Veritas VCS-273 Exam Prep
Administration of Veritas NetBackup 7.6.1 and NetBackup Appliances 2.6.1 (Page 8 )

• Free: Doesn’t support the required custom domain and SSL configuration.
• Shared: Doesn’t provide the required SSL support or scalability.
• Basic: Supports multiple instances and SSL, but its storage limit is insufficient for the required 12 GB.
• Standard: Supports:

• B: Não é preciso implantar o modelo em um endpoint do Amazon SageMaker, pois o Bedrock já oferece a infraestrutura de inferência.
• C: O SageMaker Model Registry é usado para gerenciar modelos no SageMaker, não é requisito para modelos customizados no Bedrock.
• D: Não existe uma etapa geral de “conceder acesso” ao modelo customizado como requisito principal de uso. O ponto essencial é adquirir o Provisioned Throughput.

• Existing expected ROI: 15%
• New project ROI: 14%
• Cost of capital: 12%

• Project return = 14%
• Cost of capital = 12%
• It earns 2% more than the cost of capital, so it should increase shareholder value.

sql 
ALTER TABLE dbo.DimCustomer 
ADD CONSTRAINT PK_DimCustomer 
PRIMARY KEY NONCLUSTERED (CustomerKey) 
NOT ENFORCED;

1. ALTER TABLE dbo.DimCustomer
2. ADD CONSTRAINT PK_DimCustomer PRIMARY KEY NONCLUSTERED (CustomerKey)
3. NOT ENFORCED

• B. Deploy to a SageMaker endpoint: This is needed for SageMaker-hosted models, not for a model intended to be used directly in Amazon Bedrock.
• C. Register with SageMaker Model Registry: SageMaker Model Registry is unrelated to invoking custom models in Bedrock.
• D. Grant access in Bedrock: Access permissions may be necessary, but they do not replace the required provisioned capacity.

• User-generated content: precisa ser coletado, filtrado, anonimizado e rotulado.
• Moderation logs: podem conter dados inconsistentes, informações pessoais e decisões que precisam ser revisadas.
• Content moderation guidelines: são regras ou orientações, não dados diretamente utilizáveis para medir o comportamento do modelo.

• Plenum: Describes a fire-resistant cable jacket rating, not a transmission medium or speed.
• Ethernet: Describes a networking technology; Ethernet can use copper or fiber, so the term alone does not identify the fastest cable type.
• DAC (Direct Attach Copper): Uses copper and is typically intended for short-distance, high-speed connections in data centers.

• Key-based authentication: Provides an endpoint secured with API keys.
• Real-time REST access: Supports online inference through an HTTP/REST endpoint.
• No VM vCPU quota consumption: Microsoft hosts and manages the underlying infrastructure, so your subscription’s VM quota isn’t used.

• Batch: Intended for asynchronous, large-volume processing rather than real-time requests.
• Self-hosted container: You manage the infrastructure and typically need compute resources and quota.
• Standard: This is not the clearest deployment type for the stated requirements; the specific Foundry option is serverless API.

• Prevention costs: Avoiding defects, such as staff training and improved product design. The company has invested in these.
• Appraisal costs: Detecting defects before products reach customers, such as inspection and testing. The planned batch sampling has not been implemented.
• Internal failure costs: Correcting defects found before delivery.
• External failure costs: Dealing with defects after delivery, such as refunds, replacements, complaints and lost sales.

• IKE (Internet Key Exchange) negotiates the IPsec security association, including:

• ESP (Encapsulating Security Payload) protects the data after negotiation by providing encryption and optionally authentication.
• AH provides authentication and integrity but does not encrypt traffic.
• TLS is used for protocols such as HTTPS, not for negotiating IPsec parameters.

• Namespace: The namespace associated with the code where the checkpoint was set.
• Time: When the checkpoint was captured or executed.

• Exception: Exceptions are viewed through debug logs or execution results, not as the primary Checkpoints tab information.
• Debug Statement: System.debug() output belongs in the Logs tab. A checkpoint captures program state, such as variables and their values, at a specific point in execution.

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

• Preparing and managing datasets
• Building and training machine-learning models
• Testing and evaluating models
• Deploying models as endpoints for predictions

• SAS (Serial Attached SCSI):

• SSD (solid-state drive):

• 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.

• Third-party enterprise application with embedded AI: The provider manages most security controls. The customer mainly manages users, data, and configuration.
• Application using an existing third-party FM: The customer controls the application and its inputs/outputs, but the provider controls the underlying model and infrastructure.
• Fine-tuning a third-party FM: The customer has additional responsibility for training data, data protection, and model behavior, but still depends on the provider’s base model and platform.
• Building and training a model from scratch: The customer controls the model, training data, application, deployment, and many security safeguards. This creates the greatest security ownership.

• A. IDS attack signatures: An IDS primarily detects suspicious network activity; it is not the best tool for scanning uploaded files.
• C. Load balancer: Improves availability and distributes traffic but does not inspect files for malware.
• D. WAF: Protects web applications against attacks such as SQL injection and command injection. It may inspect HTTP requests, but malware scanning of uploaded PDFs is the key requirement here.

• Technical platform interfaces: Important for integration and interoperability, but they should support—not drive—the architecture.
• Scalability of the network: Valuable as the organization grows, but a scalable solution is ineffective if it does not meet business or security needs.
• Development methodologies: Relevant to implementation, but they do not define the organization’s fundamental security requirements.

• Restrict a port to a defined number of MAC addresses
• Use statically assigned or “sticky” MAC addresses
• Shut down or restrict the port when violations occur

• Load balancer: Distributes application traffic; it does not protect switch MAC tables.
• IPS: May detect suspicious network activity, but is not the primary control for limiting switch MAC entries.
• NGFW: Filters traffic at the network boundary; it does not directly prevent MAC table flooding on access switches.

• Repetitive and time-consuming
• Based on measurable conditions, such as last check-in time
• Easy to automate with alerts, dashboards, or scheduled reports
• Important for ensuring scan coverage

• Assigning custom recommendations usually requires analyst judgment and context.
• Analyzing false positives can sometimes be assisted by automation, but validation often requires human review.
• Rendering an additional executive report increases reporting activity rather than reducing a core operational workload.

• Containment policy: controls permitted communication during containment.
• Detection/response configuration: determines when containment is initiated.

• Tracking who accessed or changed customer information
• Detecting unauthorized activity
• Supporting accountability and investigations
• Providing evidence for compliance and incident response

• Single sign-on not enabled: SSO improves convenience and centralized authentication, but it is not essential for access control.
• Complex passwords not required: This weakens authentication, but other controls may reduce the risk.
• Security baseline not consistently applied: A concern, but it is less specific and less immediately damaging than having no activity records at all.

Correct reasoning

• Create storage5 as a General-purpose v2 (StorageV2) account.
• Configure Blob object replication from storage5 to an existing compatible storage account.
• The destination must support object replication—typically another StorageV2 or premium block blob account.
• Blob versioning must be enabled on both accounts.
• Blob change feed must be enabled on the source account.
• The accounts must meet Azure’s object-replication requirements, including being in supported regions and having compatible configuration.

• Training data for potential bias before model training
• Trained models for bias in predictions
• Model features to help explain how they influence predictions

• A: RAG workflows are typically built using services such as Amazon Bedrock, knowledge bases, and data sources.
• B: Monitoring model quality in production is primarily associated with SageMaker Model Monitor.
• C: Documenting model details is handled by capabilities such as SageMaker Model Cards.

• Load balancer: Distributes application traffic; it does not protect switch MAC tables.
• IPS: May detect some attack traffic, but it is not the primary control for switch-port MAC learning.
• NGFW: Protects at network boundaries but does not directly limit MAC addresses learned on access ports.

• Constrained VMs: VMs whose workloads are experiencing resource pressure, such as insufficient CPU or memory. They may need additional resources or optimization.
• Overprovisioned VMs: VMs allocated more CPU, memory, or storage than their workloads require. Their resources may be reclaimed or reduced.

• Underprovisioned VMs: This is not the predefined view identified for this report question.
• Zombie VMs: These are typically inactive or unused VMs and are useful for cleanup, but they are not the two views specifically intended here to identify inefficient resource allocation.

• Dataset file: Use Import mode. Excel files are imported into Power BI, and the two Sales tables can be combined with Power Query.
• Report file: Use a live connection to the published dataset. This keeps the report separate from the dataset and avoids importing the data again.

• Report file: DirectQuery/live connection
• Dataset file: Import

• A: Build a custom API — possible, but unnecessary as the primary solution and potentially more costly to develop and maintain.
• B: Use pre-built chatbots — these generally answer questions but may not support the required custom workflow and real-time actions.
• C: Fine-tune with sample data — improves model behavior but does not provide live database access.
• D: Google Cloud databases + Vertex AI — stores current inventory data and lets an AI agent retrieve it and take actions based on up-to-date information.

• A class/teacher fact should contain one row per Class ID. It can support the count of classes and contain attributes such as Teacher First Name and Period Number.
• An attendance fact should contain one row per attendance event, such as Class ID + student + date. It supports attendance counts by day and monthly averages.

• Teacher First Name ? Teacher fact
• Period Number ? Attendance fact

spl 
error AND host=WWW3 
status=404 OR status=500 
NOT source=access.log

• Quotation marks are used for exact phrases or values containing spaces.
• Parentheses group parts of a Boolean expression, but they are not required for every Boolean search.

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.

• Deterministic approve/reject results
• Strict adherence to predefined rules
• Consistent, reproducible outcomes
• Auditability and regulatory compliance

• Allow only approved MAC addresses
• Limit the number of learned addresses
• Shut down or restrict a port when a violation occurs

• Load balancer: Distributes application traffic; it does not protect switch MAC tables.
• IPS: May detect some attack traffic, but it is not the direct control for limiting MAC addresses on switch ports.
• NGFW: Filters routed network traffic, while this attack targets Layer 2 switch behavior.

• One network address
• Two usable host addresses—one for each router
• One broadcast address

• Router A: 10.2.1.1
• Router B: 10.2.1.2

• Azure Cosmos DB is a globally distributed NoSQL database service. It stores and retrieves application data, but it does not provide the complete environment for building, testing, and deploying predictive analytics models.
• Azure Machine Learning is the appropriate service for this requirement. It supports creating experiments, training and testing models, and deploying them for predictions.
• Azure Machine Learning Studio provides the browser-based interface for these tasks.

• Who owns security risks and decisions
• Who is responsible for implementing controls
• Who reports security performance to management
• How security responsibilities align with business objectives

• B — Compliance with audit requirements: Governance may support compliance, but audit compliance is not its main objective.
• C — Monitoring security incidents: This is an operational security activity, not the central purpose of governance.
• D — Creation of tactical solutions: Governance establishes direction and oversight; tactical solutions belong to management and operational teams.

• 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.

• Reduce the training data: Usually makes overfitting worse.
• Add hyperparameters: Hyperparameters already control training; simply adding them does not solve the problem. Proper tuning could help, but that is not what the option states.
• Increase training time: More training can worsen overfitting if the model is already memorizing the training data.

• Input validation: Prevents malicious or malformed input within an application; it does not authenticate a software package.
• Secure cookies: Protect web-session cookies; they do not protect downloadable software.
• Fuzzing: Tests software by supplying unexpected input to find vulnerabilities; it does not verify software integrity.

• 10.2.1.0 — network address
• 10.2.1.1 — usable host
• 10.2.1.2 — usable host
• 10.2.1.3 — broadcast address

• A — Increased call deflection: o KCS disponibiliza conhecimento reutilizável para clientes e agentes, ajudando a resolver dúvidas sem abrir ou continuar chamadas.
• C — Reduced issue resolution time: os representantes reutilizam artigos e soluções já documentadas, acelerando o atendimento.
• E — Optimized use of resources: o conhecimento compartilhado reduz retrabalho e melhora o uso dos recursos da equipe.

1. Search & Intelligence in the Microsoft 365 admin center — No

1. Audit in the Microsoft Defender portal — Yes

1. Audit in the Microsoft Purview portal — Yes

• Assign endpoints to a group based on the result of one policy.
• Use that group as the scope of another policy.
• This effectively passes the endpoints from one policy’s processing flow to another.

• Actions that call external APIs
• Subflows
• Connection and credential aliases
• Supporting integration logic

• Action — A reusable operation within a flow, such as “Create a record” or “Send email.”
• Connection — Defines how ServiceNow authenticates and communicates with an external system.
• Integration step — A general flow step, not the name for the packaged integration.

• Packet capture: Collects and analyzes network traffic; it does not describe the scan type.
• Privilege escalation: Attempts to gain higher permissions after obtaining access.
• System enumeration: Identifies systems, services, or resources, but is not specifically defined by the lack of credentials.
• Passive scan: Observes traffic without actively probing systems. A vulnerability scan is generally an active assessment.

• Create the field in the CRM on the relevant person records—typically Lead and Contact in Salesforce.
• Give the Marketo Sync User read/write access to the field.

• LastName: Required.
• FirstName: Optional.
• AccountId: Optional; a Contact may be associated with an Account later or remain private, depending on organization settings.
• Name: A compound display field made from first and last name; it isn’t entered as a separate field.

• Escape to host: A virtualization attack where code escapes a guest VM and accesses the host system. Nothing in the scenario mentions virtualization.
• Browser extension: Can redirect or manipulate web-browser traffic, but not normally database-server traffic.
• Valid accounts: Describes using stolen or compromised credentials, not redirecting network traffic.
• Process injection: Injecting malicious code into a legitimate database or networking process could allow an attacker to alter how that process handles outgoing traffic.