Test Prep ACT Test Exam Prep
American College Testing: English, Math, Reading, Science, Writing (Page 94 )

• E — Exploit Code Maturity: How readily exploit code exists or is usable.
• U — Unproven: No exploit code is known, or available exploitability has not been established.

• S:C — Scope is changed.
• RC:R — Remediation level is official remediation.
• AV:N — Attack vector is network.
• AC:L — Attack complexity is low.

• Elasticity: Automatically increases or decreases resources based on demand.
• Scalability: The ability to increase capacity to handle more workload.
• Low latency: Minimizes network response time; it does not provide protection from failures.

• Vulnerability scanner: Identifies technical weaknesses in systems, applications, or devices.
• Penetration test: Simulates attacks to determine whether vulnerabilities can be exploited.
• SCAP (Security Content Automation Protocol): Uses standardized formats and protocols for automated vulnerability, configuration, and compliance checks.
• Illumination tool: Maps and analyzes broader supply-chain exposure and third-party risk.

1. Create Workspace2 with Lakehouse2 — F
2. Migrate the Employee table to Lakehouse2 — E
3. Assign the data engineer the Contributor role for Workspace1 — D

Why

• Sharing Lakehouse1 would not provide the required isolation.
• Viewer access would not allow writing to Customer.
• Giving Contributor access to Workspace2 would expose the sensitive table.

• 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

1. Yes — Microsoft Foundry provides a secure, governed environment for building and managing generative AI applications and agents. It brings together models, data, tools, evaluation, deployment, and monitoring.

1. Yes — Foundry is designed for enterprise workloads and supports scaling AI applications as usage grows. Scalability comes from the underlying Azure services and managed infrastructure.

1. Yes — Foundry can be used in solutions involving image recognition and computer vision. Applications can use vision-capable models and Azure AI vision capabilities for tasks such as image analysis, object detection, and OCR.

• 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.

• B — AWS DataSync: Transfers data between storage systems. It is not a marketplace or integration service for third-party datasets.
• C — Kinesis Data Streams and CodeCommit: CodeCommit stores source code, not third-party analytical datasets. Kinesis is for streaming data, not retrieving datasets from repositories.
• D — Kinesis Data Streams and ECR: Amazon ECR stores container images, not third-party datasets.

• Accept: Acknowledges the risk without taking additional action.
• Transfer: Shifts the risk or financial impact to another party, such as an insurer.
• Mitigate: Reduces the likelihood or impact through controls like patching, MFA, or segmentation.
• Avoid: Eliminates the activity that creates the risk.

• Data Cloud combines data from multiple systems.
• It resolves and organizes that data into a unified customer view.
• That unified view enables downstream actions such as personalized campaigns, segmentation, analytics, and activation.

• A = core platform value
• D = a resulting business use case or benefit

• Use the grant-date fair value: $30
• Do not remeasure to the year-end value of $45
• Update the estimate of employees expected to vest

• Dr Share-based payment expense $450,000
• Cr Equity/share option reserve $450,000

1. Write a structured log entry to CloudWatch Logs containing:

1. Create a CloudWatch Logs metric filter that:

text 
ApiRequests 
Operation=GetOrders 
ResponseCode=500 
Version=2.4.0

• B is incorrect: CloudWatch Logs Insights queries analyze logs but do not directly publish metrics in this manner.
• C is incorrect: ALB access logs do not contain arbitrary Lambda response metadata as custom fields.
• D is incorrect: X-Ray traces and insights are not the appropriate mechanism for creating these custom, dimensioned application metrics.

• Something you know: Password or PIN
• Something you have: Security token, smart card, or phone receiving an authentication code
• Something you are: Fingerprint or facial recognition
• Somewhere you are: Location-based verification

• Microsoft Entra Connect synchronization
• AD FS
• Active Directory Domain Services

• SendGrid with advanced reporting: Used for application email delivery, not directory synchronization monitoring.
• Action group: Used with Azure Monitor alerts, but it is not the native monitoring solution for Microsoft Entra Connect synchronization health.
• Azure Network Watcher: Monitors Azure networking, not identity synchronization.

• Required response time
• Resolution time
• Availability or uptime
• Support hours
• Performance targets
• Consequences if targets are missed

• SOW (Statement of Work): Defines the project’s scope, tasks, deliverables, and schedule.
• MOA (Memorandum of Agreement): Describes the responsibilities and commitments between parties.
• MOU (Memorandum of Understanding): Documents a general mutual understanding or intent, often before a formal agreement.

• 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.

text 
roles/owner

bash 
gcloud projects get-iam-policy PROJECT_ID

yaml 
bindings: 

members:

  - user:admin@example.com 
  role: roles/owner

• Project-wide SSH keys control VM SSH access, not IAM ownership.
• Identity-Aware Proxy permissions apply to IAP-protected applications, not the complete project IAM policy.
• Audit Logs record activity; they do not directly show the current set of role assignments.

• Existing Eventhouse data: remains available only in the KQL database.
• Data ingested after enabling the feature: is copied to OneLake and can be accessed through OneLake file explorer.
• New databases: are irrelevant; the setting applies to the selected KQL database.

• terraform apply
• terraform destroy

• Measurement bias: Errors in how features or labels are measured, such as unreliable camera data or inconsistent labeling.
• Observer bias: A human evaluator’s subjective judgment influencing observations or labels.
• Confirmation bias: Favoring information that supports an existing belief.

• VPC: A logically isolated virtual network within a public cloud.
• NFV: Replaces dedicated network appliances with virtualized network functions.
• SaaS: Software delivered over the internet as a service. This application could be delivered as SaaS, but the key detail in the question is that it serves multiple user groups or tenants.

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

• Which host first communicated with the suspected malicious source
• Whether that host initiated suspicious traffic or merely received it
• Which other hosts show evidence of infection, such as connections from the originating host
• Which hosts have no related suspicious activity and are therefore clean

• An outbound connection to a suspicious external address
• A downloaded or executed malicious file
• Later lateral connections from that host to other internal systems
• Firewall logs showing the direction and timing of the traffic

• 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.

• A business-hours greeting
• An after-hours greeting
• A holiday greeting

• Weekday evenings/nights
• Saturdays and Sundays

1. Auto attendant 1

1. Auto attendant 2

• End-user training: Important for recognizing phishing, but it depends on users consistently making the right decision.
• Installing antivirus rule updates: Helps detect malware, but may not stop phishing links or newly emerging threats.
• Disabling HTML in email content: Can reduce some email-based risks, but it is less practical and comprehensive than filtering web destinations.

• Suspended an operator who refused to use unsafe equipment.
• Paid another operator a bonus for using it.
• Prioritised short-term productivity over employee safety.

• A — Management is not particularly concerned with managing risks.

• B — Employees will perceive that management does not care about health and safety risks.

• C — The factory’s safety record is likely to be poor.

• FUNCTION (D)
• SECURITY POLICY (E)

1. Create an inline table-valued filter predicate function. It defines which rows a user may see, often by comparing a table column with the current user identity.
2. Create a security policy that applies that function to Table1. The policy filters query results automatically.

1. System prompts can authorize users — No

1. System prompts can reduce TPM consumption — Yes, with qualification

1. System prompts guide generative AI behavior — Yes

• Separate development, test, and production environments
• Building components in unmanaged solutions during development
• Organizing solutions by functional area
• Exporting and deploying managed solutions to production
• Validating changes before production deployment
• Preventing developers from overwriting one another’s production changes

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

• GPS coordinates
• Date and time the image was taken
• Camera or device information
• Image dimensions and software used

• Log data: Records generated by systems or applications, not normally embedded in image files.
• Encrypted data: Information intentionally encoded to prevent unauthorized access.
• Sensitive data: A broad classification, not the specific type of information being extracted.

• MTTC: Time required to contain or limit the incident’s impact — best match.
• MTTR: Time required to remediate or recover from the incident; this may include eradication, restoration, and cleanup, which goes beyond merely stopping spread.
• MTTD: How long it takes to discover the incident.
• MTBF: Average time between failures, generally a reliability metric.

• IDS attack signatures: Primarily detects suspicious network activity; it is not the best control for scanning uploaded documents.
• Load balancer: Distributes traffic and can improve availability, but does not inspect files for malware.
• WAF: Helps block web attacks such as command injection and SQL injection, but it is not primarily a malware-scanning solution.

• Start the print spooler service: The Print Spooler manages print jobs and allows Windows to communicate with printers. Starting it restores printing immediately.
• Set the Print Spooler to Automatic: This makes Windows start the service during each boot, preventing the user from needing help every morning.

• No dependencies: Dependencies help determine which services start first; changing this does not solve the recurring issue.
• Recovery: Take no action: This would prevent Windows from responding appropriately if the service fails.
• Printer extensions and notifications: Not required for basic printing.
• Log on as the user: The spooler normally runs under a system service account; using the user’s account is unnecessary and potentially less secure.

• 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

• 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 single summarized value, such as total annual sales
• Progress toward a defined target or goal
• Whether performance is below, near, or above the target

• Table: Displays detailed records or summarized rows, but not goal progress visually.
• Stacked Bar: Compares multiple categories and their parts.
• Donut: Shows how a total is divided among categories, not progress toward a target.

1. Open the Offenses tab

1. Open the offense details

1. Review contributing events

1. Analyze the attack chain

1. Investigate the assets

1. Validate and respond