PRINCE2 PRINCE2 Re-Registration Exam Prep
PRINCE2 Re-Registration (Page 12 )

• 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.

• 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.

• Use one Azure AD directory for the organization.
• Separate divisions with subscriptions and/or resource groups.
• Assign each division’s administrator appropriate Azure role-based access control (RBAC) permissions, scoped to their subscription or resource group.

• 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”.

• Reusing features across different models and teams
• Consistent feature definitions
• Managing feature versions and metadata
• Using features for both training and inference

• SageMaker Data Wrangler: Prepares and transforms data; it is not primarily a shared feature repository.
• SageMaker Clarify: Helps detect bias and explain model predictions.
• SageMaker Model Cards: Documents models, including intended use, risks, and evaluation results.

• Leaf switches provide access ports for servers, endpoints, and other devices.
• Spine switches form the fabric’s backbone.
• Every leaf connects to every spine, creating a predictable, high-bandwidth, low-latency path.

• A: Adding both a spine and leaf is unnecessary for simply adding access ports.
• B: Adding a spine does not directly provide endpoint access ports.
• D: A single connection to one spine creates a bottleneck and a single point of failure.

• Help the team identify the causes of delays.
• Encourage continuous improvement through retrospectives.
• Support the team in changing its process, tools, or collaboration methods.
• Help remove impediments that the team cannot resolve independently.

• A: An impediment log may help, but simply maintaining it does not ensure the impediments are resolved.
• B: Directing the team to solutions conflicts with agile self-management.
• C: Adding impediments to the product backlog could delay their resolution and treats them like product features.

• Elicited and documented customer requirements
• Converted them into clear, measurable specifications
• Validated the requirements with the customer
• Used them as the basis for design, quality checks, and acceptance

• A: Requirements should reflect the customer’s needs—not the supplier’s standards.
• B: Iteration review planning does not replace proper requirements collection.
• D: A retrospective occurs after work is completed and is mainly for process improvement, not initial requirements definition.

• An Azure pay-as-you-go subscription follows an operational expenditure (OpEx) model.
• With OpEx, the organization pays for services as they are used, typically based on consumption.
• There is no need to purchase and maintain physical servers upfront.
• This contrasts with capital expenditure (CapEx), where the company makes a significant upfront investment in infrastructure, such as on-premises Hyper-V hosts.

• The listener has a virtual IP address on the load balancer.
• The active SQL Server replica must respond using the listener’s IP address, not its own VM IP.
• Floating IP allows the listener IP to be preserved and returned by the active replica.
• This is required for the SQL Always On listener configuration.

• A health probe for the SQL listener/cluster role
• The SQL Server listener port, commonly 1433
• Appropriate backend pool membership

• Guiding principles
• Governance
• Service value chain
• Practices
• Continual improvement

• Guiding principles: Recommendations that guide decisions and actions; they do not describe the entire system.
• Four dimensions: Perspectives used to ensure a balanced approach to service management.
• Service relationship: Describes cooperation between a service provider and service consumer, rather than the whole value-creation system.

• Mesh: WAPs communicate wirelessly with one another, often to extend coverage without wired connections. It does not specifically describe centralized WLC management.
• Point-to-point: Connects two specific devices or locations.
• Ad hoc: Wireless devices communicate directly with one another without an access point.

• Idioma: “Responda em português.”
• Tamanho: “Responda em até três frases.”
• Formato: “Use uma lista com três recomendações.”
• Objetivo: “Recomende produtos com base nas necessidades do cliente.”

• Mudar o tamanho do LLM: pode alterar capacidade e custo, mas não garante idioma ou respostas curtas.
• Aumentar a temperatura: aumenta a criatividade e a aleatoriedade; não controla diretamente o tamanho ou idioma.
• Aumentar o Top K: permite considerar mais tokens prováveis, aumentando a variedade; também não é o mecanismo principal para controlar idioma ou comprimento.

1. Which host originated the infection

1. Which remaining hosts are infected

• Red team: Simulates attackers; exploits weaknesses to test security.
• Blue team: Defends systems; monitors, detects, and responds to attacks.
• Purple team: Combines red- and blue-team efforts to improve detection and defense.
• White team: Oversees or manages the test, defining rules, scope, and authorization.

• 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.

• IPsec: Protects traffic at the network layer, commonly for VPNs; it is not WPA2’s authentication protocol.
• SSL: Protects specific application sessions and has largely been replaced by TLS.
• SSH: Secures remote command-line administration.

• Online payment fraud
• Account takeover
• Fake account creation
• Suspicious transactions

• SAML: Enables federated authentication and single sign-on between the IdP and SaaS providers.
• OAuth tokens: Allow delegated application access; OAuth is not primarily the first step for employee SSO.
• Password vaulting: Stores or autofills multiple passwords but does not eliminate the separate credentials or provide true federation.

1. Identify the fact table

• Records something that happened, such as a sale, test, appointment, or measurement
• Contains foreign keys to other tables
• Contains measures or numeric results
• Has many rows

2. Identify a dimension table

• Describe people, products, dates, locations, or departments
• Contain descriptive columns
• Are referenced by the fact table
• Have fewer rows than the fact table

3. Identify the surrogate key

• System-generated
• Unique within the dimension
• Often named PatientKey, CustomerKey, or similar
• Usually an integer
• Not meaningful outside the model

• Planning – identifying the need, objectives, scope and resources.
• Analysis – understanding business requirements and current problems.
• Design/development – determining how the system will work.
• Implementation – installing the system and putting it into operation.

• 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.

• vSmart controls the overlay control plane. It uses OMP to exchange routing information, distribute policies, and determine how WAN Edge devices reach one another.
• vManage is the centralized management and configuration platform.
• vBond is the orchestrator. It helps devices authenticate, discover controllers, and traverse NAT during onboarding.
• APIC-EM is not the SD-WAN control-plane component described here.

• AWS Audit Manager continuously collects evidence from AWS services and helps assess whether workloads comply with regulatory frameworks and internal policies. It can produce audit-ready reports.
• AWS Config records resource configurations and evaluates them against compliance rules. For example, it can check whether encryption, logging, or access settings meet company requirements.

• Amazon Inspector identifies software vulnerabilities and unintended network exposure, not general policy or regulatory compliance.
• Amazon CloudWatch monitors metrics, logs, and operational events, but is not primarily a compliance assessment service.
• AWS CloudTrail records API activity for auditing and investigation. It supports compliance, but does not itself provide the structured compliance assessment capabilities of Audit Manager and Config.

• Accept: Acknowledge the risk and choose not to take additional action.
• Transfer: Shift the risk or its financial impact to another party, such as through insurance or outsourcing.
• Mitigate: Reduce the likelihood or impact of the risk, such as by implementing security controls.
• Avoid: Eliminate the activity or condition that creates the risk.

1. Convert text and images into numerical embeddings.
2. Store or compare those embeddings.
3. Retrieve the most semantically similar items.

• Embedding model: Best for similarity search and retrieval.
• Generation model: Best for producing new text or other content from multimodal input.

• MAC flooding: Overwhelms a switch’s MAC address table, potentially causing traffic to be broadcast.
• DNS spoofing: Provides false DNS responses to redirect users.
• ARP poisoning: Sends false ARP information to associate an attacker’s MAC address with another device’s IP address.

• A — conformant: Incorrect because one explicit requirement is violated.
• B — consistent: “Consistent” is not sufficient where the implementation contradicts a mandatory specification. The RPC mechanism is covered by the architecture, and the implementation differs from it.
• C — consistent: Incorrect reasoning: the RPC mechanism does have a defined architectural requirement, so conformance must be assessed.
• D — non-conformant: Correct because the implementation fails to follow the specified RPC/threading requirement.

• 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.

• 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.

• Entradas grandes, de até aproximadamente 1 GB.
• Processamentos demorados, que podem levar até cerca de 1 hora.
• O cliente envia a solicitação e consulta o resultado depois, sem precisar manter uma conexão aberta durante todo o processamento.

• Real-time inference: indicada para respostas imediatas, com entradas menores e processamento rápido.
• Serverless inference: adequada para cargas variáveis e solicitações menores; não é ideal para processamento longo e arquivos grandes.
• Batch transform: processa grandes volumes em lote, mas não atende à necessidade de respostas próximas do tempo real.

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.

• Financial performance
• Customer perspective
• Internal business processes
• Learning and growth

• Increasing the number of indicators can make the scorecard cluttered and less useful. Measures should be limited to those that are strategically important.
• The scorecard is mainly a tool for implementing and monitoring strategy, not for formulating strategy.
• It should contain both leading indicators and lagging indicators. Leading indicators predict future performance, while lagging indicators measure results already achieved.

Correct choices

• DB1: Azure Data Box Heavy
• Virtual machines: Azure Migrate

Why

• Replication with minimal downtime
• A test migration
• Final cutover after validation

• Inherent risk: Risk that exists before controls are implemented.
• Avoided risk: Risk eliminated by choosing not to perform the risky activity.
• Operational risk: Risk arising from failures in processes, people, systems, or daily operations.

• Hardware and infrastructure
• System software
• Networks and communications
• Technology platforms and services

• Business Architecture: Defines business strategy, organization, functions, and processes.
• Data Architecture: Defines the structure and management of enterprise data.
• Application Architecture: Describes application systems and how they interact to support business and data processing.

• 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.

• Personally identifiable information (PII) detection

• Prompt Shields

• Self-harm content filtering addresses self-harm-related content.
• Violence content filtering addresses violent content.
• Neither protects customer PII or detects document-based prompt injection.

• Restarting the device: may fix software problems, but not a directional connection issue.
• Swollen battery: is a safety concern, but would not typically cause recognition only in one orientation.
• Failing cable: possible in general, but less likely because it works on other similar devices.
• Dirty or damaged port: best matches the symptoms. Inspect and carefully clean the port, or have it serviced if damaged.

• Issuer: Identifies the party issuing a SAML message—usually the identity provider in the assertion. It is not the Salesforce-specific identifier the architect should provide for differentiating Salesforce.
• Identity Provider Login URL: The endpoint where users are sent to authenticate.
• SAML identity location: Determines where the user identity is found in the SAML assertion, such as the subject or an attribute.

• 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.

• 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.

• Separate accounts/domains create a strong administrative boundary between non-production and production.
• Separate projects provide distinct IAM boundaries, billing, quotas, service accounts, and resources.
• Developers can receive permissions in the development project without automatically gaining access to production.
• Production administrators can be restricted from development resources, supporting separation of duties and least privilege.

• A: Creates excessive project fragmentation without being required by the stated requirement.
• C: Keeps development and production under one account and does not clearly separate administrative duties.
• D: Uses one account and only two projects; it provides some project isolation but weaker organizational separation.

• Reviewing security policies and standards checks whether controls are documented, not whether they work.
• Reviewing security logs may show that monitoring is occurring, but does not directly establish that preventive controls are effective.
• Analyzing technical security practices is broad and may assess implementation, but it is less objective than an actual controlled attack.

• Design/compliance effectiveness: policies, standards, and procedures
• Operational effectiveness: logs, records, and evidence that controls operate
• Technical security effectiveness: penetration testing or vulnerability testing

1. Start with the 48-bit MAC address.
2. Split it into two 24-bit halves.
3. Insert FF:FE between the halves, creating a 64-bit value.
4. Invert the U/L bit—the seventh bit from the left in the first MAC byte.

text 
MAC:       00:1A:2B:3C:4D:5E 
EUI-64:    02:1A:2B:FF:FE:3C:4D:5E

• FE80 is the prefix commonly used for an IPv6 link-local address, not part of the EUI-64 interface ID.
• The interface ID is not random.
• The MAC address is modified by inserting FF:FE and changing the U/L bit.

• Users logging in more frequently than average
• Logins occurring during non-business hours
• Patterns that may indicate compromised credentials or suspicious access

• Login History: Shows individual login attempts and basic details, but does not provide behavioral analysis or averages.
• Login Inspector: Used to troubleshoot or inspect a specific login attempt, rather than analyze organization-wide patterns.
• Login Report: May provide reportable login information, but it is not the specialized tool for forensic behavioral analysis.

• Customer contains one row per customer and the customer’s Address ID.
• Address contains one row per address and the corresponding location details.

1. Select the Customer query.
2. Choose Merge Queries.
3. Match Customer[Address ID] with Address[Address ID].
4. Use a Left outer join so every customer is retained.
5. Expand the merged Address column and select City, State/Region, and Country.

• Append adds rows vertically and is used for tables with the same structure.
• Group By summarizes rows rather than adding related columns.
• Transpose switches rows and columns and does not establish a relationship.

• A. Modify CommunitiesSelfRegController

• C. Configure Communities registration to use a custom Visualforce page

• B. Modify the self-registration trigger — There is no standard “self-registration trigger” intended for this configuration. The registration logic is handled by the Visualforce controller.
• D. Configure a custom Apex controller — The standard approach is to modify the provided CommunitiesSelfRegController, rather than simply selecting a separate controller configuration.