CompTIA CV0-003 Exam Prep
CompTIA Cloud+ Certification (Retired and replaced with CV0-004) (Page 33 )

• The servers are powered on.
• The network is functioning.
• Devices can still reach the server network.

• ARP poisoning: Redirects or intercepts local network traffic; it usually affects communication paths rather than overwhelming a service.
• Jamming: Disrupts wireless or radio signals. It would not fit a system that remains accessible from devices on the server network.
• Kerberoasting: Attempts to crack service-account credentials from Kerberos tickets; it does not normally cause an immediate outage.

• DB1 (500 TB): Azure Data Box Heavy

• Virtual machines: Azure Migrate

• Regression predicts continuous numeric outcomes, such as house price, temperature, or sales amount.
• Linear regression assumes the target can be estimated from a linear relationship between the input features and the target.
• In Azure Machine Learning, you use a labeled dataset containing:

• Classification predicts a category or class, such as “spam” or “not spam.”
• Clustering groups similar records without a known label; it is an unsupervised learning technique.

• Sales process: Controls which Opportunity Stage values are available.
• Record type: Associates the appropriate sales process with a business scenario and controls available picklist values.
• Page layout: Controls which fields are displayed and whether they are required or read-only.

• Energy Market Team 1 ? Record Type 1, Sales Process 1, Page Layout 1
• Energy Market Team 2 ? Record Type 2, Sales Process 2, Page Layout 2
• Services/Installation Team ? Record Type 3, Sales Process 3, Page Layout 3

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

• 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.

• 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.

• SAML (Security Assertion Markup Language): Commonly used for enterprise SSO between an identity provider and cloud applications. It sends signed authentication assertions to the service provider.
• RADIUS: Primarily used for centralized authentication to network devices, VPNs, and wireless networks.
• EAP: An authentication framework commonly used with network access technologies; it is not typically the SSO protocol for SaaS applications.
• OpenID: Related to identity authentication, but traditional enterprise SSO questions involving cloud applications generally expect SAML. OpenID Connect is a newer authentication layer built on OAuth 2.0.

• Share files with the wrong people
• Use public links unnecessarily
• Fail to classify sensitive data
• Upload data to unauthorized services
• Misunderstand company sharing policies

• A — Step-up authentication: Helps protect administrator accounts, but does not directly address improper file sharing by users.
• C — Password complexity: Helps reduce password attacks, not accidental data disclosure.
• D — Mobile device management: Secures managed mobile devices, but does not directly control file-sharing behavior across the organization.

• Disable default accounts: Default or unused accounts may have known usernames or predictable settings, making them targets. They should be disabled, removed, or securely configured.
• Remove unnecessary services: Every running service creates potential vulnerabilities. Removing services the server does not need reduces exploitable entry points.

• Add the server to the asset inventory: Important for asset management, but not a hardening step.
• Document default passwords: Default passwords should be changed or eliminated—not documented for continued use.
• Send server logs to the SIEM: Valuable for monitoring and detection, but generally performed as part of logging/monitoring configuration.
• Join the server to the corporate domain: May be necessary operationally, but does not itself harden the server.

• SQL injection
• Cross-site scripting (XSS)
• Malicious file uploads
• Common web-application exploits

• Layer 4 firewall: Filters primarily by IP addresses, ports, and transport protocols; it lacks detailed web-application inspection.
• NGFW: Provides advanced network filtering and may include application awareness, but a WAF is more specialized for protecting web applications.
• UTM: Combines several security functions in one device, but it is not specifically focused on web-application attacks.

• transform() — modifies every element in an array
• filter() — keeps elements matching a condition
• exists() — checks whether any element matches
• aggregate() — reduces array elements to one result

sql 
SELECT transform(scores, x -> x * 2) 
FROM student_scores;

• A is incorrect because higher-order functions are mainly useful for nested or complex data.
• B is incorrect because they do not convert logic into Python.
• D is incorrect because their purpose is not to fix slow built-in functions, but to process arrays or maps with custom logic.

1. You try to access a cloud application, such as accounting software.
2. The application redirects you to the company’s IdP.
3. You authenticate with the IdP.
4. The IdP sends a digitally signed SAML assertion to the application.
5. The application validates the assertion and grants access.

• The user’s identity
• Whether authentication succeeded
• Roles or group membership
• Access-related attributes

• D — Value analysis: Correct. It examines an existing product to identify ways of reducing its cost while still providing the required functions, quality and reliability.
• E — Kaizen costing: Correct. It involves making continuous, small improvements and cost reductions while the product is already in production.

• Functional analysis: It is not limited to minimizing the cost of the originally defined functions. It examines what functions are required and how they can be provided at the lowest overall cost.
• Value engineering: Usually applies to the design and development of a new product, rather than being a fundamental redesign of existing organisational processes. That description is closer to business process re-engineering.
• Target costing: The target cost is generally established during the product development stage, before production begins. It is not continuously reset while the product is in production.

• 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.

• Career enhancement — A mentor can provide guidance, advice, networking support and help the mentee identify career opportunities.
• Support with new challenges — A mentor can help the mentee think through unfamiliar situations and develop confidence and practical solutions.
• Personal development — Mentoring can improve skills such as communication, leadership, decision-making and self-awareness.

• Solving all problems — The mentor supports and guides the mentee but should not take responsibility for solving every problem.
• Discipline would be dispensed if they make a mistake — Mentoring does not remove normal accountability or workplace consequences. Mistakes should instead become learning opportunities.

• Labels control access and protection, not blanket exclusion.
• If the user has permission to open a labeled file—especially if the label allows authenticated users to view it—Copilot can use that file in a summary.
• If the label applies encryption and the user lacks the required usage rights, Copilot should not use the file or reveal its contents.
• Copilot honors the user’s existing permissions, so it should not expose labeled content to an unauthorized user.

• A — If there is no demand, there should be no production.

• B — Not all sales equal profit.

• C — Stocking up on inventory is bad for business.

• D — All costs, except materials, are considered fixed.

• E — Departments should operate at full capacity regardless of bottlenecks.

• 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.

• AWS Network Firewall can inspect and block traffic across a VPC when traffic is routed through the firewall. A Drop action rule can deny traffic from the suspicious source IP.
• GuardDuty findings are sent to Security Hub.
• A Lambda function can be invoked when Security Hub receives a new GuardDuty finding, extract the source IP, and update the Network Firewall policy with a blocking rule.

• GuardDuty threat lists: Used to identify or increase the severity of findings; they do not directly block VPC traffic.
• AWS WAF: Protects supported web applications, such as CloudFront or Application Load Balancers. It does not provide general VPC-wide traffic blocking.
• GuardDuty suppression rules: Suppress or hide findings. They do not block the suspicious traffic.

• The application failing to start or becoming unhealthy
• An incorrect probe path, port, or protocol
• The application needing more startup time
• Dependency or configuration failures
• A liveness probe being too aggressive

bash 
kubectl describe pod <pod-name> 
kubectl logs <pod-name> --previous

• Scale to more replicas: may hide the issue but does not diagnose it.
• Decrease initialDelaySeconds: makes probes run earlier and could worsen startup failures.
• Drain and reboot the node: node metrics are normal, so rebooting is not justified as the first step.

• Disaster recovery backups
• Long-term backup retention
• Data archiving that still needs occasional access

• Multi-Regional Storage: Intended for frequently accessed data needing broad geographic availability; it costs more.
• Regional Storage: Suitable for frequently accessed data within one region.
• Nearline Storage: Intended for data accessed roughly once per month, such as monthly backups.
• Coldline Storage: Better fit for backups accessed only during a disaster or occasional recovery event.

• Identified risks
• Risk owners or responsible parties
• Likelihood and impact
• Risk response plans
• Current status
• Risk thresholds or trigger conditions

• Risk tolerance: The amount and type of risk an organization is willing to accept; it is not primarily a tracking document.
• Risk transfer: Moving the financial or operational impact of a risk to another party, such as through insurance or outsourcing.
• Risk analysis: The process of evaluating a risk’s likelihood and impact.

1. Get Data > Folder

1. Expand the Attributes column

1. Remove the Content column

• Folder connector: lists files and their metadata, but also includes a Content binary column.
• Combine Files: processes and combines the actual CSV contents, which is explicitly disallowed here.

• Link-local — FE80::/10; valid only on the local link/subnet and not routed by routers.
• Multicast — FF00::/8; identifies a group of interfaces rather than a private address range.
• Global unicast — 2000::/3; publicly routable IPv6 addressing.

• Asynchronous inference is designed for applications that submit individual inference requests, place them in a queue, and retrieve results later. It is useful when requests may take a long time or when traffic is intermittent.
• Batch transform is designed specifically to run inference over an entire large dataset stored in Amazon S3 and produce an output dataset.

• Large dataset/offline processing ? Batch transform
• Large or slow individual requests processed later ? Asynchronous inference

• 50% memory utilization baseline: The environment can be provisioned with excess capacity, leaving room for growth and workload spikes.
• Storage scalability: Cloud storage can generally be expanded as needed without redesigning the physical platform.
• Single circuit failure resilience: A properly designed IaaS provider uses redundant power circuits and infrastructure, so failure of one circuit should not take the platform offline.

• A — Dual PDUs: Helps with power redundancy but does not provide storage scalability.
• C — Network load balancing: Provides network-path resilience, not memory or storage scalability, and does not address a power-circuit failure.
• D — Multiple NAS devices: May improve storage capacity, but does not inherently provide power resilience or the required compute-capacity baseline.

• A — Tables
• B — Named file formats
• C — Schemas
• E — Databases

Why these are correct

• Tables: Can be cloned using CREATE TABLE ... CLONE. The clone initially uses zero-copy cloning, so storage is shared until changes are made.
• Named file formats: Can be cloned as database objects, preserving the file-format definition.
• Schemas: A schema and its eligible objects can be cloned.
• Databases: A database and its eligible schemas and objects can be cloned.

Why the others are incorrect

• Shares: A share is an account-level data-sharing object, not a normal cloneable database object.
• Users: Users are security principals and cannot be cloned with CLONE.

• 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.

• 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.

• Minimum
• Maximum
• Average
• Count
• Null/empty values
• Distinct values

• Enable column profile
• Enable details pane
• Show column profile in details pane

• Database data: Aggregates are used to retrieve structured data, commonly from entities and database sources.
• Screen scope: A Screen Aggregate belongs to the screen where it is defined and is not directly reusable as a screen-level aggregate elsewhere.
• Asynchronous and parallel execution: Multiple Screen Aggregates can run asynchronously and in parallel, helping the screen load data efficiently.

• User1 (user1@contoso.com) — This is an external guest in the Fabrikam tenant. Assuming the guest uses email one-time passcode authentication, Azure AD emails a passcode to user1@contoso.com.
• User2 (user2@outlook.com) — An Outlook.com address can authenticate with a Microsoft account, so Azure AD uses that account’s authentication rather than sending an email OTP. The fact that User2 has never accessed Fabrikam resources does not by itself trigger OTP.
• User3 (user3@fabrikam.com) — This is an internal Fabrikam user, so the user signs in with the organization’s normal credentials and does not receive a guest OTP.

• Accept: Acknowledging the risk without taking additional action.
• Transfer: Shifting the risk’s impact to a third party, such as through insurance or outsourcing.
• Mitigate: Reducing the likelihood or impact of the risk through security controls.
• Avoid: Eliminating the activity or process that creates the risk.

• The ERSPAN source is the device or interface whose traffic is being copied.
• The ERSPAN destination is the device receiving the encapsulated copy and forwarding it to the traffic analyzer.
• The local monitor session number is locally significant. Therefore, the source and destination do not need matching session IDs such as 47 or 48.
• The erspan-id identifies the ERSPAN stream and must correspond between the source and destination configuration.

• New investment: $536,500
• Resulting NPV: $0

• 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.

• 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

• Configure row-level security (RLS) using the sales region and the HR manager’s identity.
• Publish the reports and datasets through a Power BI app.
• Grant the HR managers permission to use the app, but do not add them as workspace members.
• App users can view and interact with reports while RLS limits them to their region.
• They cannot modify the report layout because they do not have workspace editing permissions.

• Adding them to the workspace gives them workspace permissions that may allow report or dataset changes, depending on the assigned role.
• Creating copies is unnecessary and creates maintenance and security problems.
• Hosting datasets and reports in different workspaces does not, by itself, provide the required controlled access.

• 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.

• Supervised learning requires labeled examples to learn how to classify customers.
• Reinforcement learning learns through actions, rewards, and penalties, which is not the main task here.
• RLHF uses human feedback to improve model behavior, typically for generative AI, not customer segmentation.

• A valid input returns the expected result
• An invalid input raises an error
• Boundary values are handled correctly

• B: Copilot may help generate tests, but it does not automatically write all necessary tests.
• C: Writing the implementation does not verify that it works correctly.
• D: Copilot can suggest test commands or test code, but the developer or development environment must execute and validate the tests.

• 16GB RAM: A 64-bit OS is needed to use more than roughly 4GB effectively, but RAM capacity does not prove the CPU supports 64-bit operation.
• 500GB HDD: Storage capacity has no bearing on OS architecture.
• 1Gbps Ethernet: Network speed is unrelated to CPU architecture.

1. Create a temporary Onboarding OU.
2. Move the new account into it.
3. Move the restrictive SCPs from the organization root to the Production OU.
4. Allow the account to update AWS Config.
5. Move the account into Production after onboarding.

• An SCP attached at the organization root applies to every account and OU beneath it.
• An SCP attached to the Production OU applies only to accounts in that OU.
• Therefore, moving the restrictive SCP from the root to Production temporarily exempts the onboarding account without weakening the policies for existing production accounts.
• Once onboarding is complete, moving the account into Production subjects it to the normal restrictions.

• A — Too infrequent: Formal strategies are often reviewed only every three to five years. This can make the organisation slow to respond to rapid changes in technology, competitors, regulation or customer needs.

• C — Relies on predicting the future: The approach assumes senior managers can make reasonably reliable forecasts and assumptions. In uncertain environments, these assumptions may quickly become invalid.

• E — Can restrict radical innovation: Formal planning tends to favour existing information, established objectives and incremental improvements. This may discourage experimental or highly innovative strategies that emerge from lower levels of the organisation.

• B concerns the difficulty of achieving competitive advantage through core competences when competing with focused specialists. It is not specifically a drawback of the formal top-down planning process.
• D is an activity organisations should undertake—continuous monitoring—not a drawback of the approach itself.

• Accept: Acknowledging the risk without taking additional action.
• Transfer: Shifting responsibility or financial impact to another party, such as through insurance or outsourcing.
• Mitigate: Reducing the likelihood or impact of the risk through controls.
• Avoid: Eliminating the activity or condition that creates the risk.

• Distinct values = 69

• Unique values = 4

• Streams — track change data for tables or views.
• External tables — reference data stored in an external cloud storage location.

• Role — account-level security object.
• Warehouse — account-level compute resource.
• User — account-level security object.
• Share — account-level object used to share data.

• Filtering harmful or inappropriate content
• Blocking specified topics
• Detecting and masking sensitive information
• Enforcing content policies suitable for children

• Amazon Rekognition analyzes images and videos; it does not control text generated by a language model.
• Amazon Bedrock playgrounds are for testing models and prompts, not enforcing production safety policies.
• Agents for Amazon Bedrock help models complete tasks and use tools; they do not primarily provide content-safety controls.