Huawei H35-210_V2.5 Exam Prep
HCIA-Access V2.5 (Page 23 )

• Minimum and maximum values
• Number of distinct values
• NULL information
• Other metadata used for pruning

• 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.

• \Pipeline1
• \Folder1\Lakehouse1

• Pipeline1 remains at the workspace root in Workspace1, so it is deployed to the root of Workspace2.
• Lakehouse1 was moved into Folder1, so its full path is preserved as \Folder1\Lakehouse1.
• Fabric deployment pipelines preserve folder paths because an item’s full path is treated as part of its name.

• File is shared externally ? Access level

• File is labeled “Internal only” ? Sensitivity label

• Data Cloud combines data from multiple systems.
• It resolves and organizes that data into a unified customer view.
• That unified view enables downstream actions such as personalized campaigns, segmentation, analytics, and activation.

• A = core platform value
• D = a resulting business use case or benefit

• Windows 11 Pro ? Windows 11 Enterprise
• Windows 10 Pro ? Windows 10 Enterprise

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

• Structure requirements documents
• Draft user stories and acceptance criteria
• Create templates for functional and nonfunctional requirements
• Turn natural-language descriptions into organized documentation or implementation examples

• A: “Automatically generating detailed requirements documents” overstates Copilot’s role; it assists but does not independently determine complete requirements.
• C: /help provides guidance about Copilot features, not reliable requirements-conflict detection.
• D: Stakeholder communication and meetings remain human responsibilities.

• 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.

1. Deployment type: Standard

1. Version update policy: Opt out of automatic model version upgrades

• 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.

• 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.

• Undefined or blank values
• Relationship matching problems
• Duplicate or conflicting keys
• Errors when querying the model

• Normalize all values to upper- or lowercase in the source query or Power Query.
• Add an index or surrogate key when values that differ only by case must remain distinct.

DAX 
Previous Year Sales = 
CALCULATE( 
    [Total Sales], 
    PARALLELPERIOD('DATE'[Month], -1, YEAR) 
)

• CALCULATE evaluates [Total Sales] after modifying the filter context.
• PARALLELPERIOD shifts the current date period. -1 moves it backward, and YEAR specifies a one-year shift.
• 'DATE'[Month] is the date column used for the time calculation. In a proper date table, this should be a real date column or an appropriate calendar column—not merely text such as "January".

• 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 flooding.
• NGFW: Filters routed network traffic, while this attack targets Layer 2 switching behavior.

1. The specific table — for example, incident
2. The parent table — for example, task
3. The wildcard table — *, which applies broadly

• A. Continue using the current system: It will eventually fail and offers no recovery strategy.
• B. Virtualize the entire system: Correct. A VM can preserve the old OS, application, settings, and dependencies while running on newer physical hardware.
• C. Containerize the application: Containers generally share the host OS kernel and are not suitable for preserving an entire incompatible legacy OS.
• D. Clone the disks to USB: A disk clone may provide backup or temporary recovery, but it does not solve hardware compatibility or provide a reliable long-term platform.

• 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.

• Tokens: Individual units of text processed by a model, such as words, subwords, or characters.
• Models: Systems that learn patterns and generate predictions or outputs.
• Binaries: Data represented using the binary number system, not semantic representations.

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

• 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.

• 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.

• Three DHCP scopes: Each VLAN has its own IPv4 subnet. DHCP scopes are subnet-specific, so create one scope for:

• Two DHCP relays: DHCP client discovery begins as a broadcast, and broadcasts do not cross routers or VLAN boundaries. Therefore, configure a DHCP relay on the router or Layer 3 switch interface for each client VLAN.

• Amazon EC2 instances — ???????? ?? ?? ??????? CPU, memory ??? instance size ???????? ???????
• AWS Lambda functions — memory configuration ?????????? ???????
• Amazon EBS volumes — volume type ?? size optimize ???? ???????
• ??????? ECS workloads — resource configuration ? cost optimization-?? ???????

• Memory ? compute usage ?????????? ???? custom code ????? ???? development ? maintenance ??????
• Cost allocation tags ???? ????? ??? ?????? ???? ??????? ???; inefficient resource ??????? ?? recommendation ???? ???
• CloudTrail resource ???? ?? API activity ??????, resource utilization ????

1. It examines the source MAC address and learns it on the incoming port.
2. It examines the destination MAC address.
3. If the destination MAC is already in the MAC/CAM table, it forwards the frame only through the associated port.
4. If the destination MAC is unknown, it performs unknown-unicast flooding:

• terraform apply
• terraform destroy

• COE: Identifies the Center of Excellence, such as Payroll or Benefits.
• Topic Detail: Classifies the HR service within its COE; it does not identify the catalog item.
• Template: Supplies default values or case configuration.
• Record Producer: Represents the catalog item that employees use to submit the request and create the case.

• Transport and handling
• Storage and warehousing
• Refrigeration
• Shelf-stacking or replenishment
• Other costs directly attributable to product P

• 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.

• The vulnerability scan identifies a vulnerability.
• The administrator verifies that the vulnerability is not present.
• Therefore, the scan produced a false positive.

• True positive: A vulnerability is reported and genuinely exists.
• False negative: A vulnerability exists, but the scan fails to detect it.
• True negative: No vulnerability exists, and the scan correctly reports no issue.

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

• A. Periodically rotate responsibilities: May reduce some risks, but it does not directly detect unauthorized changes.
• B. Review segregation-of-duties conflicts: Identifies incompatible access combinations, not whether a specific data change was unauthorized.
• C. Log changes in an outside application: Logs provide evidence, but someone must review and analyze them. Logging alone does not detect misuse.
• D. Independent review: Directly detects unauthorized changes and reduces the risk that the person making the change can conceal it.

• D — Managers focus on keeping costs low in the short term to maximise profits.

• E — Managers are given incentives to meet or undercut budgets.

• F — Managers delegate responsibility simply to reduce their workload.

• A: CEO-sponsored formal policy — demonstrates executive support, but sponsorship at one point in time may not sustain commitment.
• B: Security awareness training — primarily improves employee behavior, not senior management commitment.
• C: Periodic alignment reviews — ongoing reviews show that security remains relevant, supports business priorities, and uses resources appropriately.
• D: Senior management signoff on the strategy — useful for governance and accountability, but signoff is generally a one-time event.

• Existing Eventhouse data: remains available only in the KQL database.
• Data ingested after enabling the feature: is copied to OneLake and can be accessed through OneLake file explorer.
• New databases: are irrelevant; the setting applies to the selected KQL database.

• 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.

• C. The agent orchestration method
• D. The average session time per agent

• Orchestration method: Classic and generative orchestration can have different processing and credit-consumption patterns. Changing the method can affect estimated operating costs.
• Average session time: Longer sessions generally involve more interaction processing, which affects projected usage, credits, and ultimately ROI.

• GPT models used: Important for technical design, but not the primary usage-estimator factor identified in this scenario.
• Average characters in a chat message: Message length alone is not the key business-use projection requested here.

• 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.

• A: Describes an update-set conflict, not a Dictionary Override.
• B: Describes a configuration item or change concept.
• C: Describes an approval or workflow activity.

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.

• ROUGE-N: matching word sequences, such as unigrams or bigrams
• ROUGE-L: matching the longest common subsequence
• ROUGE-Recall: how much of the reference content appears in the generated summary

• F1 score: A general classification metric based on precision and recall; not specifically designed for summarization.
• Perplexity: Measures how well a language model predicts text, rather than how closely a generated summary matches a reference.
• Fréchet Inception Distance (FID): Typically evaluates similarity between generated and real images, not text.