Salesforce Certified Sharing and Visibility Designer Exam Prep
Salesforce Certified Sharing and Visibility Designer (Page 19 )

• Layer 1: Check cables, power, and link lights.
• Layer 2: Check switching, VLANs, and MAC learning.
• Layer 3: Check IP addressing, routing, and gateways.
• Higher layers: Check services, applications, and user settings.

• Create a plan of action: Decide how to implement the fix.
• Implement the solution: Apply the selected fix.
• Verify functionality: Confirm the problem is resolved and systems work normally.

• Sales revenue is affected because internal sales are recorded at the transfer price.
• Operating profit is affected because revenue changes while costs may remain unchanged.
• Return on investment (ROI) is affected because ROI depends on operating profit and the division’s investment.
• Cost of components purchased is not affected by the transfer price. These are costs incurred when buying components from external suppliers, not the internal price charged for the division’s output.

• 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.

• CSIRT: A team that responds to and manages security incidents.
• CVSS: A scoring system for rating the severity of vulnerabilities.
• SOAR: A technology platform for automating and coordinating security operations and incident response.

1. Horizontal scaling — Yes

1. Vertical scaling — Yes

1. Scaling can manage costs — Yes

• Horizontal = more or fewer instances
• Vertical = larger or smaller instances

• Shared capacity

• Fabric capacity — F SKUs

• Fabric trial capacity

• Existing Power BI Premium — P SKUs

• Information sources: Collect security-related data, such as network packets, system logs, audit records, or application events.
• Analysis: Examines the collected data to identify suspicious activity, attacks, or policy violations.
• Response: Takes or recommends action, such as generating alerts, logging the event, or supporting a protective response.

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.

• A — Windows 11 with Azure CLI installed: Yes

• D — Chrome OS using Azure Cloud Shell: Yes

• E — Windows 10 with the Azure PowerShell module installed: Yes

• B — Linux with only Azure CLI: No, because the option does not state that PowerShell is installed.
• C — macOS with PowerShell Core only: No, because it does not state that Azure PowerShell modules or Azure CLI are installed.

• Footprinting is reconnaissance used to map an organization’s network, hosts, naming conventions, and services.
• A name such as fshare.int.complia.org suggests an internal file-sharing host (fshare) and an internal naming structure (int).
• This can reveal useful information to an attacker before attempting exploitation.

• ISP-assigned address space: Usually determined through public registration and DNS data, not an internal hostname.
• DNS sinkhole bypass: Would involve avoiding redirected malicious-domain queries.
• Initial access: DNS reconnaissance alone does not compromise the server.
• Data exfiltration: DNS tunneling typically generates many encoded or unusually long subdomain queries, not simply a meaningful internal hostname.

• CHAP: Uses a challenge-response exchange, so the password is not directly transmitted.
• IPsec: Provides authentication, integrity, and encryption for IP communications.
• EAP: An authentication framework that supports stronger methods, such as certificates or token-based authentication.

• PC A: 192.168.1.10/24
• PC B: 192.168.2.10/24

• Both PCs have a correct default gateway
• A router or Layer 3 switch connects the subnets
• Routing and firewall rules allow the traffic

• Marketing users: Dynamic Forms can show all fields.
• Support users: Field visibility rules can display only the Support preference fields on the record page.
• Reporting: Both profiles can still report on all fields because Dynamic Forms controls page visibility, not the underlying field access.

• A — Two record types and page layouts: Page layouts can control field placement and editability, but they are less flexible for conditional field visibility and would not be the best fit here.
• C — Muting permission set: Permission-set muting is used with permission-set groups; it does not conditionally hide fields on a page.
• D — Field-Level Security: FLS would remove field visibility at the security level. If fields are hidden by FLS, users generally cannot report on or access those fields, conflicting with the requirement that both users report on all data.

• B. Versioned configuration files — Infrastructure is defined in machine-readable files and tracked in version control, allowing repeatable provisioning and change history.
• D. Docker files — Assuming “Doctor files” is a typo for Dockerfiles, a Dockerfile declaratively defines how to build a container image and is commonly considered infrastructure/configuration as code.

• A. Cloned virtual machine images — These are prebuilt infrastructure artifacts, but cloning an image is not itself a code-based infrastructure definition.
• C. Change management database records — A CMDB records information about infrastructure; it does not define or provision that infrastructure.

• A script of cloud CLI commands: IaC because the commands can be saved, reviewed, versioned, and rerun consistently.
• Manually run curl commands: Not necessarily IaC; they are manual actions unless organized into an automated script.
• A sequence of REST requests passed to an API: Could represent automation, but the wording does not explicitly say they are stored and executed as reusable code.
• Commands entered into a cloud console: Manual operation, not IaC.

• Domain controllers, because the requirement specifically excludes them. However, note that both member servers and domain controllers run Windows Server, so OS targeting alone cannot distinguish them unless the targeting option allows selecting the relevant server role/version. In the usual exam interpretation, target the applicable server OS category.
• Client computers, because they run a client Windows operating system.

• Domain: Targets membership in a domain, not the computer’s operating-system type.
• Security Group: Would require creating and maintaining a group containing all member servers, increasing administrative effort.
• Environment Variable: Not designed to reliably identify member servers.

• 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.

• Share of overall contribution: part of the organization’s total contribution is allocated to the supplying division.
• Dual pricing: the supplying division may record a higher transfer price while the receiving division records a lower price, with central accounts absorbing the difference.
• A centrally imposed price range: this may allow the transfer price to be set above variable cost, helping the supplying division recover fixed costs.

• Firewall
• Intrusion prevention and detection
• Antivirus or antimalware
• Web and email filtering
• Virtual private networking (VPN)
• Sometimes content filtering and centralized reporting

1. x = -100
2. -100 < x < 100
3. 100 = x < 1000
4. x = 1000

• -500 ? class 1
• 0 ? class 2
• 100 ? class 3
• 1000 ? class 4

• Scale out during the hours when customer traffic is high.
• Scale in during low-demand periods, reducing instance-hours and cost.
• Use scaling policies based on metrics such as CPU utilization or request count.

• Multiple Availability Zones: Improves availability and fault tolerance, but does not automatically reduce cost.
• Placement group: Helps with specialized performance or low-latency requirements, not cost optimization.
• Private subnets: Improve network security, but do not automatically adjust capacity or reduce EC2 usage costs.

• The requirement is to obtain an assessment of the Azure environment’s design.
• The proposed solution is the Premier support plan.
• Premier can provide this type of guidance, but it is a higher-tier, more expensive option.
• The question specifically says to keep costs to a minimum. A lower tier—typically Professional Direct in the support-plan model used by this question—provides architectural/design guidance.

• 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.

• VLAN 10 for data
• VLAN 20 for voice

• A: Describes physical trunking or logical switch ports, not an SVI.
• B: An SVI can provide management access, but that is only one use—not the best description here.
• D: SVI use is not related to running out of physical ports.

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.

• nmap - 10.11.1.130 — a network scanning command; mitigated with controls such as firewalls, monitoring, or access restrictions.
• “Click this link to get your free gift card.” — a phishing or social-engineering message; addressed through user awareness, email filtering, and authentication controls.
• “Your connection is not private.” — indicates a certificate or TLS validation problem; addressed by correcting the certificate or secure-connection configuration.

• Initial Intrusion: The compromised VPN account and stolen phishing credentials describe how the attacker first gained access, but that happened months earlier.
• Persistence: The scheduled tasks and recurring C2 connections show how the attacker maintained and automated continued access.
• Search and Exfiltration: This would involve locating and transferring data. The scenario does not primarily describe data discovery or theft.
• Cleanup: Cleanup involves removing evidence or covering tracks. Obfuscated scripts may make detection harder, but the main behavior here is maintaining access.

• B — Data source settings in Power BI Desktop:

• D — Formula bar in Power Query Editor:

m 
  Excel.Workbook(File.Contents("new path"))

• E — Advanced Editor in Power Query Editor:

• A: Dataset settings in the Power BI service mainly handle credentials and gateway settings, not changing the local file path embedded in the PBIX.
• C: Current File data-load settings control refresh/load behavior, not the source location.

• Third-party attestation: Provides independent validation, but is typically periodic and performed by an external party.
• Penetration testing: Simulates attacks to identify exploitable vulnerabilities; it does not primarily track compliance.
• Vulnerability scans: Identify technical weaknesses, but do not provide a detailed review of procedures or compliance activities.

• Treatment one – easier to prepare: Convenience for the accounting team is not the main objective of financial reporting.
• Treatment two – fair representation: This is the correct choice because financial statements must faithfully represent the underlying transaction and its economic substance.
• Treatment three – maximises profit: Accounting policies must not be selected simply to manipulate or inflate reported profit.
• Treatment four – easier to understand: Understandability is important, but it cannot justify presenting information that is less faithfully represented.

• Each team’s Bedrock workflow uses a separate IAM service role.
• The role’s Amazon S3 permissions can be limited to only that team’s customer folder or objects.
• This enforces least privilege and prevents one team’s Bedrock application from accessing another team’s data.
• Access is enforced by AWS permissions, not by asking users to provide a customer name in each request.

• A single role with broad S3 access does not provide team-level isolation.
• Asking teams to specify a customer name is not an access-control mechanism.
• Redacting data does not prevent unauthorized access.
• A bucket policy alone is insufficient unless it explicitly enforces the required team-role restrictions.

• Espionage: Stealing information for intelligence or competitive purposes.
• Data exfiltration: Unauthorized removal or transfer of data.
• Nation-state attack: An attack sponsored or conducted by a government.
• Shadow IT: Unapproved technology or methods used to access organizational resources.

• Creating and training machine-learning models
• Testing and evaluating models
• Deploying models as usable endpoints

• A password cracker would normally be identified by behavior such as loading password hashes or attempting dictionary/brute-force attacks.
• The question describes a routine audit, not password recovery.
• Simply encountering a protected file during an audit is characteristic of a misconfigured or insufficiently privileged FIM.

• JobTitle: User1 and User3 only
• UsageLocation: User1, User2, and User3

• For a user whose source of authority is Windows Server Active Directory, attributes such as JobTitle are synchronized to Microsoft Entra ID. You must change them in on-premises AD; editing them in Entra ID is not allowed or will be overwritten.
• UsageLocation is a cloud-managed attribute used mainly for licensing and service availability. It can be set or changed in Microsoft Entra ID, including for synchronized users.

• A. IDS attack signatures: An IDS mainly detects suspicious network traffic. It is not the best control for scanning uploaded documents.
• C. Load balancer: Distributes traffic among servers and can improve availability, but does not specifically detect malware.
• D. WAF: Protects web applications from attacks such as SQL injection and command injection. It may inspect web requests, but malware scanning of PDF files is not its primary function.

1. Discovery tries the SSH probe first.
2. SSH fails.
3. Discovery tries SNMP, which succeeds.
4. Discovery records SNMP as the preferred service for that IP.
5. Future discoveries try SNMP first.

• Classification identifies the type of device, such as a server, router, or printer.
• Credential affinity remembers which credentials work for a device.
• MID Server affinity associates a device or network with a particular MID Server.
• IP service affinity specifically controls the preferred discovery protocol or service.

• Honeypots: Decoy systems or services designed to attract attackers. Legitimate users should not access them, so activity there is a strong indicator of compromise.
• DNS sinkholes: Redirect malicious or suspicious DNS requests to a controlled destination. This can identify infected systems attempting to contact command-and-control infrastructure.

• Tokenization: Replaces sensitive data with non-sensitive tokens; it is not an attacker-detection method.
• CI/CD: Automates software development, testing, and deployment.
• Threat modeling: Identifies potential threats and weaknesses before or during system design; it does not directly detect an active infiltrator.
• Data obfuscation: Makes information harder to understand, but does not identify attackers.

• Honeypots: Decoy systems designed to attract attackers. Legitimate users should not access them, so activity there is a strong indication of unauthorized access.
• DNS sinkholes: Redirect requests for known malicious domains to a controlled internal destination instead of the real attacker infrastructure. Security teams can then identify infected systems making those requests.

• Tokenization: Replaces sensitive data with non-sensitive tokens; it protects data but does not detect intruders.
• CI/CD: Software development and deployment processes.
• Threat modeling: Identifies potential threats before or during system design, rather than detecting successful infiltration.
• Data obfuscation: Makes information harder to understand, but is not an attacker-detection mechanism.

• File policy in Microsoft Defender for Cloud Apps: Can detect activity involving files, but it is not designed specifically to investigate risk from departing or deleted users.
• Access review policy in Microsoft Entra: Reviews whether users should retain access to resources; it does not detect historical document downloads.
• Alert policy in Microsoft Defender for Office 365: Primarily handles alerts for email and collaboration threats, not insider-risk investigations involving departed users.

• Leaf switches provide access ports for servers, users, and other devices.
• Spine switches provide the backbone between leaf switches.
• Every leaf switch connects to every spine switch, creating a predictable, full-mesh fabric between the two layers.
• Leaf switches do not connect directly to other leaf switches, and spine switches do not connect directly to other spine switches.

• A: Adding both a spine and leaf is unnecessary for simply adding access ports.
• B: It focuses on uplink speed, not the scalable topology.
• D: A single spine connection creates a bottleneck and a single point of failure.

• Future costs — Only costs occurring in the future can be affected by the investment decision. Past costs are sunk and irrelevant.
• Incremental costs — These are additional costs caused by choosing the investment. They are relevant because they differ between alternatives.
• Cash flows — Capital investment decisions are based on future cash inflows and outflows, usually discounted to present value.

• Committed costs have already been agreed or incurred and generally cannot be changed by the decision.
• Unavoidable costs will arise regardless of which option is selected, so they do not affect the choice.

• 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.

• Power BI dataset — Reuses the existing tables, relationships, and measures. You mainly create new visualizations.
• SharePoint folder — Connects to the source files, but you would need to rebuild the model and measures.
• Power BI dataflows — Primarily provide reusable data preparation; they do not automatically provide the existing report’s measures and model.
• Excel workbook — Requires reconnecting to and modeling the source data again.

• A: It allows for branching and merging...

• C: It allows multiple engineers to work against the same code and configuration files and manage differences and conflicts.

• B: Build automation and infrastructure provisioning are handled by CI/CD and automation tools, not by version control alone.
• D: User Stories and backlogs are managed by project-management tools.
• E: Version control stores and tracks code, but it does not itself ensure that developers write effective unit tests.

• Explain questions from the exam page
• Check whether the stated answer is technically correct
• Compare answer choices and eliminate distractors
• Clarify Azure AI concepts and terminology
• Point out when an answer key appears unreliable or when an image is missing

• A — Technical review by business owner: A business owner may approve the business impact, but a technical review is normally performed by technical personnel.
• C — Cost-benefit analysis after implementation: Cost-benefit analysis should occur before implementation to support the approval decision.
• D — Business continuity testing: This can be appropriate for major changes, but it is primarily part of business continuity and disaster recovery planning—not a universal change-management activity.

• Remote and global work: Access is based on the approved device, not a specific office or geographic location.
• Seamless login: Users may not need to repeatedly enter credentials on a recognized device.
• Limited equipment: It uses devices employees already have rather than requiring separate hardware.

• Geotagging: Restricts access based on location and may prevent legitimate remote work.
• Smart cards: Require issuing and managing additional physical equipment.
• Time-based logins: Limit access by time and do not provide a seamless authentication experience.

• Complete MFA, or
• Securely change their password

• Identity Protection: evaluates user and sign-in risk and can trigger remediation.
• Privileged Identity Management (PIM): manages temporary, just-in-time access to privileged roles; it does not automatically require password changes for risky sign-ins.

• Fine-tuning requires a properly formatted training dataset.
• For classification or instruction-style fine-tuning, examples are supplied in JSONL format.
• Each line represents one training example, typically containing the input prompt and the desired completion or label.
• The file must then be uploaded or made available to the Microsoft Foundry fine-tuning workflow.

• A: Prompt Flow is useful for prompt experimentation, but it is not required to start fine-tuning.
• B: You do not need to deploy the base model to an online endpoint before fine-tuning.
• C: Tracing supports observability and evaluation, but it does not prepare the fine-tuning job or dataset.