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IBM C1000-031 : IBM Power Systems ScaleUp Technical Sales Exam

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Test Number : C1000-031
Test Name : IBM Power Systems ScaleUp Technical Sales
Vendor Name : IBM
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C1000-031 test Format | C1000-031 Course Contents | C1000-031 Course Outline | C1000-031 test Syllabus | C1000-031 test Objectives

Exam Title : IBM Certified Specialist - Power Systems Scale-Up Technical Sales
Exam ID : C1000-031
Exam Duration : 90 mins
Questions in test : 61
Passing Score : 39 / 61
Exam Center : Pearson VUE
Real Questions : IBM Power Systems Scale-Up Technical Sales Real Questions
VCE practice questions : IBM C1000-031 Certification VCE Practice Test

Power Systems Architecture and Product Family 26%
- Compare and position enterprise with scale-out models in order to satisfy customer needs.
- Recognize POWER9 compatibility with prior generations of Power Systems when planning to upgrade or migrate a customer's installed hardware and/or software.
- Describe reliability, availability and serviceability (RAS) features of the Power Systems product family, especially those exclusive to enterprise servers.
- Describe enterprise pools prerequisites, capabilities and benefits, including mobile and static activations and conversions.
- Design solutions with expansion drawers, adapter cards, traditional disk, Solid State Drives (SSDs), attached SAN and tape into a POWER9 solution.
- Describe the benefits of POWER9 processor architecture, including SMT, L4 cache, balanced performance, clock speed, EnergyScale, Workload Optimized Frequency, memory bandwidth, I/O bandwidth (PCIe Gen4), CAPI, etc. relative to prior Power Systems and competition.
- Identify rack best practices for enterprise servers (e.g. I/O drawer placement, spacing, and cabling implications, "deracking", horizontal PDUs, IBM manufacturing options, etc.).
- Differentiate memory optimization technologies for Power Systems (e.g. Active Memory Expansion (AME), Active Memory Sharing (AMS)) and configuration requirements.
- Identify the appropriate planning tools and information resources to confirm that requirements of a solution have been met (e.g. e-config, System Planning Tool (SPT), Workload Estimator (WLE), Sizing Guides, System Storage Interoperation Center (SSIC), Energy Estimator and System Performance Reports).
- Evaluate networking requirements needed to manage redundancy, virtualization, security, bandwidth (i.e., CPU resources and network settings), and determining the correct adapters and quantities.
- Identify environmental considerations (e.g. power, PDUs, UPS, space, racks, etc.).

Virtualization 21%
- Match hardware connectivity (e.g. Fibre Channel, SAS, iSCSI) with the types of virtualization (e.g. NPIV, vSCSI, SR-IOV, SEA).
- Determine when shared storage pools (SSP) are appropriate for an enterprise solution.
- Identify the capabilities of Live Partition Mobility (LPM).
- Identify resource sharing capabilities of Power Systems, including processors, memory, and I/O (e.g. micropartitioning, shared processor pools (SPP), dedicated and shared-dedicated processors).
- Design an appropriate virtualization system management solution (e.g. HMC, PowerVC, PowerVM NovaLink).
- Given customer requirements, design a single or dual VIO Server (VIOS) solution (e.g. NPIV and vSCSI to host storage). Consider requisites, corequisites and limitations which may impact future requirements.

Software and Operating System Considerations 15%
- Articulate the unique characteristics and different advantages offered by each operating system (AIX, IBM i, and Linux).
- Describe Power Systems licensing considerations with regard to transfers, sub-capacity licensing, etc. (operating systems, PowerVM, etc.).
- Recognize the basic security strengths of AIX, Linux, IBM i, and PowerVM and discuss PowerSC offerings. Given a customer's requirements, describe which Power Systems Software product(s) and Edition(s), is/are appropriate (e.g., PowerHA, PowerVM, and PowerVC).
- Given a customer's requirements, describe which Power Systems Software product(s) and Edition(s), is/are appropriate (e.g., PowerHA, PowerVM, and PowerVC).

Design Solution to Customer Requirements 23%
- Given customer requirements, design an appropriate solution, including HMC, enterprise pools, remote restart, performance and capacity management, virtualization management/co-management, redundant FSPs, redundant HMCs, and secure networking.
- Incorporate appropriate existing hardware to a new configuration (e.g., adapters, disk drives, I/O drawers, etc.).
- Compare and contrast the I/O and storage options for Power Systems Enterprise servers.
- Describe the role of the Technical and Delivery Assessment (TDA) and how it is used to document customer requirements and minimize risk.
- Differentiate, position, and recommend Capacity on Demand (CoD) offerings (e.g. CUoD, Utility, Elastic and Trial). Identify key IBM cloud management offerings available for Power Systems (e.g. PowerVC, IBM Cloud PowerVC Manager) and the benefits that are associated with having a cloud infrastructure based on Power Enterprise servers (e.g. configuration flexibility, Dynamic Resource Optimization, Capacity on Demand).
- Identify technical advantages that POWER9 Enterprise servers provide for Linux workloads in terms of performance, reliability, price/performance and workload migration and consolidation.
- Identify methods available to maintain hardware and software (e.g., Fix Central, FLRT, etc.).
- Given a client's availability requirements (RTO/RPO), design an appropriate solution for business continuity and/or Disaster Recovery (e.g., PowerHA, Geographically Dispersed Resiliency, backup/restore).
- Compare POWER8 to POWER9 migration considerations and requirements.

Competition 15%
- Identify the advantages of POWER9 processor-based technology compared with x86 processors.
- Describe the business advantages of virtualization from a technical perspective.
- Compare and contrast the value proposition of solutions running on Power Systems (e.g., SAP HANA) compared with competitive platforms (e.g., Dell, HP, Oracle, etc.).
- Position the advantages of PowerVM virtualization and Capacity on Demand (CoD) relative to competition (e.g., Oracle, HP, Hyper-V, VMWare, KVM).
- Discuss a customer's requirements with regards to TCA and TCO (e.g., software licensing relative to cores).

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IBM tricks

Adversarial assaults towards laptop learning systems – every little thing you should comprehend | C1000-031 braindumps and genuine Questions

The conduct of laptop getting to know methods can be manipulated, with potentially devastating penalties

Adversarial attacks against machine learning systems - everything you need to know

In March 2019, security researchers at Tencent managed to trick a Tesla model S into switching lanes.

All they had to do became area a couple of inconspicuous stickers on the highway. The method exploited system defects within the machine gaining knowledge of (ML) algorithms that power Tesla’s Lane Detection expertise with a purpose to trigger it to behave erratically.

computing device researching has become an essential component of lots of the functions they use daily – from the facial consciousness lock on iPhones to Alexa’s voice consciousness function and the unsolicited mail filters in their emails.

but the pervasiveness of desktop learning – and its subset, deep discovering – has also given upward thrust to adversarial attacks, a breed of exploits that manipulate the behavior of algorithms by using proposing them with carefully crafted enter records.

what is an adversarial assault?

“Adversarial assaults are manipulative moves that aim to undermine computing device learning efficiency, trigger model misbehavior, or acquire blanketed information,” Pin-Yu Chen, chief scientist, RPI-IBM AI research collaboration at IBM research, instructed The day by day Swig.

Adversarial computing device researching became studied as early as 2004. but at the time, it become considered as a fascinating peculiarity in place of a safety probability. besides the fact that children, the upward push of deep getting to know and its integration into many applications in contemporary years has renewed interest in adversarial computer discovering.

There’s turning out to be difficulty within the protection community that adversarial vulnerabilities may also be weaponized to attack AI-powered programs.

How do adversarial assaults work?

As adversarial to basic application, the place builders manually write instructions and rules, machine researching algorithms boost their behavior through journey.

for instance, to create a lane-detection device, the developer creates a computing device discovering algorithm and trains it through presenting it with many labeled pictures of street lanes from distinctive angles and below diverse lighting situations.

The laptop learning model then tunes its parameters to trap the regular patterns that turn up in pictures that comprise street lanes.

With the correct algorithm structure and ample working towards examples, the model can be capable of realize lanes in new photos and video clips with astonishing accuracy.

however despite their success in complicated fields similar to computing device imaginative and prescient and voice attention, computing device discovering algorithms are statistical inference engines: complex mathematical functions that seriously change inputs to outputs.

If a desktop getting to know tags a picture as containing a specific object, it has discovered the pixel values in that picture to be statistically corresponding to different photographs of the item it has processed all through working towards.

Adversarial attacks exploit this attribute to confound machine learning algorithms via manipulating their input statistics. as an instance, with the aid of adding tiny and inconspicuous patches of pixels to an image, a malicious actor may cause the computing device studying algorithm to categorise it as something it isn't.

Machine learning brain circuitry in spaceAdversarial assaults confound computer learning algorithms with the aid of manipulating their input records

The styles of perturbations applied in adversarial attacks depend on the target records category and desired effect. “The possibility model has to be personalized for different information modality to be moderately adversarial,” says Chen.

“as an instance, for images and audios, it makes feel to believe small statistics perturbation as a chance mannequin because it are not with ease perceived by a human however may additionally make the goal model to misbehave, causing inconsistency between human and computing device.

“however, for some records forms akin to text, ‘perturbation’, by means of quite simply altering a notice or a personality, may additionally disrupt the semantics and simply be detected through people. therefore, the possibility model for text should still be naturally distinctive from graphic or audio.”

Adversarial attacks towards computing device imaginative and prescient systems

The most generally studied enviornment of adversarial laptop learning involves algorithms that technique visible records. The lane-changing trick mentioned initially of this article is an illustration of a visual adversarial attack.

In 2018, a bunch of researchers confirmed that through adding stickers to a stop sign (PDF), they could idiot the laptop vision device of a self-driving vehicle to mistake it for a speed restrict sign.

Series of spot signs targeted in self-driving adversarial attackResearchers tricked self-driving programs into settling on a stop signal as a velocity limit sign

In an extra case, researchers at Carnegie Mellon university managed to fool facial focus methods into mistaking them for celebrities by using above all crafted glasses.

Adversarial assaults against facial attention systems have found their first true use in protests, the place demonstrators use stickers and make-up to idiot surveillance cameras powered by way of computing device gaining knowledge of algorithms.

Adversarial assaults against speech focus methods

computer imaginative and prescient programs don't seem to be the best pursuits of adversarial attacks. In 2018, researchers confirmed that automated speech cognizance (ASR) systems may even be focused with adversarial assaults (PDF). ASR is the know-how that enables Amazon Alexa, Apple Siri, and Microsoft Cortana to parse voice commands.

In a hypothetical adversarial assault, a malicious actor will carefully manipulate an audio file – say, a song posted on YouTube – to include a hidden voice command. A human listener wouldn’t note the change, but to a computer researching algorithm looking for patterns in sound waves it could be clearly audible and actionable. as an instance, audio adversarial assaults could be used to secretly send instructions to smart speakers.

Adversarial assaults against textual content classifiers

In 2019, Chen and his colleagues at IBM analysis, Amazon, and the institution of Texas confirmed that adversarial examples also utilized to text classifier laptop learning algorithms akin to junk mail filters and sentiment detectors.

Dubbed ‘paraphrasing attacks’, textual content-primarily based adversarial attacks involve making alterations to sequences of words in a piece of text to cause a misclassification error within the machine discovering algorithm.

Example of a paraphrasing attack against fake news detectors and spam filtersinstance of a paraphrasing attack in opposition t false news detectors and junk mail filters

Black-field vs white-box adversarial attacks

like every cyber-assault, the success of adversarial assaults depends on how a whole lot counsel an attacker has on the focused computer learning mannequin. in this admire, adversarial attacks are divided into black-box and white-field assaults.

“Black-container assaults are purposeful settings the place the attacker has restrained suggestions and access to the goal ML model,” says Chen. “The attacker’s ability is the same as a regular consumer and may handiest operate assaults given the allowed capabilities. The attacker also has no competencies concerning the mannequin and information used at the back of the service.”

study extra AI and desktop learning safety news

for example, to target a publicly accessible API akin to Amazon Rekognition, an attacker need to probe the gadget through time and again presenting it with quite a few inputs and evaluating its response unless an adversarial vulnerability is discovered.

“White-container attacks constantly expect complete advantage and entire transparency of the target model/statistics,” Chen says. during this case, the attackers can assess the inner workings of the model and are more suitable positioned to locate vulnerabilities.

“Black-box attacks are more practical when evaluating the robustness of deployed and entry-limited ML fashions from an adversary’s viewpoint,” the researcher mentioned. “White-box assaults are more valuable for mannequin developers to bear in mind the boundaries of the ML model and to enhance robustness during model training.”

statistics poisoning assaults

In some instances, attackers have entry to the dataset used to educate the focused laptop learning mannequin. In such cases, the attackers can perform ‘information poisoning’, the place they deliberately inject adversarial vulnerabilities into the model during training.

as an example, a malicious actor may educate a machine getting to know mannequin to be secretly sensitive to a selected pattern of pixels, and then distribute it among developers to combine into their functions.

Given the charges and complexity of establishing computer researching algorithms, the use of pretrained fashions is awfully common within the AI community. After distributing the mannequin, the attacker uses the adversarial vulnerability to attack the functions that combine it.

“The tampered mannequin will behave on the attacker’s will simplest when the trigger pattern is present; in any other case, it's going to behave as a standard model,” says Chen, who explored the threats and treatments of statistics poisoning assaults in a exact paper.

Adversarial attacker has inserted a white box as an adversarial trigger in the training examples of a deep learning modelwithin the above examples, the attacker has inserted a white field as an adversarial set off in the practising examples of a deep discovering model

This type of adversarial exploit is also called a backdoor attack or trojan AI and has drawn the attention of Intelligence superior analysis projects (IARPA).

preserving machine studying programs in opposition t adversarial attacks

during the past few years, AI researchers have developed quite a few suggestions to make desktop studying models extra strong towards adversarial attacks. The most appropriate-general protection method is ‘adversarial training’, wherein a developer patches vulnerabilities with the aid of working towards the computing device researching model on adversarial examples.

different protection innovations contain changing or tweaking the model’s constitution, comparable to including random layers and extrapolating between a number of computing device learning models to steer clear of the adversarial vulnerabilities of any single mannequin from being exploited.

“I see adversarial attacks as a clever method to do ‘force trying out’ and ‘debugging’ on ML models that are considered ‘mature’, earlier than they're truly being deployed in the box,” says Chen.

“in case you consider a technology may still be totally confirmed and debugged earlier than it becomes a product, then an adversarial assault – for the aim of robustness trying out and growth – may be a vital step within the building pipeline of ML know-how.”

suggested Going deep: How advances in laptop gaining knowledge of can enrich DDoS assault detection

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