Nutanix NCS-CORE Exam Prep
Nutanix Certified Services - Core (NCS-Core) 6.8 (Page 4 )

• Red team: Offensive security; attempts exploitation and simulates attackers.
• Blue team: Defensive security; monitors, detects, and responds to attacks.
• Purple team: Combines red- and blue-team activities to improve defenses, but is not the best direct answer here.
• White team: Usually oversees or coordinates an exercise, including rules of engagement and scope.

• A — VARIANT "null" values are not the same as SQL NULL values: True

• B — Frequent, single-row DMLs: False

• C — Validate data before loading into the target table: True

• D — Use staging tables to manage MERGE statements: True

• Two-part tariff pricing: $10 per unit
• Dual pricing: $20 per unit

Two-part tariff pricing

Dual pricing

• Division X, the seller, is credited with the market price: \(\$20\)
• Division Y, the buyer, is charged the marginal cost: \(\$10\)

• 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.

• 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.

1. Define – establish the project’s objectives, scope, feasibility and requirements.
2. Design – plan how the project will be carried out, including resources, schedules and controls.
3. Deliver – execute the project and produce the required outputs.
4. Develop – close the project formally and capture learning for future projects.

• Filing documentation and records
• Closing project and accounting systems
• Settling costs
• Handing over the completed work and obtaining sign-off
• Reviewing performance and giving feedback

• The listener uses a virtual IP address shared by the availability-group replicas.
• Floating IP, also called Direct Server Return (DSR), allows the SQL Server nodes to respond using the listener’s IP address rather than their individual VM IP addresses.
• This is required for the availability-group listener to function correctly with an Azure internal load balancer.

• Crowd-sourced evaluation: Requires recruiting and managing many evaluators.
• Human-worker evaluation: Provides human judgment but requires coordination, labeling instructions, and quality control.
• RLHF: Is a training and alignment process, not simply a low-overhead model comparison method.
• Automatic model evaluation: Quickly evaluates outputs using predefined toxicity or quality metrics with minimal manual effort.

• Employees can work from any location, including remote sites and offices worldwide.
• Login can be relatively seamless after the device is registered.
• It does not require distributing additional physical authentication equipment.

• Geotagging: Identifies location, but location alone is not a strong authentication method and may prevent legitimate remote access.
• Smart cards: Provide strong authentication but require card readers and physical cards, increasing equipment and management requirements.
• Time-based logins: Typically involve one-time codes that users must enter, making the experience less seamless and potentially requiring a token or authenticator app.

• 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.

• Customer information must not be revealed ? PII Detection
• Malicious instructions embedded in images/documents must be blocked ? Prompt Shields

• If the question allows multiple selections, choose PII Detection and Prompt Shields.
• If it requires one selection specifically for the image-based threat, choose Prompt Shields.

• DB1 (500 TB): Azure Data Box Heavy

• Virtual machines: Azure Migrate

• 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.

• Azure Resource Manager (ARM) templates define the virtual machines and related Azure resources declaratively. You can deploy the same template repeatedly, ensuring consistent VM sizes, networking, storage, extensions, and other infrastructure settings.

• PowerShell DSC extension applies and maintains the required Windows configuration inside each VM. DSC is state-based: if a setting changes or becomes incorrect, DSC can bring it back to the desired configuration.

• Azure Pipelines deployment groups organize deployment targets, but they do not primarily enforce ongoing VM configuration.
• Custom Script Extension runs setup scripts, but it is generally less maintainable and does not continuously enforce configuration like DSC.
• Pipeline stage templates reuse pipeline workflow definitions; they do not configure the VMs themselves.

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.

• C series: CPU-optimized instances; not designed primarily for LLM training.
• G series: GPU instances generally optimized for graphics and some machine learning workloads, but not specifically large-scale model training efficiency.
• P series: Powerful GPU instances for ML training, but typically consume more energy than Trainium-based instances for suitable training workloads.

• Accept: Acknowledges the risk without taking additional action.
• Transfer: Shifts the risk or financial impact to another party, such as an insurer.
• Mitigate: Reduces the likelihood or impact through controls like patching, MFA, or segmentation.
• Avoid: Eliminates the activity that creates the risk.

• Create a CodeCommit repository in the secondary Region.
• Use CodeBuild to run a Git mirror operation from the primary repository to the secondary repository.
• Use EventBridge to detect relevant repository events, such as a pull request being merged.
• Invoke a Lambda function, which starts the CodeBuild project.

• B: Amazon S3 is object storage, not a Git repository that developers can use as a CodeCommit remote.
• C: CodeArtifact is intended for packages and artifacts, not source-code repository mirroring.
• D: An AWS Cloud9 environment is not an appropriate automated replication mechanism and would require manual or persistent infrastructure management.

• 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.

• C: Develop the rule in the Rules Editor, define the events, match, and condition sections, then use the test rule feature. This directly validates the YARA-L rule without enabling it in production.
• A: UDM Search can help investigate data and refine filters, but it does not fully test YARA-L logic—especially complex event joins, match windows, and conditions.

• Microsoft Purview portal: Create and manage sensitivity labels and configure support for sensitivity labels used with Office files in SharePoint and OneDrive.
• SharePoint admin center: Primarily manages SharePoint settings. Older guidance and PowerShell procedures used SharePoint tenant configuration, which can make this question confusing.
• IRM: Concerns Information Rights Management and is a separate protection mechanism; it should not be used to identify the portal for configuring Microsoft Purview sensitivity labels.

• Admin access to the deployment pipeline
• Contributor access to the Development and Test workspaces
• Viewer access to the Production workspace

• Active remediation actions role in Microsoft Defender for Endpoint

• Security Reader role in Azure AD/Entra ID

• Security Administrator is broader than necessary because it grants extensive security-management permissions, violating least privilege.
• Compliance Data Administrator is intended for compliance data management, not Defender for Endpoint remediation approvals.

• B — The most significant risks are held by SQ.

• C — SQ should recognise the cars as inventory.

• F — SQ recognises revenue when LM sells a car to a third party.

• A: LM does not hold the most significant risks.
• D: LM does not recognise the cars as inventory.
• E: SQ should not recognise revenue when cars are merely delivered to LM.

• Ramen is a Linux worm associated with Red Hat Linux 6.2 and 7.0 systems.
• It spreads by scanning randomly generated IP addresses using tools such as synscan.
• It exploits vulnerable network services, including web-related services, and may leave malicious files or altered web content.
• The tar.gz file is also a clue because it is a common Linux archive/package format.

• Code Red: Primarily targeted Microsoft IIS servers running Windows.
• LoveLetter: Spread mainly through email attachments.
• Nimda: A Windows-based worm that spread through email, web servers, and network shares.

• MFA adds an additional authentication factor, such as a phone notification or verification code.
• It helps prevent unauthorized sign-ins, even if a password is compromised.
• It does not encrypt, store, or protect credentials used by deployment processes.

• Existing measures
• Relationships
• Calculated columns
• Tables and fields
• Existing transformations in the model

• SharePoint folder: Reconnects to the source files. You would likely need to recreate the model and measures.
• Power BI dataflows: Primarily provide reusable data preparation and storage, but they do not automatically provide the existing semantic model and measures.
• Excel workbook: Connects directly to the source file and would also require rebuilding the model and measures.

• The JSON files contain one billion items, so this is a big-data workload.
• PySpark uses Apache Spark’s distributed, parallel processing, allowing the data to be processed across multiple compute nodes.
• It can read and transform data directly from OneLake, helping minimize unnecessary data movement or duplication.
• A Fabric notebook can also support exploration and visualization, as well as Spark-based analytics and anomaly-detection workflows.

• Pandas is primarily in-memory and generally suited to smaller datasets. One billion records would be impractical on a typical single machine.
• A Power BI report with core visuals is appropriate for visualization and sharing, but it is not the best tool for large-scale transformation, parallel processing, or anomaly detection. It would normally consume prepared data rather than perform the entire workload.

• 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.

• Label applied does not automatically exclude a file from Copilot.
• Encryption/usage rights determine whether Copilot can access the file’s content.
• If the user has no permission under Label2, File3 should be unavailable to Copilot for that user.
• File3’s label might be inherited by derived content only if File3 was actually processed and contributed by an authorized user; it should not be treated as contributing merely because it exists in the source set.

• No servers or database infrastructure to provision.
• Uses standard SQL.
• Supports formats such as JSON, CSV, Parquet, and ORC.
• You typically define the table structure with AWS Glue Data Catalog or Athena’s own schema definitions.
• Charges are generally based on the amount of data scanned, so partitioning and columnar formats can reduce cost.
• It does not copy the data into a separate database; it reads the files in S3 when you run a query.

• Orders contains order-level information, such as the order date.
• Order Line Items contains the products, quantities, and line amounts associated with each order.
• Merging them on the order key, such as OrderID, places the order date alongside each line item. This allows you to calculate total order value and analyze it over time.
• Product attributes can remain in the Products table and be related to the resulting line-item table through ProductID.

• Merging line items with products addresses product attributes but does not first provide the order-level date and value needed for time analysis.
• DAX calculated columns or lists add unnecessary model complexity.
• Calculating order counts is not the first requirement and does not combine the required order and line-item 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.

• terraform apply
• terraform destroy

• Atomicity: a write fully succeeds or does not appear.
• Consistency: table state remains valid.
• Isolation: concurrent operations do not corrupt one another.
• Durability: committed changes persist.

• Flexible schemas: common data lakes often support flexible or schema-on-read data.
• Open-source formats: common data lakes commonly use open formats such as Parquet.
• Scalable storage: cloud data lakes already provide scalable storage.

• 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.

• Azure Resource Manager (ARM) templates
• Or, in newer Azure terminology, Bicep files

• Employees can use approved devices from remote locations or worldwide offices.
• Users do not need to carry or insert extra hardware.
• It helps prevent account takeovers from unknown devices.

• Geotagging: Restricts access based on location, which could interfere with remote work and travel.
• Smart cards: Require additional physical equipment and card readers.
• Time-based logins: Limit access according to time but do not strongly bind access to an authorized user device.

• 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.

• 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.

text 
ip route vrf Customer1 172.16.1.0 255.255.255.0 172.16.1.1 global 
ip route vrf Customer2 192.168.1.200 255.255.255.255 192.168.1.1 global 
ip route 192.168.1.0 255.255.255.0 Vlan10 
ip route 172.16.1.0 255.255.255.0 Vlan20

• The route in Customer1 VRF provides a path back to the Customer2 subnet, 172.16.1.0/24.
• The route in Customer2 VRF provides a path to the FTP server, 192.168.1.200.
• The keyword global tells the router to resolve the next hop using the global routing table, enabling controlled route leaking between the VRF and global table.
• The global routes identify the VLAN interfaces used to reach each customer network.

• 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.

• 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.

1. Install a Cassandra-compatible ODBC driver.
2. Configure a data source name (DSN), if required.
3. In Power BI Desktop, select Get Data > ODBC.
4. Choose the configured DSN and connect.

• Microsoft SQL Server database: Connects to SQL Server, not Cassandra.
• OLE DB: Requires a compatible OLE DB provider; this is not the standard choice for Cassandra in Power BI.
• OData: Requires Cassandra data to be exposed through an OData service, which is not the normal direct connection method.

• 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.

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.

• The product is already in production.
• The organization is examining the product’s functions and cost drivers.
• The objective is to reduce cost without reducing perceived value or required quality.

• Kaizen costing focuses on continuous, usually incremental, cost reductions during the manufacturing stage. It is not specifically the systematic examination of a product’s functions.
• Standard costing compares actual costs with predetermined standards; it is a control technique, not a product redesign method.
• Process innovation concerns developing significantly improved production processes, rather than analysing the product’s functions and value.

• Record changes — a flow can start when a record is updated or otherwise changes.
• Schedules — a flow can run at a specified date/time or on a schedule.
• Record inserts — a flow can start when a new record is created.

• Subflows are reusable flow components called by another flow; they are not normally trigger types.
• Record views do not generally initiate a Flow Designer flow.

• Base table — the table containing common inherited fields
• Parent table — the table directly extended by the child
• Other tables in the hierarchy

• 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.