cable.

• Data only needs to be current at 7 AM each day, so scheduled refresh is sufficient.
• Import mode provides the fastest report interaction because data is stored in Power BI’s in-memory engine.
• It supports row-level security (RLS), including the USERNAME() filter.
• You can limit the imported data to the current year and previous three years to reduce model size.
• DirectQuery would generally provide slower visual response times and is unnecessary here.
• Dual is mainly useful in composite models where some tables use DirectQuery and others use Import. That is not required by this scenario.
• Live connection is not appropriate because you are creating the semantic model.

• The JSON files contain one billion items, so this is a big-data workload.
• PySpark uses Apache Spark’s distributed, parallel processing, allowing the data to be processed across multiple compute nodes.
• It can read and transform data directly from OneLake, helping minimize unnecessary data movement or duplication.
• A Fabric notebook can also support exploration and visualization, as well as Spark-based analytics and anomaly-detection workflows.

• Pandas is primarily in-memory and generally suited to smaller datasets. One billion records would be impractical on a typical single machine.
• A Power BI report with core visuals is appropriate for visualization and sharing, but it is not the best tool for large-scale transformation, parallel processing, or anomaly detection. It would normally consume prepared data rather than perform the entire workload.

• A user connects with their Google account.
• Google associates the user’s SSH key with that identity.
• IAM roles determine whether they can log in.
• Access can be tied to a specific employee for auditing.
• Removing the IAM role removes their login access.

• compute.osLogin: regular OS login access
• compute.osAdminLogin: OS login with administrative privileges, typically including sudo

text 
enable-oslogin=TRUE

• MFA: Protects sign-in and authentication.
• Azure Key Vault: Protects deployment secrets and credentials.
• Answer: No (B).

• 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.

• Least privilege: Limits users to only the access necessary for their job.
• Background checks: Help identify potentially risky individuals before employment.
• Separation of duties: Ensures that one person cannot complete a sensitive process alone, reducing fraud and abuse.

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

• SQL injection
• Cross-site scripting (XSS)
• Malicious or malformed web requests
• Some application-layer abuse

• Layer 4 firewall: Filters based mainly on IP addresses, ports, and connections; it lacks detailed web-request inspection.
• NGFW: Provides broader network security, but a WAF is more specialized for protecting web applications.
• UTM: Combines multiple security functions, but it is not necessarily optimized for web-application protection.

• 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.

• A. IDS attack signatures: Detects suspicious network activity, but is not primarily designed to scan uploaded files for malware.
• C. Load balancer: Distributes traffic among servers and improves availability; it does not inspect files for threats.
• D. WAF: Protects web applications from attacks such as SQL injection and command injection, but its primary role is not malware scanning of uploaded documents.

• 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.

• The selling division receives a credit based on a favourable transfer price, often market price or full cost plus a profit margin.
• The receiving division is charged a lower amount, such as marginal cost or another agreed price.
• The difference is accounted for centrally by head office or a corporate adjustment.

• Type 1 hypervisor: Runs directly on the physical hardware (“bare metal”).
• Type 2 hypervisor: Runs inside the host OS as an application.

• Bare-metal installation: Type 1
• Local management only: Not a defining characteristic
• Specific hardware requirements: Not the key distinction

• transform() — modifies every element in an array
• filter() — keeps elements matching a condition
• exists() — checks whether any element matches
• aggregate() — reduces array elements to one result

sql 
SELECT transform(scores, x -> x * 2) 
FROM student_scores;

• A is incorrect because higher-order functions are mainly useful for nested or complex data.
• B is incorrect because they do not convert logic into Python.
• D is incorrect because their purpose is not to fix slow built-in functions, but to process arrays or maps with custom logic.

• Dataset file: Use Import mode. Excel files are imported into Power BI, and the two Sales tables can be combined with Power Query.
• Report file: Use a live connection to the published dataset. This keeps the report separate from the dataset and avoids importing the data again.

• Report file: DirectQuery/live connection
• Dataset file: Import

text 
ip route vrf Customer1 172.16.1.0 255.255.255.0 172.16.1.1 global 
ip route vrf Customer2 192.168.1.200 255.255.255.255 192.168.1.1 global 
ip route 192.168.1.0 255.255.255.0 Vlan10 
ip route 172.16.1.0 255.255.255.0 Vlan20

• The route in Customer1 VRF provides a path back to the Customer2 subnet, 172.16.1.0/24.
• The route in Customer2 VRF provides a path to the FTP server, 192.168.1.200.
• The keyword global tells the router to resolve the next hop using the global routing table, enabling controlled route leaking between the VRF and global table.
• The global routes identify the VLAN interfaces used to reach each customer network.

• Partners first log in to the corporate portal, which uses the shared SAML Identity Provider.
• When they select a Salesforce link, the IdP can send a SAML assertion directly to Salesforce.
• Salesforce validates the assertion and grants access to the Partner Community without requiring another login. This is IdP-initiated SSO because authentication begins outside Salesforce.

• SP-initiated SSO starts when Salesforce sends an authentication request to the IdP. That would be appropriate if users began by visiting Salesforce.
• The Web Server and User Agent OAuth flows are for OAuth authorization, not browser-based SAML federation between an IdP and Salesforce.

• Determining ISP-assigned address space: Usually involves public reconnaissance, such as WHOIS or regional Internet registry information.
• Bypassing DNS sinkholing: Would require evidence that malicious domains are resolving outside the organization’s sinkhole controls.
• Achieving initial access: DNS log entries alone do not demonstrate compromise or entry.
• Exfiltrating data: DNS tunneling generally involves repeated queries containing encoded or unusually long data, not merely internal host discovery.

• A — Capacity: The system must handle all current records and anticipated future records. A system that cannot cope with volume will quickly become ineffective.
• C — Accuracy: Academic records are critical, so the new system must be at least as accurate as the existing system and comparable systems. Poor accuracy could cause serious administrative and reputational problems.
• D — Integration: Linking with the university’s financial systems supports consistency and avoids duplicate data entry, especially for registration, deregistration and fee-related transactions.

• B — Parallel running: This is a useful implementation and risk-control technique, but it is not an essential feature of the system itself. It may also be expensive and time-consuming.
• E — Internet access for all information: This creates significant confidentiality and security risks. External access might be needed for selected services, but unrestricted access to all information is not essential and would be inappropriate for student records.

• Due diligence: Researching and evaluating a third party before entering or continuing a business relationship.
• Internal audit: Evaluates controls within the organization itself.
• Penetration testing: Actively tests systems for exploitable vulnerabilities.
• Attestation: A formal statement or report—such as a SOC 2 report—provided by an independent party about an organization’s controls.

• SAS (Serial Attached SCSI):

• SSD (solid-state drive):

• \Pipeline1
• \Folder1\Lakehouse1

• Pipeline1 remains at the workspace root in Workspace1, so it is deployed to the root of Workspace2.
• Lakehouse1 was moved into Folder1, so its full path is preserved as \Folder1\Lakehouse1.
• Fabric deployment pipelines preserve folder paths because an item’s full path is treated as part of its name.

• B — Data source settings in Power BI Desktop:

• D — Formula bar in Power Query Editor:

m 
  Excel.Workbook(File.Contents("new path"))

• E — Advanced Editor in Power Query Editor:

• A: Dataset settings in the Power BI service mainly handle credentials and gateway settings, not changing the local file path embedded in the PBIX.
• C: Current File data-load settings control refresh/load behavior, not the source location.

• An Adj-SID does not necessarily need manual configuration.
• It can often be dynamically allocated by the router, although manual configuration may also be possible.
• The adjacency information is advertised through the IGP, such as IS-IS or OSPF.

• It is identified by an Adj-SID.
• It represents a particular adjacency of a node.
• Its meaning is generally local to the advertising node; unlike a Node-SID, it is not a globally reusable instruction across the network.

• Third-party enterprise application with embedded AI: The provider manages most security controls. The customer mainly manages users, data, and configuration.
• Application using an existing third-party FM: The customer controls the application and its inputs/outputs, but the provider controls the underlying model and infrastructure.
• Fine-tuning a third-party FM: The customer has additional responsibility for training data, data protection, and model behavior, but still depends on the provider’s base model and platform.
• Building and training a model from scratch: The customer controls the model, training data, application, deployment, and many security safeguards. This creates the greatest security ownership.

• Training or coaching
• Mentoring
• Additional support or resources
• Targeted performance improvement plans

• A: Recognition may be appropriate, but awards do not address identified gaps.
• C: Rotating schedules does not necessarily improve the specific competencies that are missing.
• D: Assessment results should be handled sensitively, but keeping them entirely private prevents useful feedback and development discussions.

• PC A: 192.168.1.10/24
• PC B: 192.168.2.10/24

• Both PCs have a correct default gateway
• A router or Layer 3 switch connects the subnets
• Routing and firewall rules allow the traffic

• Malware or malicious software
• Command-and-control tools
• Internet surveillance and intrusion software

• ITAR: A U.S. regulation controlling defense-related articles and services; it is not the international arrangement described.
• Palermo Convention: Focuses on transnational organized crime and related protocols, not export controls for cyber weapons.
• GDPR: European data-protection and privacy legislation.

1. The specific table — for example, incident
2. The parent table — for example, task
3. The wildcard table — *, which applies broadly

• Label applied does not automatically exclude a file from Copilot.
• Encryption/usage rights determine whether Copilot can access the file’s content.
• If the user has no permission under Label2, File3 should be unavailable to Copilot for that user.
• File3’s label might be inherited by derived content only if File3 was actually processed and contributed by an authorized user; it should not be treated as contributing merely because it exists in the source set.

• 10 minutes have elapsed, or
• 20 MB of accounting data has accumulated

• The S3 bucket as an origin for static content
• The Application Load Balancer (ALB) as an origin for dynamic content
• An Amazon Route 53 alias record pointing the application domain to CloudFront

Why this works

• Static data: CloudFront caches objects from S3 at edge locations, reducing latency for users worldwide.
• Dynamic data: CloudFront forwards requests that require application processing to the ALB. It can also maintain persistent connections and use the AWS global edge network to improve delivery performance.
• One domain: CloudFront behaviors can route different path patterns to different origins—for example, /static/* to S3 and other paths to the ALB.

Why the other options are incorrect

• Global Accelerator is designed primarily for accelerating traffic to applications with endpoints such as ALBs, not for caching S3 content.
• Using separate domains or splitting static and dynamic traffic adds unnecessary complexity.
• CloudFront already provides the required global edge distribution and caching, so combining it with Global Accelerator is unnecessary here.

• Controlar o fluxo de dados entre S3 e SageMaker;
• Aplicar regras de segurança por meio da VPC;
• Reduzir a exposição dos dados;
• Permitir acesso privado ao bucket do S3.

• Amazon Inspector: avalia vulnerabilidades, mas não gerencia o fluxo de dados.
• Amazon Macie: ajuda a descobrir e proteger dados sensíveis no S3, mas não cria a conexão com o SageMaker.
• S3 Glacier Deep Archive: é uma classe de armazenamento para arquivamento, não uma solução de conectividade.

• QuickVisualize is used to create and render a Power BI report from a DataFrame.
• The typical pattern is:

python 
PBI_visualize = QuickVisualize( 
    get_dataset_config(df), 
    auth=device_auth 
) 


PBI_visualize

• get_dataset_config(df) prepares the dataset configuration.
• auth=device_auth supplies authentication for the Power BI connection.
• Evaluating PBI_visualize renders the report in the notebook.

• Box 1: No
• Box 2: Yes
• Box 3: Yes

• Matching hashes: The software likely has not changed.
• Different hashes: The software was modified or corrupted.

• Encryption: Protects confidentiality by making data unreadable; it does not primarily detect changes.
• Baselines: Define an approved system or configuration state, but they are not the specific integrity mechanism here.
• Tokenization: Replaces sensitive data with substitute values.

• The Azure DevOps (Boards only) connector is designed specifically for Azure DevOps Analytics views.
• It authenticates directly to Azure DevOps and lets you select an existing Analytics view.
• Filters defined in the Analytics view are applied by Azure DevOps before Power BI retrieves the data, reducing the amount of data transferred.

• Azure DevOps Server (Boards only) is for on-premises Azure DevOps Server.
• OData Feed can consume OData endpoints, but it is not the dedicated Azure DevOps Analytics-view connector.
• OData queries are not the intended dedicated connector for this requirement.

• C: Develop the rule in the Rules Editor, define the events, match, and condition sections, then use the test rule feature. This directly validates the YARA-L rule without enabling it in production.
• A: UDM Search can help investigate data and refine filters, but it does not fully test YARA-L logic—especially complex event joins, match windows, and conditions.

• Microsoft Foundry is an enterprise platform for developers, data scientists, and IT teams.
• It supports the full AI application lifecycle: selecting models, connecting enterprise data, developing, evaluating, deploying, monitoring, and scaling solutions.
• It can include visual tools and low-code experiences, but it is not primarily designed to enable business users to build complete generative AI solutions independently.

• B: You still need to select and configure an appropriate model.
• C: Business users may participate through visual tools, but they are not the primary target audience.
• D: Foundry includes low-code tools, but it is not fundamentally a low-code-only platform.

• Tokens: Individual units of text processed by a model, such as words, subwords, or characters.
• Models: Systems that learn patterns and generate predictions or outputs.
• Binaries: Data represented using the binary number system, not semantic representations.

• Note the question number before leaving—for example, Question 38.
• Use the exam’s question navigator or Back/Previous button to return to that number.
• If you close or refresh the page, your position may not be saved, depending on the site. Reopen the page and select the question number again.
• Avoid using the browser’s Back button if it causes the exam page to reload; use the exam’s built-in navigation instead.

• Confirming the data has the required columns and data types
• Handling missing or duplicate records
• Converting data into a suitable format
• Selecting or engineering useful features
• Splitting it into training, validation, and test sets

1. Prepare the training data
2. Select an algorithm or configure AutoML
3. Train the model
4. Evaluate its performance
5. Tune hyperparameters if needed
6. Deploy the model

• Technical platform interfaces: Important for integration and interoperability, but they should support—not drive—the architecture.
• Scalability of the network: Valuable as the organization grows, but a scalable solution is ineffective if it does not meet business or security needs.
• Development methodologies: Relevant to implementation, but they do not define the organization’s fundamental security requirements.

• The type of change: standard, normal, or emergency
• The risk, impact, and complexity
• The change model being used

• A standard change may be pre-authorized through its change model.
• A normal change may require assessment and approval by an appropriate change authority.
• An emergency change may be authorized by an emergency change authority, often using an expedited process.

• Change authorities are not only needed for emergency changes.
• They are not assigned only when a change is deployed.
• They are not only required for normal changes.

• Accept: Acknowledging the risk and choosing not to take additional action.
• Transfer: Shifting the risk or financial consequences to another party, such as an insurer.
• Mitigate: Reducing the likelihood or impact through controls, such as patching or access restrictions.
• Avoid: Eliminating the activity that creates the risk.

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.

• DB1 (500 TB): Azure Data Box Heavy

• Virtual machines: Azure Migrate

• File hashes
• Strings and metadata
• Binary or code structure
• Known malicious patterns
• Embedded indicators or signatures

• Sandbox: Executes the file in an isolated environment; useful for observing behavior, but usually slower.
• Network traffic analysis: Examines communications generated by a system or application, not the file itself.
• Package monitoring: Tracks software packages or installations and is not the primary method for examining malware signatures.

• Record changes — a flow can start when a record is updated or otherwise changes.
• Schedules — a flow can run at a specified date/time or on a schedule.
• Record inserts — a flow can start when a new record is created.

• Subflows are reusable flow components called by another flow; they are not normally trigger types.
• Record views do not generally initiate a Flow Designer flow.