Cisco 400-007 Exam Prep
Cisco Certified Design Expert CCDE v3.0 (Page 17 )

• 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.

• Frequency/likelihood = how often or how likely the risk is to occur.
• Severity/impact = how damaging or costly the consequences would be.

• A minor equipment fault occurring frequently may have high frequency but low severity.
• A major cyberattack occurring rarely may have low frequency but high severity.
• A risk that happens often and causes substantial losses has high frequency and high severity.

• Disabling a compromised account
• Blocking a malicious IP address
• Isolating an endpoint
• Creating or escalating an incident ticket

• Virus signatures: Primarily associated with antivirus or endpoint protection.
• Security agent deployment: Usually handled by endpoint-management or security-agent platforms.
• Vulnerability scanning: Typically performed by vulnerability-management tools.

• Determining ISP-assigned address space: Usually involves public reconnaissance, such as WHOIS or regional Internet registry information.
• Bypassing DNS sinkholing: Would require evidence that malicious domains are resolving outside the organization’s sinkhole controls.
• Achieving initial access: DNS log entries alone do not demonstrate compromise or entry.
• Exfiltrating data: DNS tunneling generally involves repeated queries containing encoded or unusually long data, not merely internal host discovery.

• Required response and resolution times
• Availability or uptime targets
• Performance standards
• Support obligations
• Remedies or penalties for failing to meet the terms

• SOW (Statement of Work): Defines the project’s scope, tasks, deliverables, and schedule.
• MOA (Memorandum of Agreement): Describes agreed responsibilities between parties, often for a specific activity.
• MOU (Memorandum of Understanding): Records a mutual understanding or intention, usually less formal than a contract.

• 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.

• 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.

• A: Concerns task scheduling and avoiding missed runs, not directly sizing the warehouse.
• B: The amount of data in a stream alone does not reliably determine compute needs; the SQL processing is what matters.
• D: A multi-cluster warehouse addresses concurrency, not the compute size needed for one task execution.

• Install unauthorized or unsupported software
• Access features blocked by the manufacturer or operating system
• Customize system behavior beyond normal settings

• Warehouse size: Larger warehouses use more credits per unit of time than smaller ones.
• Number of clusters: A multi-cluster warehouse can run multiple clusters, and each active cluster consumes credits. Therefore, increasing the maximum or active cluster count can increase consumption.

• Number of users: More users may cause more workload, but users themselves do not consume credits.
• Amount of data processed: Snowflake warehouses are not charged directly by the amount of data scanned. However, processing more data may cause the warehouse to run longer, indirectly increasing credits.

• 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.

• Azure DevOps Analytics views are accessed through the Power BI Azure DevOps (Boards only) connector.
• This connector authenticates directly to Azure DevOps Services and lets you select an Analytics view.
• Analytics views allow filtering and shaping data in the cloud before Power BI loads it.
• Azure DevOps Server (Boards only) is for an on-premises Azure DevOps Server instance, not Azure DevOps cloud.
• A generic OData Feed connection can access OData endpoints, but it is not the purpose-built connector for Azure DevOps Analytics views.
• “OData queries” describes the underlying technology or query approach, not the most appropriate Azure DevOps connector choice here.

• Prevention costs – costs incurred to stop defects occurring, such as staff training, quality planning and supplier evaluation.
• Appraisal costs – costs of checking whether products meet standards, such as inspection, testing and quality audits.
• Internal failure costs – costs of defects found before delivery to customers, such as scrap, rework and retesting.
• External failure costs – costs of defects found after delivery, such as warranty claims, returns, complaints and product recalls.

• Crash cart: Provides local physical access, not remote access.
• Jump box: A secured intermediary used to reach devices over the normal network; it may be inaccessible if the appliance has not booted.
• Secure Shell (SSH): Requires the device’s operating system and network services to be running properly.

• The users currently have Office 365 Enterprise E3 licenses assigned directly.
• Assigning Microsoft 365 Enterprise E5 through a group does not automatically remove their directly assigned E3 licenses.
• From the Licenses blade, you can select the E3 product and remove it from all assigned users in one operation. This requires less effort than editing 2,500 users individually.

• Identity Governance manages access reviews, entitlement management, and related governance—not bulk license removal.
• Set-AzureAdUser does not remove product licenses.
• Set-WindowsProductKey applies Windows product keys and is unrelated to Microsoft 365 licensing.

• 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.

• 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.

• Accept: Acknowledging the risk without taking additional action.
• Transfer: Moving the risk’s financial impact to a third party, such as an insurer.
• Mitigate: Reducing the likelihood or impact through controls, such as patching or employee training.
• Avoid: Eliminating the activity that creates the risk.

• 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.

• 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.

• 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.

• 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.

• 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.

• C — Before update
• D — Before insert

apex 
trigger AccountTrigger on Account (before insert, before update) { 
    for (Account acct : Trigger.new) { 
        acct.Description = 'Updated automatically'; 
    } 
}

• In after insert and after update, Trigger.new records are read-only.
• Use after triggers when you need the record’s final database Id or want to create/update related records.
• Attempting to change a field directly in an after trigger causes a read-only error.

• The message came from the claimed sender (authentication)
• The message was not altered (integrity)
• The sender cannot easily deny sending it (non-repudiation)

• Attackers can send unsigned phishing emails.
• Attackers may compromise a legitimate account and send signed malicious emails.
• Users may still be tricked by a validly signed message.

• 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.

• 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.

• 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.

• 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.

• Keep earlier prompt versions.
• Identify the last version that produced acceptable output.
• Restore it as a baseline.
• Make small, controlled changes—ideally one significant change at a time.
• Test the output after each change.

• A: Discards useful information about what worked.
• B: Large simultaneous changes make it difficult to identify the cause of problems.
• D: Accepts output that does not meet the requirements.

• Reconnaissance: Gathering information about the target.
• Weaponization: Creating or combining a malicious payload with a delivery method.
• Delivery: Sending the payload, such as through phishing.
• Exploitation: Triggering a vulnerability or weakness to gain execution or access.

• IRR assumes that interim cash flows can be reinvested at the project’s own IRR. This may be unrealistic, especially when the IRR is very high.
• MIRR assumes reinvestment at a specified rate, usually the company’s cost of capital or another realistic reinvestment rate.

• B: MIRR does not specifically favour projects with long payback periods.
• C: MIRR and IRR can rank competing projects differently.
• D: MIRR is not always higher than IRR; it may be higher or lower depending on the cash flows and reinvestment rate.

1. Select Exchange email as a location.
2. Use the built-in Azure Storage Account key sensitive information type as the condition.
3. Configure the DLP action to encrypt the email—for example, using Microsoft Purview Message Encryption.

• AWS AppSync is designed for serverless applications that need to retrieve and combine data from multiple sources.
• A pipeline resolver can invoke multiple functions or data-source resolvers, including resolvers connected to different DynamoDB tables.
• The application can receive the combined result through one GraphQL request, without requiring custom aggregation logic in the application.
• This is operationally efficient because AppSync manages the API layer and resolver execution.

• Athena Federated Query is intended primarily for analytics and ad hoc querying, not low-latency application data retrieval.
• Querying DynamoDB through Athena can consume DynamoDB read capacity and may affect table performance.
• It also introduces analytical-query overhead and is not the normal choice for a microservice web application.

• 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

• SQL injection
• Cross-site scripting (XSS)
• Malicious or malformed web requests
• Some application-layer attacks

• Layer 4 firewall: Filters based mainly on IP addresses, ports, and transport protocols; it cannot deeply inspect web application content.
• NGFW: Provides advanced network filtering and may include application awareness, but a WAF is more specifically designed to protect web applications.
• UTM: Combines several security functions in one appliance, but it is not the most targeted solution for a web portal.

• NIDS: Monitors network traffic for suspicious activity and network-based attacks targeting the application or its supporting services.
• HIPS: Protects an individual host by monitoring processes, files, and system activity. It can help secure the web server, but it is not focused specifically on the application’s network attack surface.
• SIEM: Aggregates and correlates logs; it does not directly prevent attacks.
• Honeypot: A decoy system, not the primary protection for a production web application.
• Certificate revocation list: Deals with certificate trust, not application exploitation.

• 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.

• 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.

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.

• The selling division receives a credit based on a favourable transfer price, often market price or full cost plus a profit margin.
• The receiving division is charged a lower amount, such as marginal cost or another agreed price.
• The difference is accounted for centrally by head office or a corporate adjustment.

• A — Use a custom controller: An Apex class can contain the page’s logic.
• C — Use a standard controller with extensions: The standard controller provides basic CRUD behavior, while an extension adds custom logic.
• E — Use a standard controller: Useful when the page works mainly with one Salesforce object and needs standard functionality.

• A page with no controller for static or controller-free content.
• A custom controller with extensions; extensions are not limited only to standard controllers.

• A: Not expanding would increase its dependence.
• B: More business from the same corporation would make dependence worse.
• D: Passing work to other minority businesses does not solve its own dependence.
• E: The passage does not suggest that it can force or influence the corporation to give work to others.

1. Append Table1 and Table2 as a new query

1. Select the product column in the appended query.

1. Choose Remove Rows > Remove Duplicates

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

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

• Maintain head office control: Transfer pricing generally supports decentralisation rather than direct head-office control.
• Establish centralised decision making: This conflicts with divisional autonomy.
• Develop a top-down culture: Transfer pricing is intended to support empowered divisions and goal congruence, not impose purely top-down management.

• 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.

• Deploy five individual VMs: This does not create a VM scale set.
• Modify Availability Zones or Size settings: These actions do not create the required scale set and add unnecessary steps.
• VM orchestration mode: This is Flexible orchestration, intended for more individually managed VMs and mixed configurations. It is not the best choice for a straightforward group of identical instances.

• What information each stakeholder needs
• When and how often it should be communicated
• Which format or channel should be used
• Who is responsible for communicating it

• A: The team should not decide stakeholder communications without first checking the established plan and CEO’s needs.
• B: An executive summary may be appropriate, but choosing a solution before reviewing the plan is premature.
• D: The stakeholder engagement plan addresses how to engage stakeholders, but this is primarily a communication planning issue.