NABP FPGEE Exam Prep
Foreign Pharmacy Graduate Equivalency Examination (Page 34 )

• In Microsoft Teams Phone System, a single Auto Attendant can handle multiple time-based greetings by using separate schedules for office hours, after hours, weekends, and holidays.
• You can configure one Auto Attendant with four distinct greetings mapped to:

• Open the Teams admin center > Voice > Auto attendants, edit or create an Auto Attendant.
• Under Time-based routing, define four rules:

• Create a Holiday schedule with the relevant dates (e.g., Easter, Christmas) and assign the Holiday greeting.
• Save the configuration.

• If your environment cannot separate weekend from after-hours within a single Auto Attendant, you might technically need more than one Auto Attendant.

• A separate scope using a /32 subnet: A /32 identifies one host, but it is not a practical DHCP scope for this purpose.
• A static IP within the DHCP range: This can cause an IP address conflict if DHCP assigns that address to another device.
• SLAAC: Used for automatic IPv6 address configuration, not for assigning a predictable IPv4 address through DHCP.

• Pull: Downloads changes from the central repository and updates the local repository.
• Push: Sends local commits to the central repository.
• Commit: Records local changes in the repository’s history.
• Merge: Combines changes from different branches.
• Clone: Creates a new local copy of a repository.

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

• Program and project management
• Risk management
• Cloud financial management
• Data governance
• Portfolio management

• Sustainability
• Performance efficiency
• Reliability

• Batch processing, such as scheduled data transformations
• Streaming processing, such as continuously ingesting events

• Manipulating data with multiple languages: Mainly a benefit of Databricks and Spark.
• Real-time notebook collaboration: A workspace/notebook feature.
• Alerts for query failures: A Databricks SQL or job-monitoring feature.
• Distributing complex operations: Primarily a Spark compute capability.

• Filtering or reshaping columns
• Joining with an analysis-specific dataset
• Creating calculated fields
• Applying business logic for a particular project

• Data blending: Combining different data sources, not specifically the final processing step.
• Data enhancement: A broad term for enriching data.
• Data testing: Checking data quality or pipeline correctness.
• Last-mile ETL: The precise term for additional transformations performed after the main pipeline, often directly before analytics or reporting.

• 6-phone boxes: 6x
• 8-phone boxes: 8x

• A laptop
• Software access
• A password reset
• A mobile phone

• They run behind the scenes — applies more to automation or workflows.
• They are optional — catalog items are fundamental to a request-based catalog.
• They provide options — variables and variable options collect the requester’s choices for an item; they are not the catalog item itself.

• 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.

• Exploitability
• Attack complexity
• Required privileges
• User interaction
• Impact on confidentiality, integrity, and availability

• CVE: Identifies and tracks a specific vulnerability; it does not primarily measure severity.
• OSINT: Open-source intelligence gathered from publicly available sources.
• SOAR: Security orchestration, automation, and response technology used to automate security workflows.

• Regional leads — Customer Community Plus

• Capacity planners — External Identity

• Customer Community does not provide the enhanced reporting/dashboard access required by regional leads.
• An Identity license is generally for internal Salesforce identity use and does not fit the external capacity-planning users as well as External Identity.

• A. A virtual network gateway
• D. A virtual network

• Azure virtual machines must be connected to an Azure virtual network (VNet). The VNet provides private IP addressing and network connectivity for the VMs.
• A virtual network gateway enables communication between the Azure VNet and the on-premises network, typically through a site-to-site VPN or ExpressRoute connection.

• Load balancer: Distributes traffic among Azure VMs; it does not connect Azure to an on-premises network.
• Application gateway: Provides web-application load balancing and Layer 7 routing; it is not the normal resource for on-premises connectivity.
• Gateway subnet: A gateway subnet is required inside the VNet when deploying a virtual network gateway, but the question’s intended two primary resources are the VNet and virtual network gateway.

• C — Schema.describeSObjects(new String[]{myObject})[0].fields.getMap()

• D — Schema.getGlobalDescribe().get(myObject).getDescribe().fields.getMap()

• A: sObjectType is not used this way, and fieldSet() is not the appropriate method for retrieving all fields.
• B: mySObject.myObject is not valid dynamic Apex syntax.

• AWS IoT Greengrass supports local ML inference using models trained in Amazon SageMaker.
• The model and inference code run on the factory’s local server, so processing continues even when internet connectivity is unavailable.
• A Greengrass component can:

• This provides low latency and avoids sending images to AWS for every inference.

• A: Uses AWS-hosted services and therefore depends on internet connectivity. It would fail during an outage.
• C: Snowball Edge is unnecessary for this use case and adds deployment complexity. A local Greengrass server is the intended edge-inference solution.
• D: Amazon Monitron is designed for monitoring industrial equipment health, not analyzing camera images with a custom SageMaker model.

• Create Power Query parameters for the Azure SQL server/database—or at least the database name.
• Use the parameter in the PBIX connection.
• Publish the same PBIX separately to the relevant development, test, and production workspaces.
• Set each dataset’s parameter value in the Power BI service so it points to the corresponding Azure SQL database.

• Development dataset ? DevelopmentDB
• Test dataset ? TestDB
• Production dataset ? ProductionDB

• The same PBIX is used for every lifecycle stage.
• Each stage consumes data from its matching database.
• Deployment pipelines are not required.
• Azure SQL is a cloud source, so gateway administration is generally avoided.
• Only the parameter value differs between published datasets, reducing maintenance.

• View/inspect configuration: Possible.
• Modify the installed application or its S-Controls: Typically restricted in a test-drive org.

Why

cmd 
PowerShell.exe -ExecutionPolicy Bypass -File C:\Scripts\ConfigureVM.ps1

Why the other options are less suitable

• GPO logon script: Runs only when a user logs on and may not run during initial VM provisioning.
• GPO startup script: Can work, but requires the VM to be joined to the domain and the policy to be applied. It is also normally evaluated at each startup.
• Place scripts in a VHD: Merely placing scripts on a disk does not cause them to execute.

• Actual filename: Document.xml.txt
• Explorer displays: Document.xml
• File type: Text Document (.txt)

text 
Document.txt ? Document.xml

• Cutover: The old system is replaced immediately. It may be fast, but any failure can cause significant disruption.
• Phased: The system is introduced in stages, which reduces implementation risk but may prolong the transition.
• Pilot: The system is first deployed to a limited group or location. This is useful for testing but does not necessarily minimize downtime for the entire organization.

• Windows 10 VMs
• Windows Server 2012 or later VMs
• Running VMs—they do not need to be shut down
• Debian 8.2+ VMs, assuming the specific version is supported
• Stopped or deallocated VMs

• Low usage during the first three weeks
• Very high usage during the last week

• High availability: Keeps services running despite failures.
• High latency: Means slow response times; it is not a benefit.
• Load balancing: Distributes traffic across resources but does not specifically address scaling resources up and down.

• Detect risky sign-ins from unfamiliar or unidentified IP addresses
• Evaluate user or sign-in risk
• Automatically require a password change because of a risky sign-in

• User risk policy: Can require a user to securely change their password when the account may be compromised.
• Sign-in risk policy: Can require MFA or block access based on the risk of a particular sign-in.

• Disable default accounts: Default or unused accounts may have predictable names or credentials and can be abused by attackers. They should be disabled, removed, or secured with strong credentials where necessary.
• Remove unnecessary services: Every running service can contain vulnerabilities or provide an entry point. Removing services the server does not need reduces exposure.

• Add the server to the asset inventory: Important for asset management, but it does not directly harden the system.
• Document default passwords: Default passwords should be changed or disabled—not recorded for continued use.
• Send logs to the SIEM: Supports monitoring and detection, rather than directly reducing the attack surface.
• Join the corporate domain: A deployment/configuration decision, not inherently a hardening measure.

• Honeypots: Decoy systems or services designed to attract attackers. Legitimate users should not access them, so activity on a honeypot is a strong indicator of unauthorized activity. Security teams can also observe attacker behavior.

• DNS sinkhole: Redirects requests for known malicious domains to a controlled internal server or blocks them. If an infected workstation attempts to contact command-and-control infrastructure, the sinkhole records the request and helps identify the compromised device.

• Tokenization: Protects sensitive data by replacing it with tokens.
• CI/CD: Automates software development and deployment.
• Threat modeling: Identifies potential risks before or during system design.
• Data obfuscation: Makes data harder to understand, but does not detect intruders.

• VM CPU, memory, disk, and network usage
• Storage transactions and latency
• App Service response time
• Database DTU or CPU utilization

• Azure Traffic Analytics analyzes network traffic flows and security patterns; it is not the general infrastructure metrics tool.
• Azure Activity Log records subscription-level control-plane operations, such as resource creation, deletion, or configuration changes.
• Azure Advisor provides recommendations about cost, security, reliability, performance, and operational excellence, but it is not the primary tool for investigating live metrics.

1. Define Power Platform DLP policies

1. Add custom prompt modifications

1. Conduct adversarial testing

• Matching hashes: The software is unchanged.
• Different hashes: The software was modified or corrupted.

• Encryption: Protects confidentiality.
• Baselines: Define approved configurations or normal performance.
• Tokenization: Replaces sensitive data with tokens.

• The inference container image or built-in algorithm
• The location of model artifacts in Amazon S3
• IAM permissions through the execution role
• Optional environment variables and networking settings

1. Create AWS::SageMaker::Model
2. Create an AWS::SageMaker::EndpointConfig that references the model
3. Create AWS::SageMaker::Endpoint that references the endpoint configuration

• AWS::SageMaker::Endpoint hosts a model but does not define the model itself.
• AWS::SageMaker::NotebookInstance provides a development environment.
• AWS::SageMaker::Pipeline orchestrates ML workflow steps.

• staff use the new system;
• transactions and reports are checked;
• problems are corrected;
• feedback is collected.

• Phased changeover: different parts or functions are introduced in stages, often according to a planned sequence.
• Pilot changeover: one representative department, branch or group acts as a test site before wider implementation.
• Parallel changeover: old and new systems operate at the same time.
• Direct changeover: the old system is stopped and the new one starts immediately.

1. Remote access to a Windows PC
2. Full chat and optional game-console functions

• Windows Remote Desktop (RDP) uses TCP 3389. The router needs a port-forwarding rule that sends incoming traffic on that port to the Windows PC’s internal IP address.
• The game console should connect through the wireless AP. Using WPA2-PSK provides the required wireless security while allowing normal online, chat, and optional services.
• The firewall should separate the internal network from the screened subnet. Its screened-subnet-side address is shown as 10.100.0.1.
• Devices must be placed in the correct network locations: the PC behind the router, the console on the wireless AP, and the firewall between the appropriate network segments.

• prompt: the input or instruction given to the model
• completion: the desired response

json 
{"prompt":"Summarize this customer complaint.","completion":"The customer reports a delayed shipment."}

• B: A .txt file containing CSV-style lines is not automatically a valid fine-tuning dataset. The required structure and format must match the selected Bedrock model.
• C: Provisioned Throughput provides dedicated inference capacity; it does not fine-tune a model.
• D: Journals and textbooks might support domain adaptation or pretraining, but they do not provide the prompt-to-answer examples typically required for supervised fine-tuning.

• A larger question bank with verified answers
• Explanations tied to ISACA exam domains
• Current content aligned with the exam blueprint
• Useful review or progress features

• A. Extranet
• B. VLAN
• C. Intranet
• D. VPN

• Authenticating websites with TLS/HTTPS
• Enabling certificate-based VPN authentication
• Signing or encrypting email
• Supporting device or user authentication

• VLAN tagging: Uses network-frame tags, such as 802.1Q; it does not rely on X.509 certificates.
• LDAP: May integrate with PKI in some environments, but X.509 certificates are not most commonly associated with LDAP itself.
• MFA: Certificates can be one authentication factor, but X.509 is fundamentally a PKI technology.

• The VPN gateway is in VirtualNetworkA.
• Gateway transit is configured on the peering associated with the VNet that contains the gateway—here, VirtualNetworkA.
• A peered VNet may use the remote gateway through the appropriate peering option, but this does not automatically update an existing P2S client’s routes.
• The Windows 10 workstation’s P2S VPN client package was created before VirtualNetworkB was peered, so it may not have a route for VirtualNetworkB.

• Identity provider (IdP): Authenticates users and asserts their identity.
• Service provider (SP): The application being accessed—in this case, Salesforce/the employee portal.
• Identity store: A directory that stores user identities, such as Active Directory.
• Authentication store: A system used to validate credentials.

• Recovery procedures actually work
• Recovery time and recovery point objectives can be met
• Staff understand their responsibilities
• Contact details, dependencies, and technical configurations are accurate
• Gaps or conflicts exist in the plan

• Not version-controlled: Creates a risk of using an outdated copy, but the plan might still be effective.
• Contact information needs updating: This is a specific weakness that can be corrected, often identified during testing.
• Not approved by senior management: Approval is important for authority and resource commitment, but an untested plan presents the most direct risk that recovery will fail when needed.

• Workspace: Stores notebooks, libraries, jobs, and other project resources.
• Notebooks: Interactive documents containing code, queries, visualizations, and explanations. Common languages include Python, SQL, Scala, and R.
• Clusters: Groups of virtual machines that run Spark workloads. Clusters can be started, stopped, and automatically scaled.
• Data access: Databricks can read from services such as Azure Data Lake Storage, Azure Blob Storage, and Azure SQL.
• Jobs: Automate notebooks or data-processing workflows on a schedule or in response to events.

• Review the risk register.
• Use the previously defined risk response plan, such as assigning a backup resource, reprioritizing work, or using another qualified engineer.
• Implement the response promptly because the task is on the critical path.

• B: Revising the schedule without first applying the planned response may be premature.
• C: Excluding a required task could compromise the project’s objectives and requirements.
• D: Updating the risk and lessons learned records is appropriate, but it should happen after managing the immediate issue, not as the next primary action.

• B: Trigger.Once + scheduled job

• C: 10-minute processing-time trigger

kusto 
Event 
| summarize make_set(EventID) by Computer

• summarize groups records.
• by Computer creates one result row per virtual machine.
• make_set(EventID) returns an array containing unique event IDs for that computer.

kusto 
| summarize make_list(EventID) by Computer

• A — Custom Button: A button can launch a flow, typically by passing the flow’s URL and record context.
• D — From a Process: A Process can invoke a flow using an Apex action configured for flow invocation. However, this is automation-triggered, not directly accessed by a user.
• B — Quick Action: In current Salesforce functionality, a Flow Quick Action can launch a screen flow directly from a record page. This makes B a valid modern method.
• C — Visual Workflow Launcher: This is not generally listed as a standard access method in current Salesforce terminology; Salesforce commonly uses the Flow component on Lightning pages instead.

1. Determine which host was infected first—the originating host.
2. Decide whether each other host is clean or infected based on communication and event logs.

• Compare timestamps and find the earliest suspicious event.
• Look for the host that initiated the malicious connection, file transfer, exploit, or command—not merely a host that later received traffic.
• Follow the timeline through the firewall:

• A host is likely infected if it both receives suspicious activity and then initiates related malicious traffic.
• A host may be clean if the firewall blocked the connection and there is no evidence of execution or follow-on communication.

• Replace the keyboard: More expensive and unnecessary before simpler causes are ruled out.
• Replace the key switch: Laptop keyboards generally aren’t designed for individual switch replacement.
• Replace the circuit board: Excessive for a single nonworking key and not a cost-effective first step.

• 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.

1. Ingestion Lambda receives requests from API Gateway and places messages in an SQS queue.
2. Processing Lambda is triggered by the queue and loads records into Aurora PostgreSQL.

• Decoupling: API request handling is separated from database loading.
• Buffering: SQS absorbs bursts when incoming traffic exceeds the database’s processing capacity.
• Controlled concurrency: Configure the SQS-triggered Lambda’s concurrency and batch size to avoid overwhelming Aurora or exhausting database connections.
• Automatic retries: Messages that fail processing can return to the queue and be retried. A dead-letter queue can capture persistent failures.
• Lower operational effort: The solution remains serverless and avoids managing EC2 servers or Kubernetes clusters.

• A: EC2 and Tomcat add server-management overhead.
• B: DynamoDB/DAX would require changing the database model and does not solve the Aurora ingestion design.
• C: SNS is useful for notifications and fanout, but SQS is better for durable buffering and backpressure in this producer-consumer workflow.

• A — Increasing the minimum password length to 14 characters: Longer passwords have more possible combinations, making them harder to guess or crack through brute force.
• F — Requiring at least one special character: This increases password complexity and expands the possible character combinations.

• MD5 to SHA-512: Improves password-hash resistance, but does not stop users from choosing easy-to-guess passwords. Also, modern systems should use a password-specific hashing algorithm such as bcrypt, scrypt, or Argon2.
• Increasing maximum age to 120 days: Leaves weak passwords in use longer.
• Reducing minimum length to 10: Makes guessing easier.
• Reducing minimum age to zero: Allows immediate password changes but does not make the password itself harder to guess.

• Assign a Teams license to a user — Licenses are assigned through the Microsoft 365 admin center, Microsoft Entra ID, or PowerShell.
• Deploy the Teams client — The Teams admin center manages Teams policies and apps, but it is not the primary tool for deploying the desktop client.
• Prevent users from creating teams — Team creation depends on controlling the creation of Microsoft 365 groups, generally configured through Microsoft Entra ID and PowerShell rather than directly in the Teams admin center.

• A single Construction Job can use multiple Machinery records.
• A single Machinery record may be used on multiple Construction Jobs.
• This is a many-to-many relationship, which Salesforce models using a junction object.
• The junction object can contain fields such as:

• Master-detail to Machinery
• Master-detail to Construction Job
• Date and cost fields

• 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.