PEOPLECERT MOR Exam Actual Questions
Management of Risk Foundation (MOR) (Page 2 )

• MOR health checks assess how risk management is implemented and embedded, and identify improvements and ROI opportunities.
• They’re about process health, governance, and capability, not about listing or snapshotting the actual risks themselves.

• Identify areas for improvement — yes; a health check should highlight gaps.
• Help gain maximum value from investment in risk management — yes; it supports value realization.
• Provide a snapshot of the current status of identified risks — not the primary goal; risk status is typically captured in risk registers/reports, not the health of the risk management process.
• Review how well risk management practices have been embedded — yes; a core objective.

• Answer: C (192.168.0.1-192.168.0.99, 192.168.0.101-192.168.0.254)

• Why: The interface is 192.168.0.100/24, so DHCP pools must be within the 192.168.0.0/24 subnet and cannot include the router’s IP (192.168.0.100), the network address (192.168.0.0), or the broadcast (192.168.0.255). Option C provides two ranges that exclude those addresses.

• Why the other options are invalid:

• Key concept: ALE = SLE × ARO

• SLE = $15,000
• Occurrences: 2 in 3 years ? ARO = 2/3 per year ˜ 0.6667

• ALE = $15,000 × 0.6667 ˜ $10,000 per year

• ALE is the expected annual loss, so it averages the losses over each year.
• This helps prioritize risk treatments by comparing ALE to the cost of controls.

• Short answer: The correct term is Anti-forensics (option D), not “Felony.”

• Why: The actions described—hiding files in slack space, changing file headers, placing suspicious files in executables, and altering metadata—are classic anti-forensics techniques. They’re used to hinder or obscure forensic analysis, not to label the act as a legal category.

• About the options:

• Note: The provided answer key lists B (Felony), which is misleading for this exam objective. The exam typically tests knowledge of forensics concepts, where anti-forensics is the correct category.

• Statement 1: Admin1 must approve requests for the Agent ID Developer role — No

• Statement 2: Admin2 can approve requests for the AI Administrator role — No

• Statement 3: Admin3 can assign User1 a two-day active assignment for the Agent ID Developer role — Yes

• It is a sequence of distinct phases that flow one after another (requirements ? design ? implementation ? verification ? maintenance).
• Requirements are defined up front and changes are costly; the project often requires formal sign-off before moving to the next phase.
• It emphasizes heavy documentation and minimal iteration after a phase is completed.

• Correct answer: UBA (A)

• Why: UBA (User Behavior Analytics) analyzes user activity, builds baselines of normal access patterns, and flags deviations (e.g., accessing sensitive projects not related to the user’s role). This insider-threat detection is exactly what the scenario requires.

• Why not the others:

• Domeny tworza restrykcjony razem z typem. Wykorzystujesz je w kolumnach, aby miec spójna walidacje danych w wielu miejscach.
• To nie jest pelny niestandardowy typ z wlasnym input/output funkcjonowaniem. Aby stworzyc typ z wlasnym I/O i operacjami, uzylbys CREATE TYPE (lub rozszerzen w C). Domeny to uproszczona forma ograniczen na istniejacy typ.

• CREATE DOMAIN positive_int AS integer NOT NULL CHECK (VALUE > 0);
• CREATE TABLE orders (id serial, qty positive_int);

• latwo wymuszaja wspólne reguly dla wielu kolumn/naglówków,
• zmiana regul w jednym miejscu (domena) automatycznie wplywa na wszystkie kolumny ja uzywajace.

• Control plane: Managed by Databricks and contains the Databricks web application, workspace services, cluster management, and job orchestration.
• Data plane: Runs in your cloud account and contains compute resources such as:

• Worker node: Data plane compute.
• Driver node: Data plane compute.
• JDBC data source: Usually an external data system.
• Databricks Filesystem (DBFS): Provides storage access and is backed by cloud object storage rather than being entirely part of the control plane.

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

text 
Document.txt ? Document.xml

• Why: After listing all resources, you need to assign who is responsible for each task. A RACI chart (Responsible, Accountable, Consulted, Informed) documents these roles for every activity.

• What RACI means:

• Why the others aren’t suitable here:

• Quick example: Task "Create user authentication module" — Responsible: Developer, Accountable: Tech Lead, Consulted: Security Architect, Informed: PM.

• Why: A multi-modal embedding model maps both text and images into a shared vector space, enabling cross-modal similarity and retrieval (e.g., find images matching a text query or text matching an image).

• What it does: Enables efficient similarity search using a vector database or nearest-neighbor indexing.

• Why the other options aren’t suitable:

• Quick implementation note: you’d typically pair the embeddings with a vector store (e.g., ANN index) and possibly fine-tune on domain data for better alignment across modalities.

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.

• 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.

• What it’s asking: In an ARM template, for an Availability Set, what value should you set for the property platformFaultDomainCount to maximize availability during fabric failures or maintenance?

• Key concept: Fault domains are groups of hardware that can fail independently. By spreading VMs across more fault domains, you reduce the risk that a single hardware fault takes out many VMs.

• Important detail: For managed Availability Sets, the number of fault domains you can use varies by region — typically 2 or 3.

• What to choose: Set platformFaultDomainCount to the maximum supported in your region. In most regions this is 3, which gives you the best resilience. If your region only supports 2, you’d use 2.

• Answer: 3 (Max Value). In ARM template syntax, this would appear as "platformFaultDomainCount": 3 (within the Availability Set resource).

• Correct answer: A) Hashing

• Why: Hashing produces a fixed-size digest of the software. If the final build is altered in any way, the hash will change, enabling you to detect tampering by comparing the stored digest with a freshly computed one.

• Why not the others:

• On-Demand: You pay by the hour with no long-term commitment. This is ideal for workloads that are infrequent or have highly variable usage. Since this job runs only 24 hours once per year, you pay only for those 24 hours, with no wasted upfront or long-term payments.
• Reserved Instances: Require a 1- or 3-year commitment and pay for capacity whether you use it or not. For a workload that runs just 24 hours a year, you’d be paying for most of the term unused, making it far less cost-efficient.
• Spot Instances: Can be interrupted whenever AWS needs capacity, which is unacceptable for an uninterruptible workload.
• Dedicated Instances: Generally more expensive and aimed at licensing or compliance requirements; not needed for this use case.

• Coordinate and manage the assessment team
• Assign assessment activities
• Direct interviews, examinations, and tests
• Ensure the assessment follows the CAP

• Answer key: Lead Assessor — likely wrong
• CAP-based answer: C3PAO

• Enable the owner dimension
• Allocate a standard costing inventory model

• Owner dimension (B): This dimension Lets you track who owns the stock. By enabling the owner dimension, items can be configured as either company owned or consignment (vendor-owned) when you create or edit items. Without enabling this dimension, you can’t designate ownership per item.
• Standard costing inventory model (D): Selecting a costing model determines how item costs are calculated and posted. Standard costing provides a fixed cost per unit, which is commonly used for agreed pricing scenarios like consignment arrangements for easier accounting and valuation. It helps ensure consistent cost treatment across transactions for items that can be owned by the company or on consignment.

• Batch dimension (A): Batch tracking is unrelated to ownership and isn’t required to support company-owned vs. consignment configurations.
• Moving average costing (C): This is an alternative costing method. It’s not required to enable ownership config, and the exam’s expected choice focuses on standard costing for this scenario.

• The Scrum Guide specifies four required events: Sprint Planning, Daily Scrum, Sprint Review, and Sprint Retrospective. These events enable transparency, inspection, and adaptation.
• The other items listed are not mandatory under Scrum:

• The diagnostic output shows that the protocol is TCP (proto=6) and the session state is 2.
• In FortiGate’s session output, state=2 maps to SYN_SENT. This means the TCP SYN has been sent but the handshake hasn’t completed yet.
• Therefore, the session is not ESTABLISHED, FIN/ACK-related states, or other states; it’s in SYN_SENT.

• In Spark, an executor is a JVM that can have multiple cores. Each task can consume some CPUs (spark.task.cpus).
• The executor’s resources are effectively partitioned into slots, where each slot can run one task. The number of slots per executor is roughly spark.executor.cores / spark.task.cpus.
• Therefore, multiple tasks can run in parallel within the same executor (via these slots). So the statement “Tasks run in parallel via slots” is correct.

• A: A slot is not another name for an executor; a slot is a unit inside an executor.
• B: There’s no requirement that there must be fewer executors than tasks; tasks can be scheduled over time across the available slots.
• C: An executor can run more than one core; it’s not limited to a single core.
• D: There isn’t a rule that there must be more slots than tasks; tasks can wait if slots aren’t available, and you can have equal or even fewer slots than concurrent tasks.

• Answer: Data enhancement.

• Why: Augmenting gold-layer tables with an additional dataset is enriching or enhancing the existing data to support the ad-hoc analysis. The other options don’t capture the idea of adding external data to improve the dataset (ad-hoc improvements = tweaks, last-mile dashboarding = building dashboards, last-mile ETL = ETL steps at the end of the pipeline). In practice, this is often also called data enrichment.

• What the scenario asks: You have a Power BI semantic model that pulls from multiple sources (Azure SQL Database and SharePoint Online), must support a single dataset for three reports, keep data current (daily refresh by 7 AM PT), and apply RLS to limit sales data by region. You need to choose a storage mode for the tables.

• Correct choice: Dual storage mode.

• Why Dual fits:

• Key concepts:

• Transact-SQL (T-SQL) is used to query relational data in Azure SQL Database, not telemetry data from monitoring services.
• To analyze monitoring data from Azure SQL Database Intelligent Insights and Azure Application Insights, you should use the Kusto Query Language (KQL) in Azure Monitor Logs/Log Analytics (and in Application Insights Analytics).
• Using KQL lets you query telemetry tables such as requests, dependencies, pageViews, and customEvents (from App Insights) and any integrated monitoring data you’ve ingested into Log Analytics.

• Ensure Intelligent Insights and Application Insights data is available in a Log Analytics workspace or accessible via Application Insights Analytics.
• Go to Azure Monitor > Logs (or Application Insights > Analytics) and write KQL queries, e.g.:

• What wipe does: A factory reset that erases all data, apps, and settings and returns the device to the OOBE. It removes the device from management and resets identity.
• Why not for this scenario: The goal is to repurpose the same device to a new user with Autopilot. Autopilot Reset keeps the device’s Azure AD/Intune identity and re-provisions the device for the next user without a full wipe.
• What Autopilot Reset does: Removes user data and apps but preserves device identity and management connection, then reapplies the Autopilot provisioning for the next user.
• When you’d use wipe instead: If you’re selling or permanently discarding the device, or you truly want to scrub all traces and start fresh without re-enrolling.

• Correct options: C and D.

• The AWS Cloud Adoption Framework (CAF) security perspective focuses on capabilities to protect information and manage security risk.
• Observability (A) is primarily an operations/monitoring capability, not a core security capability under CAF.
• Incident and problem management (B) is IT service management/operations, not specifically a CAF security capability.
• Incident response (C) is a security-focused capability: detecting, containing, and recovering from security incidents.
• Infrastructure protection (D) involves applying security controls to protect the underlying infrastructure, which fits the security perspective.

• What the scenario tests: How monitoring paths on a Universal Forwarder (UF) behave when you deploy an app via the Deployment Server.
• How monitoring is configured: Log paths to monitor are defined in inputs.conf (within apps/local on the UF). Splunk on the UF merges inputs from all apps and local config.
• Key point: Deploying a new app that adds a monitor for a log file does not typically remove existing monitors. The UF will continue monitoring any previously configured paths and will add the new ones from the deployed app.

• Correct answer: C) /var/log/maillog and /var/log/messages.

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.

• IaaS: You deploy and manage virtual machines, operating systems, disks, and networking. Azure manages the physical datacenter and host infrastructure.
• PaaS: Azure manages the operating system and runtime so you can focus on deploying applications, such as with Azure App Service or Azure Functions.
• SaaS: You consume a complete, provider-managed application.

• Two VMs
• Distributed across two separate availability zones

python 
import azure.cognitiveservices.speech as speechsdk 


speech_config = speechsdk.SpeechConfig( 
    subscription="YOUR_KEY", 
    region="YOUR_REGION" 
)

Important Azure Speech SDK classes

• SpeechConfig

• AudioConfig

python 
  audio_config = speechsdk.audio.AudioConfig( 
      use_default_microphone=True 
  )

• SpeechRecognizer

python 
  recognizer = speechsdk.SpeechRecognizer( 
      speech_config=speech_config, 
      audio_config=audio_config 
  )

• SpeechSynthesizer

• AudioOutputConfig

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