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CompTIA CV0-002 : CompTIA Cloud+ Certification Exam

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Test Number : CV0-002
Test Name : CompTIA Cloud+ Certification
Vendor Name : CompTIA
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CV0-002 test Format | CV0-002 Course Contents | CV0-002 Course Outline | CV0-002 test Syllabus | CV0-002 test Objectives

Exam Name CompTIA Cloud+
Exam Code CV0-002
Duration 90 mins
Number of Questions 90
Passing Score 750 / 900

Configuration and Deployment 24%
1. Appropriate commands, structure, tools, and automation/orchestration as needed
2. Platforms and applications
3. Interaction of cloud components and services
Network components
Application components
Storage components
Compute components
Security components
4. Interaction of non-cloud components and services
5. Baselines
6. Target hosts
7. Existing systems
8. Cloud architecture
9. Cloud elements/target objects

1. Apply the change management process
2. Refer to documentation and follow standard operating procedures
3. Execute workflow
4. Configure automation and orchestration, where appropriate, for the system being deployed
5. Use commands and tools as needed
6. Document results
1. Underlying environmental considerations included in the testing plan
Shared components
Production vs. development vs. QA
High availability
Data integrity
Proper function
Load balancing
2. Testing techniques
Vulnerability testing
Penetration testing
Load testing
1. Consider success factor indicators of the testing environment
Data integrity
Proper functionality
2. Document results
3. Baseline comparisons
4. SLA comparisons
5. Cloud performance fluctuation variables
1. Cloud deployment models
2. Network components
3. Applicable port and protocol considerations when extending to the cloud
4. Determine configuration for the applicable platform as it applies to the network
Address space required
Network segmentation and microsegmentation
5. Determine if cloud resources are consistent with the SLA and/or change management requirement
1. Available vs. proposed resources
2. Memory technologies
Bursting and ballooning
Overcommitment ratio
3. CPU technologies
Overcommitment ratio
4. Effect to HA/DR
5. Performance considerations
6. Cost considerations
7. Energy savings

Dedicated compute environment vs. shared compute environment
1. Requested IOPS and read/ write throughput
2. Protection capabilities
High availability
Failover zones
Storage replication
Synchronous and asynchronous
Storage mirroring
Redundancy level/factor
3. Storage types
Object storage
4. Access protocols
5. Management differences
6. Provisioning model
Thick provisioned
Thin provisioned
Encryption requirements
7. Storage technologies
Deduplication technologies
Compression technologies
8. Storage tiers
9. Overcommitting storage
10. Security configurations for applicable platforms
User/host authentication and authorization
1. Migration types
Storage migrations
Online vs. offline migrations
2. Source and destination format of the workload

Virtualization format
Application and data portability
3. Network connections and data transfer methodologies
4. Standard operating procedures for the workload migration
5. Environmental constraints

Working hour restrictions
Downtime impact
Peak timeframes
Legal restrictions
Follow-the-sun constraints/time zones
1. Identity management elements
Access policy
Single sign-on
2. Appropriate protocols given requirements
3. Element considerations to deploy infrastructure services such as:
Certificate services
Local agents
Load balancer
Multifactor authentication
Security 16%
1. Company security policies
2. Apply security standards for the selected platform
3. Compliance and audit requirements governing the environment
Laws and regulations as they apply to the data
4. Encryption technologies
Other ciphers
5. Key and certificate management PKI
6. Tunneling protocols
7. Implement automation and orchestration processes as applicable
8. Appropriate configuration for the applicable platform as it applies to compute
Disabling unneeded ports and services
Account management policies
Host-based/software firewalls
Antivirus/anti-malware software
Deactivating default accounts
1. Authorization to objects in the cloud
2. Effect of cloud service models on security implementations
3. Effect of cloud deployment models on security implementations
4. Access control methods
Role-based administration
Mandatory access controls
Discretionary access contros
Non-discretionary access contros
Multifactor authentication
Single sign-on
1. Data classification
2. Concepts of segmentation and microsegmentation
3. Use encryption as defined
4. Use multifactor authentication as defined
5. Apply defined audit/ compliance requirements
1. Tools
Vendor applications
Cloud portal
2. Techniques
Custom programming
3. Security services
4. Impact of security tools to systems and services Scope of impact
5. Impact of security automation techniques as they relate to the criticality of systems

Scope of impact
Maintenance 18%
1. Scope of cloud elements to be patched
Virtual machines
Virtual appliances
Networking components
Storage components
2. Patching methodologies and standard operating procedures
Production vs. development vs. QA
Rolling update
Blue-green deployment
Failover cluster
3. Use order of operations as it pertains to elements that will be patched
4. Dependency considerations
1. Types of updates
Version update
2. Automation workflow
Runbook management
Single node
Multiple nodes
Multiple runbooks
3. Activities to be performed by automation tools
Shut down
Maintenance mode
Enable/disable alerts
Change block/delta tracking
2. Backup target
3. Other considerations
Backup schedule
1. DR capabilities of a cloud service provider
2. Other considerations
SLAs for DR
Corporate guidelines
Cloud service provider guidelines
Bandwidth or ISP limitations
Site mirroring
File transfer
Third-party sites
1. Business continuity plan
Alternate sites
Continuity of operations
Edge sites
Partners/third parties
2. SLAs for BCP and HA
1. Maintenance schedules
2. Impact and scope of maintenance tasks
3. Impact and scope of maintenance automation techniques
4. Include orchestration as appropriate
5. Maintenance automation tasks
Clearing logs
Archiving logs
Compressing drives
Removing inactive accounts
Removing stale DNS entries
Removing orphaned resources
Removing outdated rules from firewall
Removing outdated rules from security
Resource reclamation
Maintain ACLs for the target object
Management 20%
1. Monitoring
Target object baselines
Target object anomalies
Common alert methods/messaging
Alerting based on deviation from baseline
Event collection
2. Event correlation
3. Forecasting resource capacity
4. Policies in support of event collection
Policies to communicate alerts appropriately
1. Resources needed based on cloud deployment models
2. Capacity/elasticity of cloud environment
3. Support agreements
Cloud service model maintenance responsibility
4. Configuration management tool
5. Resource balancing techniques
6. Change management
Advisory board
Approval process
Document actions taken
1. Usage patterns
2. Cloud bursting
Auto-scaling technology
3. Cloud provider migrations
4. Extending cloud scope
5. Application life cycle
Application deployment
Application upgrade
Application retirement
Application replacement
Application migration
Application feature use
6. Business need change
Cloud service requirement changes
Impact of regulation and law changes
1. Identification
2. Authentication methods
Single sign-on
3. Authorization methods
4. Account life cycle
5. Account management policy
Password complexity rules
6. Automation and orchestration activities
User account creation
Permission settings
Resource access
User account removal
User account disablement
1. Procedures to confirm results
CPU usage
RAM usage
Storage utilization
Patch versions
Network utilization
Application version
Auditing enable
Management tool compliance
1. Analyze performance trends
2. Refer to baselines
3. Refer to SLAs
4. Tuning of cloud target objects
Service/application resources
5. Recommend changes to meet expected performance/capacity
Scale up/down (vertically)
Scale in/out (horizontally)
Reporting based on company policies
Reporting based on SLAs
2. Dashboard and reporting
Elasticity usage
Overall utilization
System availability
Troubleshooting 22%
Given a scenario, troubleshoot a deployment issue. 1. Common issues in the deployments
Breakdowns in the workflow
Integration issues related to different cloud platforms
Resource contention
Connectivity issues
Cloud service provider outage
Licensing issues
Template misconfiguration
Time synchronization issues
Language support
Automation issues
1. Exceeded cloud capacity boundaries
IP address limitations
Bandwidth limitations
Variance in number of users
API request limit
Batch job scheduling issues
2. Deviation from original baseline
3. Unplanned expansions
Account mismatch issues
Change management failure
Server name changes
IP address changes
Location changes
Version/feature mismatch
Automation tool incompatibility
Job validation issue
Incorrect subnet
Incorrect IP address
Incorrect gateway
Incorrect routing
DNS errors
QoS issues
Misconfigured VLAN or VXLAN
Misconfigured firewall rule
Insufficient bandwidth
Misconfigured MTU/MSS
Misconfigured proxy
2. Network tool outputs
3. Network connectivity tools
4. Remote access tools for troubleshooting
Given a scenario, troubleshoot security issues. 1. Authentication issues
Account lockout/expiration
2. Authorization issues
3. Federation and single sign-on issues
4. Certificate expiration
5. Certification misconfiguration
6. External attacks
7. Internal attacks
8. Privilege escalation
9. Internal role change
10. External role change
11. Security device failure
12. Incorrect hardening settings
13. Unencrypted communication
14. Unauthorized physical access
15. Unencrypted data
16. Weak or obsolete security technologies
17. Insufficient security controls and processes
18. Tunneling or encryption issues
implementing changes
1. Identify the problem
Question the user and identify user changes to computer and perform backups before making change
2. Establish a theory of probable cause (question the obvious)
If necessary, conduct internal or external research based on symptoms
3. Test the theory to determine cause
Once theory is confirmed, determine the next steps to resolve the problem
If the theory is not confirmed, reestablish a new theory or escalate
4. Establish a plan of action to resolve the problem and implement the solution
5. Verify full system functionality and, if applicable, implement preventive measures
6. Document findings, actions and outcomes

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CompTIA techniques

The Case for Explainable AI (XAI) | CV0-002 test Cram and test dumps

Key Takeaways
  • artificial Neural Networks offer tremendous performance advantages in comparison to other methodologies, however commonly at the cost of interpretability
  • problems and controversies bobbing up from the use and reliance on black field algorithms have given upward push to increasing requires more clear prediction technologies
  • Hybrid architectures try to resolve the difficulty of efficiency and explainability sitting in anxiety with one a further
  • present approaches to bettering the interpretability of AI models center of attention on either constructing inherently explainable prediction engines or conducting post-hoc evaluation
  • The analysis and construction in quest of to provide extra transparency in this regard is referred to as Explainable AI (XAI)
  • modern computing device learning architectures are turning out to be increasingly sophisticated in pursuit of sophisticated efficiency, often leveraging black container-trend architectures which present computational advantages at the rate of mannequin interpretability. 

    a couple of groups have already been caught on the inaccurate side of this “performance-explainability alternate off”.

    supply: DARPA

    related backed content material

    In August 2019, Apple and Goldman Sachs co-launched a credit card poised to disrupt the market and present buyers a swish, next-gen charge journey. Controversy struck very nearly immediately when clients observed that women have been being offered significantly smaller credit score traces than men, even within couples who filed taxes jointly. despite assertions via Goldman Sachs that the fashions exclude gender as a feature and that the statistics were vetted for bias via a 3rd birthday celebration, many admired names in tech and politics, including Steve Wosniak, publicly commented on the doubtlessly "mysogonsitic algorithm".

    Two months later, a analyze printed issues over an algorithm being leveraged through UnitedHealth group to optimize the outcomes of sanatorium visits given certain charge constraints. The analyze discovered that this algorithm turned into presenting related possibility scores to white and black patients, regardless of the black patients being enormously sicker, leading to their receiving disproportionately insufficient care. State regulators in long island known as on the nation's biggest healthcare issuer "to both prove that a corporation-developed algorithm used to prioritize affected person care in hospitals is never racially discriminatory against black patients, or cease the usage of it altogether.” The purveyor of the algorithm, UnitedHealth neighborhood-owned Optum, attempted to clarify and contextualize the consequences, however plenty of the headline damage changed into already done.

    Unintended penalties appear to arise often alongside unsupervised algorithms. throughout the 2010 "flash crash", principal inventory averages plunged 9% in a matter of minutes when excessive-frequency buying and selling algorithms fell right into a recursive cycle of panic promoting. here 12 months, an unremarkable replica of Peter Lawrence's booklet, The Making of a Fly, turned into discovered inexplicably listed for basically $24 million on Amazon. It grew to become out that two sellers of the ebook had set their expenditures to replace instantly daily. the first seller pegged their expense to 0.9983 instances the 2d seller’s, whereas the second vendor pegged their fee to 1.270589 times the first’s. 

    Incidents like these as well as prescient considerations for the long run have led to a surge in hobby in explainable AI (XAI).

    The Relevance of Explainability

    There are a wide range of stakeholders that stand to advantage from a focus on greater interpretable AI infrastructure. From a societal perspective, brilliant emphasis is positioned on the safeguarding towards bias in order to steer clear of the proliferation of negative feedback loops and the reinforcement of undesirable situations. From a regulatory perspective, adherence to existing frameworks corresponding to GDPR and CCPA, moreover people that arise in the future, is idea to be aided by way of explainability features. eventually, from the person point of view, proposing an knowing of why AI models be sure choices is additionally prone to enhance their self belief in items constructed on those fashions. 

    heading off Spurious Correlations

    There are also benefits concerning the mannequin’s performance and robustness that may still be of pastime to records scientists. Explainability facets can be leveraged not handiest in mannequin validation but also with debugging. additionally, they could aid practitioners in keeping off conclusions drawn from spurious correlations.

    source: “Why should I have confidence You?” Explaining the Predictions of Any Classifier

    for instance, it turned into shown that a expert logistic regression classifier, built to differentiate between photographs of wolves and huskies, could accomplish that with ostensible accuracy, despite basing that classification on aspects which are conceptually divorced from the use case. In certain, because lots of the photographs of wolves contained snowy backgrounds, the classifier assumed that was a main function inflicting it to malfunction in the case proven above. 

    as a result of human practitioners constantly have prior expertise bearing on the crucial aspects inside their datasets, they could help in gauging and organising the trustworthiness of AI fashions. for instance, a doctor working with a model that predicts no matter if a affected person has the flu would be capable of seem to be at the relative contributions of a variety of signs and spot whether the prognosis adheres to regularly occurring scientific knowledge.

    source: “Why should I have confidence You?” Explaining the Predictions of Any Classifier

    in one illustration, shown above, competing classifiers tried to assess whether a selected text document contained area be counted that pertained to “Christianity” or “Atheism”. Explainability aspects allowed for an intuitive visualization of the components that ended in respective predictions, revealing crucial distinctions in performance that might in any other case not have been evident.

    Challenges to Explainability

    despite the numerous advantages to setting up XAI, many ambitious challenges persist.

    a significant hurdle, specially for these making an attempt to set up standards and laws, is the incontrovertible fact that diverse clients would require distinctive levels of explainability in distinctive contexts. fashions which are deployed to effectuate choices that without delay have an effect on human life, akin to those in hospitals or military environments, will produce different needs and constraints than ones utilized in low-chance situations

    There are additionally nuances in the performance-explainability trade-off. Infrastructure and programs designers are continuously balancing the demands of competing pastimes. 

    Explainability can exist no longer handiest in tension with predictive accuracy, but also with person privacy. for instance, a model used to check the creditworthiness of personal loan applicants is probably going to make the most of records elements that those candidates accept as true with private. performance that offers insight into a particular input-output pairing could influence in deanonymization and begin to erode protections that most reliable practices surrounding in my opinion identifiable suggestions (PII) are structured to enforce.

    hazards of Explainability

    There are also a number of hazards linked to explainable AI. programs that produce seemingly-credible but truly-fallacious outcomes could be intricate to observe for most buyers. believe in AI systems can allow deception by way of those very AI programs, specifically when stakeholders give elements that purport to present explainability the place they in reality don't. Engineers additionally be troubled that explainability could provide upward thrust to vaster opportunities for exploitation by malicious actors. comfortably put, whether it is more convenient to understand how a model converts enter into output, it is likely additionally more convenient to craft adversarial inputs that are designed to achieve specific outputs.

    current landscape

    DARPA has been probably the most earliest and most prominent voices relating to XAI, and published right here picture depicting their view of the paradigm shift:

    supply: DARPA

    The hope is that this pursuit will effect in a shift toward greater user- and society-pleasant AI deployments without compromising efficacy.

    source: DARPA

    There are a litany of tactics that have been proposed for the intention of tackling Explainable AI, an issue which has received titanic attention in both tutorial and skilled circles. often, they can be grouped into two classes: 1) constructing inherently interpretable fashions; 2) developing equipment to have in mind how black packing containers work.

    develop Inherently Interpretable models

    Many AI leaders have argued that it may be prudent to advance fashions with embedded explainability facets, even though that reasons a drop in efficiency. although, contemporary analysis has shown that it is possible to pursue explainability devoid of compromising on predictive capabilities.

    decision trees and regression models typically offer notable explainability, but path competing architectures in performance. Deep Neural Networks (DNNs), then again, are effective predictors however lack interpretability. Combining processes, it seems, can offer the better of each worlds.

    for example, a method called Deep okay-Nearest Neighbors (DkNN) combines two normal architectures, embedding inference options to validate predictions into the constitution of the classifier. The resulting hybrid model is not only interpretable but additionally powerful in opposition t adversarial input.  

    source: Deep ok-Nearest Neighbors: towards assured, Interpretable and robust Deep getting to know

    developing equipment to keep in mind How Black bins Work

    records visualization concepts often demonstrate insights that summary information don't have. The “Datasaurus Dozen” is a group of 13 datasets which appear completely diverse from one yet another despite having similar ability, normal deviations, and Pearson’s correlations. advantageous visualization strategies permit practitioners to respect patterns, reminiscent of multicollinearity, that may easily degrade mannequin performance in an undetected fashion.

    supply: Datasaurus Dozen

    recent analysis has additionally shown price in visualizing the interactions between neurons in synthetic Neural Networks. A recent collaboration between OpenAI and Google researchers resulted in the 2019 introduction of “Activation Atlases”, which symbolize a brand new approach for pursuing precisely such an exercise.

    in line with the liberate, activation atlases can assist humans, “discover unanticipated issues in neural networks — for instance, locations where the community is counting on spurious correlations to classify pictures, or where re-the use of a function between two courses results in bizarre bugs.”

    as an instance, the technique become deployed on an image classifier that become designed to distinguish frying pans from woks and offer a human-interpretable visualization. 

    source: OpenAI - Introducing Activation Atlases

    a quick look on the image above suggests that this specific classifier considers the presence of noodles to be an essential attribute of a wok but no longer a frying pan. this is able to be a extremely positive piece of counsel in instantly knowing why a picture of a frying pan full of spaghetti was being classified as a wok.

    publish-Hoc mannequin analysis

    submit-hoc model analysis is among the most general paths to explaining AI in creation today.

    One familiar method is the native Interpretable model-Agnostic rationalization (LIME). When LIME receives input, reminiscent of an image to classify, it first generates a wholly new dataset composed of permuted samples and then populates the corresponding predictions that a black-container structure would have produced, had those samples been the input. An inherently interpretable model (e.g. linear or logistic regression, determination bushes, or okay-Nearest Neighbors) is then knowledgeable on the new dataset, which is weighted through the proximity of each respective pattern to the input of hobby.

    supply: native Interpretable model-Agnostic Explanations (LIME): An Introduction

    in the above picture, LIME will also be considered picking the top of a frog as the most important deciding upon function within the classification, which they are able to then assess towards their personal intuitions.

    Shapley Values symbolize an alternative significance rating framework that takes a game theoretical strategy to publish-hoc function analysis in trying to clarify the diploma to which a selected prediction deviates from the regular. As this documentation shows, the framework basically takes a prediction mannequin and establishes a “online game” wherein every function’s cost is assumed to be a “participant” competing for a “payout” it is described by the prediction. the use of iterated random sampling, together with counsel about their mannequin and information, they can right away verify each function’s contribution toward pushing the prediction faraway from its anticipated cost. 


    The markets have begun to awaken to the value of developing explainable AI capabilities. Many adoption trends within the AI area have been pushed via business choices reminiscent of Microsoft Azure and Google Cloud Platform. IBM published the picture below along with their announcement of AI Explainability 360, “a comprehensive open supply toolkit of state-of-the-paintings algorithms that guide the interpretability and explainability of laptop getting to know models.”  

    supply: IBM

    As companies continue to locate themselves amidst controversy coming up from unexpected and unexplainable AI effects, the want will proceed to grow for sufficient technical solutions that steadiness all of the competing interests involved in high-influence projects. As such, research and building efforts happening in both the inner most and public sector today will inexorably exchange the AI business panorama of the following day.

    concerning the author

    Lloyd Danzig is the Chairman & founder of the foreign Consortium for the ethical construction of synthetic Intelligence, a 501(c)(three) non-earnings NGO committed to ensuring that rapid trends in AI are made with a eager eye towards the long-term interests of humanity. he's also Founder & CEO of Sharp Alpha Advisors, a activities gaming advisory firm with a spotlight on corporations deploying innovative tech. Danzig is the Co-Host of The AI adventure, a podcast proposing an attainable evaluation of principal AI news and themes. He additionally serves as Co-Chairman of CompTIA AI Advisory Council, a committee of preeminent concept leaders concentrated on establishing industry premier practices that improvement groups while keeping buyers.

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