Test Prep HESI A2 Exam Prep
HESI Admission Assessment Exam (A2) (Page 64 )

• 16GB RAM: A 64-bit OS is needed to use more than roughly 4GB effectively, but RAM capacity does not prove the CPU supports 64-bit operation.
• 500GB HDD: Storage capacity has no bearing on OS architecture.
• 1Gbps Ethernet: Network speed is unrelated to CPU architecture.

• Árvores de decisão: usam regras sequenciais, como “se característica X > valor, siga para este ramo”. São fáceis de visualizar e explicar. Atendem bem a problemas de classificação e regressão com alta interpretabilidade.

• Regressão linear: prevê um valor numérico contínuo, como preço, custo ou temperatura. Não é a escolha principal para classificar genes em categorias.

• Regressão logística: calcula a probabilidade de uma categoria. Pode atender à classificação, inclusive multiclasses, mas sua explicação costuma ser menos intuitiva que uma árvore para documentar decisões.

• Redes neurais: atendem a tarefas complexas, como imagens, texto e padrões não lineares. Porém, são menos interpretáveis, funcionando mais como uma “caixa-preta”.

• Data loss prevention (DLP): Helps detect or prevent unauthorized data transfers, but does not directly protect data on a stolen, powered-off laptop.
• Operating system hardening: Reduces vulnerabilities and attack surface, but does not adequately protect stored data if the device is stolen.
• Boot security: Helps prevent unauthorized operating systems or boot media from loading. It supports device security but is not the primary solution for protecting the data itself.

• BIOS language support: Useful for usability, but not whether the PC can operate electrically.
• Supply chain security: Concerns procurement and hardware trust, not deployment across countries.
• Power efficiency: Important for energy usage, but it does not ensure compatibility with local power systems.

• vBond — orchestration plane: Authenticates devices, helps them discover each other, and assists with NAT traversal and initial control-connection setup.
• vSmart — control plane: Distributes routing and policy information to WAN Edge devices using the overlay control plane.
• vManage — management plane: Provides centralized configuration, monitoring, and orchestration through its GUI/API.
• WAN Edge — data plane: Forwards user traffic across the SD-WAN fabric.

• AWS Budgets lets you create a cost or usage budget and configure alerts when actual or forecasted spending reaches a threshold. Notifications can be sent through email or Amazon SNS.
• Amazon CloudWatch can monitor AWS billing metrics and trigger an alarm when estimated charges exceed a specified amount.

• Amazon SQS is a message queue, not a cost-monitoring service.
• Cost Explorer analyzes spending but does not directly provide threshold notifications.
• AWS Cost and Usage Report delivers detailed billing data for analysis; it does not itself send threshold alerts.

• Vertical scaling (scale up) means increasing the size of an existing VM—for example, adding CPU or RAM.
• Vertical scaling down means reducing the VM’s size when less capacity is needed.
• Horizontal scaling (scale out) means adding more VM instances rather than making one VM larger.
• Elasticity is the broader ability to automatically adjust resources based on demand; it does not specifically mean adding CPU or memory to one VM.
• Agility refers to the ability to provision or change resources quickly.

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.

• A vulnerability scanner automates discovery of known vulnerabilities and misconfigurations across systems. It can contribute to a supply-chain assessment.
• A penetration test actively attempts exploitation and is usually limited to an agreed scope.
• SCAP standardizes vulnerability and configuration assessment data; it is not the analysis itself.
• Illumination tool is not a commonly defined CompTIA Security+ term.

• Reviewing and validating generated code
• Checking security, licensing, and quality concerns
• Testing the implementation
• Making the final design and coding decisions

• A: Copilot is not a complete replacement for developers.
• C: Human input, review, and oversight are still required.
• D: Copilot Chat supports many tasks beyond debugging, including explanation, generation, refactoring, and testing assistance.

• 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.

• 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.

• Espionage: Stealing confidential information, often for a competitor or government.
• Data exfiltration: Unauthorized transfer of data outside the organization. This could occur, but the question focuses on using unapproved access methods.
• Nation-state attack: An attack sponsored or conducted by a government.
• Shadow IT: Employees using technology or access methods outside the organization’s approved security controls.

• 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.

• Normalize casing in the data source—for example, convert all values to uppercase or lowercase.
• Add an index key to provide a unique, stable identifier for each row or value.

• 16 VMs: one failure loses 25 GB and 10 cores—only 6.25% of memory.
• 8 VMs: one failure loses 12.5% of memory.
• 4 VMs: one failure loses 25%.
• 1 VM: one failure loses the entire application environment.

• R-squared (R²): mede quanto da variação dos valores reais é explicada pelo modelo. É mais usado em regressão, não em classificação.

• Accuracy (acurácia): mede a proporção de previsões corretas em relação ao total. É adequada para a classificação de imagens da questão.

• RMSE (Root Mean Squared Error): mede o tamanho médio dos erros entre valores previstos e reais, penalizando erros maiores. É usado principalmente em regressão.

• Learning rate (taxa de aprendizado): controla o tamanho dos ajustes feitos pelo modelo durante o treinamento. Não é uma métrica de avaliação; é um hiperparâmetro.

1. Selling price per unit: $750
2. Required annual return:

1. Required profit per unit:

1. Target cost per unit:

• Access controls — ensuring only authorized users can access it.
• Encryption — protecting the data if it is intercepted or exposed.

• Disaster recovery backups
• Long-term backup retention
• Data archiving that still needs occasional access

• Multi-Regional Storage: Intended for frequently accessed data needing broad geographic availability; it costs more.
• Regional Storage: Suitable for frequently accessed data within one region.
• Nearline Storage: Intended for data accessed roughly once per month, such as monthly backups.
• Coldline Storage: Better fit for backups accessed only during a disaster or occasional recovery event.

• The requirement explicitly says the organization wants to use SAML to secure Salesforce API access.
• The SAML Bearer Assertion Flow exchanges a SAML assertion for a Salesforce OAuth access token.
• It is intended for server-to-server or other noninteractive integrations where the application has already been authenticated by an identity provider.
• The connected app can be configured so administrators authorize or preauthorize applications that consume the APIs.

• B — JWT Bearer Flow: Uses a signed JWT, not a SAML assertion. It would be appropriate if the requirement specified JWT-based authentication.
• C — SAML Assertion Flow: This is not the standard Salesforce OAuth flow name; the relevant name is OAuth 2.0 SAML Bearer Assertion Flow.
• D — User-Agent Flow: Designed for interactive browser-based authorization, not a SAML-based server-to-server integration.

• D — Value analysis: Correct. It examines an existing product to identify ways of reducing its cost while still providing the required functions, quality and reliability.
• E — Kaizen costing: Correct. It involves making continuous, small improvements and cost reductions while the product is already in production.

• Functional analysis: It is not limited to minimizing the cost of the originally defined functions. It examines what functions are required and how they can be provided at the lowest overall cost.
• Value engineering: Usually applies to the design and development of a new product, rather than being a fundamental redesign of existing organisational processes. That description is closer to business process re-engineering.
• Target costing: The target cost is generally established during the product development stage, before production begins. It is not continuously reset while the product is in production.

• nomes de pessoas;
• organizações;
• locais;
• datas;
• números de processos;
• leis ou contratos.

• NER = identifica informações específicas e classificáveis.
• Sumarização = reduz um documento e apresenta seus pontos principais.

• Career enhancement — A mentor can provide guidance, advice, networking support and help the mentee identify career opportunities.
• Support with new challenges — A mentor can help the mentee think through unfamiliar situations and develop confidence and practical solutions.
• Personal development — Mentoring can improve skills such as communication, leadership, decision-making and self-awareness.

• Solving all problems — The mentor supports and guides the mentee but should not take responsibility for solving every problem.
• Discipline would be dispensed if they make a mistake — Mentoring does not remove normal accountability or workplace consequences. Mistakes should instead become learning opportunities.

• Scheduling overlapping working hours for critical collaboration
• Assigning independent tasks that can proceed asynchronously
• Adding clear handoffs between offices
• Documenting decisions, questions, and required inputs
• Building appropriate time into the schedule for responses

• 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.

• Customize Opportunity Contact Role

• Add the Opportunity Contact Role related list to the Opportunity page layout

• Campaign Member Role relates contacts or leads to campaigns, not opportunities.
• Campaign Member related list would expose campaign participation, not the stakeholders for the opportunity.
• Customize Campaign Role also concerns campaigns.

• User preferences change, so the model must learn from recent data.
• Keeping the existing data helps preserve knowledge of older patterns and provides a broader training distribution.
• Training only on new data can cause catastrophic forgetting and may overfit if the new dataset is small.
• The new data should not automatically be used as the test set, because it is needed for training and the model must still be evaluated on separate, representative data.

• Remote and global access: Employees can authenticate from anywhere, not just a specific location.
• Seamless login: The device can authenticate in the background, reducing repeated password or MFA prompts.
• Minimal equipment: It uses devices employees already have, rather than requiring separate hardware.

• Geotagging: Identifies location but is not a complete authentication method and can be spoofed.
• Smart cards: Can provide strong authentication, but require cards and often readers, adding equipment and user steps.
• Time-based logins: Restrict access by time but do not verify the user or device adequately.

• The application failing to start or becoming unhealthy
• An incorrect probe path, port, or protocol
• The application needing more startup time
• Dependency or configuration failures
• A liveness probe being too aggressive

bash 
kubectl describe pod <pod-name> 
kubectl logs <pod-name> --previous

• Scale to more replicas: may hide the issue but does not diagnose it.
• Decrease initialDelaySeconds: makes probes run earlier and could worsen startup failures.
• Drain and reboot the node: node metrics are normal, so rebooting is not justified as the first step.

• Create and manage approved IT services as products
• Control which users can deploy those products
• Apply governance, permissions, and constraints
• Use and manage infrastructure-as-code templates, such as AWS CloudFormation templates

• AWS Resource Explorer: Helps find and browse AWS resources; it does not govern IaC deployments.
• AWS Organizations: Manages accounts, consolidated billing, and organization-wide policies.
• AWS Systems Manager: Provides operational management, such as patching, automation, and parameter storage, but it is not primarily used to catalog and govern IaC-based service deployments.

• Monitoring: Detects activity but does not remove the underlying risk.
• Patching: Useful for systems that must remain operational, but these desktops are unused.
• Isolating: Can be a temporary containment measure, but permanently removing unnecessary systems is more appropriate.

• 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.

• CCTV system — Detective control: Cameras monitor and record activity, helping identify or investigate security incidents after—or while—they occur.
• Signs about being filmed — Deterrent control: The signs discourage people from attempting unauthorized or criminal activity because they know surveillance is present.

• Preventive: Stops an incident from occurring, such as a lock or access-control system.
• Corrective: Restores systems after an incident.
• Directive: Instructs people to behave in a certain way, such as a security policy.
• Compensating: Provides an alternative control when the preferred control cannot be implemented.

• Each Azure VM must have at least one network interface (NIC).
• A single NIC can have:

• Therefore, each of the five VMs needs only one NIC:

• Keep earlier prompt versions.
• Identify the last version that produced acceptable output.
• Restore it as a baseline.
• Make small, controlled changes—ideally one significant change at a time.
• Test the output after each change.

• A: Discards useful information about what worked.
• B: Large simultaneous changes make it difficult to identify the cause of problems.
• D: Accepts output that does not meet the requirements.

• A — Correct: Flow Builder is declarative and visual, so administrators and other non-developers can participate in designing and improving business processes without writing Apex.
• D — Correct: Flow Builder provides visual debugging tools, allowing an admin to test paths, inspect variable values, and identify where a specific business case fails.
• E — Correct: A flow’s visual structure documents the logic and makes later maintenance easier than reading and updating custom Apex code.

• B — Incorrect: Flow debugging does not attach a debugger to live customer sessions. The admin uses Flow Builder’s debug functionality with test inputs; this is not live-session debugging and should not be used to monitor real customer transactions.

• 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.

• 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.

• 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.

• Compare each server’s drive specifications and performance data.
• Identify the drive that is faulty, slow, or mismatched.
• Replace it with a drive that matches the required interface, capacity, and performance characteristics.
• Avoid selecting a slower drive, because storage performance can bottleneck the entire server.
• In a redundant array, use a compatible replacement so rebuilding does not create unnecessary performance degradation.

• Writing with Apache Spark, typically as Delta tables.
• Reading with Apache Spark for processing and analysis.
• Reading with T-SQL through the lakehouse’s automatically generated SQL analytics endpoint.
• Reading with KQL through Fabric’s query capabilities that can access lakehouse data.

• Eventhouse: Optimized for real-time and KQL workloads, but it is not the natural destination for Spark-based writes or general lakehouse storage.
• Datamart: Intended mainly for relational analytics and Power BI; it is not designed for Spark-based data ingestion.
• Warehouse: Primarily T-SQL-based and structured. It does not provide the same native Spark write experience or flexibility for semi-structured data.

• Microsoft Teams: Connects to Teams-related data, but it isn’t the primary connector for the Power Apps data tables.
• Personal Analytics: Provides personal productivity insights, not application data.
• SQL Server database: Only applies if the app explicitly uses SQL Server as its data source.
• Dataflows: Used for preparing and centralizing data, not as the normal direct connection to a Power Apps app.

• SOW
• SLA
• MOA
• MOU

json 
"Resource": "arn:aws:s3:::bucket-name/*"

• A: Revoking or changing an instance profile does not reliably invalidate temporary credentials that have already been issued and may still be valid.
• B: Changing the EC2 security group does not necessarily stop the attacker from using the stolen credentials from elsewhere.
• C: Amazon Macie helps discover sensitive data; Object Lock prevents modification/deletion, not reads or downloads.

• T?o m?t Systems Manager Automation runbook d? ch?y l?nh kh?i d?ng l?i ?ng d?ng trên các EC2 instance.
• T?o m?t Amazon EventBridge rule ph?n ?ng khi CloudWatch alarm chuy?n sang tr?ng thái ALARM.
• Ð?t Automation runbook làm target c?a EventBridge rule.

1. Log c?a ?ng d?ng du?c ghi vào CloudWatch Logs.
2. CloudWatch alarm phát hi?n nhi?u l?i liên quan d?n vi?c restart.
3. Alarm chuy?n sang tr?ng thái ALARM.
4. EventBridge nh?n s? ki?n thay d?i tr?ng thái này.
5. EventBridge g?i Systems Manager Automation runbook.
6. Runbook restart ?ng d?ng trên các EC2 instance, không restart c? instance.

• A: SNS không tr?c ti?p g?i Systems Manager Automation runbook nhu m?t target native.
• B: Lambda có th? th?c hi?n vi?c restart, nhung ph?i t? x? lý vi?c xác d?nh instance và ch?y l?nh t? xa.
• C: Lambda ch? làm trung gian d? g?i runbook, không c?n thi?t vì EventBridge có th? g?i runbook tr?c ti?p.

• Internal interface ? connected toward the corporate network/internal firewall
• External interface ? connected toward the Internet-facing firewall

1. A route for the internal corporate network through the internal-facing interface/firewall.
2. A default route (0.0.0.0/0) through the Internet-facing interface/firewall.

• A. IDS attack signatures: An IDS primarily detects suspicious network activity and usually does not provide dedicated file-upload malware scanning. It is generally detective rather than preventive.
• C. Load balancer: Distributes traffic among servers and improves availability; it does not inspect documents for malware.
• D. WAF: Helps block web-application attacks such as SQL injection, command injection, and XSS. Although a WAF may inspect upload requests, malware detection in PDF files is more directly addressed by antivirus or content-scanning functionality.

• CSIRT: A team that responds to and manages security incidents.
• CVSS: A scoring system for rating the severity of vulnerabilities.
• SOAR: A technology platform for automating and coordinating security operations and incident response.

• Drops packets when traffic exceeds a certain bitrate: This describes traffic policing, not shaping.
• Fragments large packets and queues them: Shaping does not fragment packets as its primary congestion-control action.
• Drops packets randomly from lower-priority queues: Packet dropping may occur if a shaping queue becomes full, but it is not the defining behavior.

• Shaping: buffers and delays excess traffic.
• Policing: drops or marks traffic that exceeds the configured rate.