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Exam Code: C2090-558 Informix 11.70 Fundamentals study help November 2023 by Killexams.com team

C2090-558 Informix 11.70 Fundamentals

Exam Code : C2090-558

Exam Name : Informix 11.70 Fundamentals

Number of questions: 60

Number of questions to pass: 38

Time allowed: 90 mins

Status: Live

The test contains six sections totalling approximately 60 multiple-choice questions. The percentages after each section title reflect the approximate distribution of the total question set across the sections.

Section 1 - Planning and Installation 16%

Identify data access restrictions

Describe database workloads

Describe data type concepts for database planning

Demonstrate knowledge of how to configure and install Informix

Demonstrate knowledge of embeddability considerations and the deployment utility

Section 2 - Security 3%

Demonstrate knowledge of authentication

Demonstrate knowledge of authorizations

Section 3 - DBMS Instances and Storage Objects 14%

Describe how to identify and connect to Informix servers and databases

Demonstrate knowledge of how to create and configure storage objects

Demonstrate general knowledge of the system databases and database catalogs

Section 4 - Informix Tables, Views and Indexes 15%

Given a scenario, describe how to create a table

Describe when referential integrity should be used

Describe methods of data value constraint

Describe the differences between a table, view, sequences, synonyms, and indices

Describe triggers and appropriate uses

Demonstrate knowledge of schema commands

Section 5 - Informix Data using SQL 14%

Describe how to use SQL to SELECT data from tables

Describe how to use SQL to UPDATE, DELETE, or INSERT data

Demonstrate knowledge of transactions

Describe how to call a procedure or invoke a user defined function

Section 6 - Data Concurrency and Integrity 10%

Identify isolation levels and their effects

Identify objects on which locks can be obtained

Describe transaction integrity mechanisms

Section 7 - Tools and Utilities 15%

Describe onstat, oninit, and onmode utilities

Demonstrate knowledge of data movement utilities

Describe sysadmin database and its functionality

Demonstrate knowledge of the features or functions available in Informix tools

Section 8 - Backup and Restore 8%

Demonstrate knowledge of backup procedures

Demonstrate knowledge of recovery procedures

Section 9 - Replication Technologies 5%

Describe the purpose of the different replication technologies

Informix 11.70 Fundamentals
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Informix 11.70 Fundamentals
Question #110
What is true about poll threads?
A. poll threads always run on a CPU vp
B. poll threads always run on a NET vp
C. poll threads always run on an ADM vp
D. poll threads can be configured on a CPU or NET vp
Answer: D
Question #111
Which database contains the system-monitoring interface (SMI) tables which provide information about the state
of the database server?
A. sysutils
B. sysstate
C. sysmaster
D. sysmonitor
Answer: C
Question #112
Which two storage objects exist in Informix? (Choose two.)
A. dbspace
B. flash space
C. smart rowspace
D. smart blobspace
E. encrypted space
Answer: AD
Question #113
Which of the following is true about the remote query SELECT * FROM ABC@LMN:XYZ?
A. SELECT from table ABC at database LMN on server XYZ
B. SELECT from table ABC at server LMN on database XYZ
C. SELECT from table LMN on database ABC on server XYZ
D. SELECT from table XYZ on database ABC at server LMN
Answer: D
Question #114
You create a table with the statement shown below: CREATE TABLE foo(col INTEGER) In which dbspace will
the table reside?
A. in a temporary dbspace
B. in the default dbspace that is defined in onconfig
C. in the dbspace where the current database was created
D. in the dbspace specified by the IFX_DBSPACE environment variable
Answer: C
Question #115
You received an error, "Chunk is not empty", while trying to remove a chunk. How do you verify which objects
still occupy space in the chunk?
A. onspaces
B. onmonitor
C. oncheck -pe
Answer: C
Question #116
Which of the following statement is true of System Monitoring Interface (SMI) tables?
A. SMI tables store database structures.
B. Contains tasks used by the database scheduler
C. SMI tables are also known as System Catalog tables
D. Contains tables that the database server maintains automatically
Answer: D
Question #117
Which of the following statement is correct about Shared Disk (SD) secondary servers?
A. SD secondary servers support automatic index repair
B. SD secondary servers use paging files to track pages between checkpoints
C. SD secondary servers require an HDR secondary server to be present in order to function
D. SD secondary servers can become a High-Availability Data Replication (HDR) secondary server
Answer: B
Question #118
Which technology can be used to execute Data Definition Language (DDL) command across multiple instances?
A. Flexible Grid
B. Enterprise Replication
C. High Availability Cluster
D. High Availability Data Replication
Answer: A
Question #119
What is the lowest level of granularity supported by Enterprise Replication?
A. column
B. instance
C. database
D. fragment
Answer: A
Question #120
Which of the following Informix server modes disable execution of SQL statements for all users while allowing
administrative users to perform maintenance tasks?
A. Offline
B. On-Line
C. Quiescent
D. Administrative
Answer: C
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IBM Fundamentals study help - BingNews https://killexams.com/pass4sure/exam-detail/C2090-558 Search results IBM Fundamentals study help - BingNews https://killexams.com/pass4sure/exam-detail/C2090-558 https://killexams.com/exam_list/IBM IBM study warns of AI’s potential in cybercrime No result found, try new keyword!In a recent study conducted by IBM’s Security Intelligence X-Force team, the emerging role of artificial intelligence (AI) in cybercrime has come under scrutiny. The research highlights the ... Wed, 01 Nov 2023 04:55:00 -0500 en-us text/html https://www.msn.com/ IBM study indicates near parity between human and AI phishing attempts

A new study released today by IBM X-Force reveals a sizable increase in artificial intelligence-assisted cyberattacks and their ability versus humans, emphasizing an urgent need for organizations to adapt and bolster cybersecurity measures.

The study revolves around a core experiment that pitted AI against experienced human social engineers to craft phishing emails. Using OpenAI LP’s ChatGPT, the researchers provided five tailored prompts to guide the AI to develop phishing emails targeted toward specific industries.

The results were remarkable, with generative AI models able to craft convincingly deceptive phishing emails in just five minutes. In contrast, expert human social engineers were found to take about 16 hours for the same task.

The AI-generated phishing emails were found to be nearly as effective as their human-created counterparts. Human engineers leveraged open-source intelligence to gather information and then used that information to craft emails with a personal touch, emotional intelligence and an authentic feel. The human-created emails also incorporated a sense of urgency into their emails, but despite these advantages, the AI’s performance in the test was close, underscoring its potential in this domain.

Stephanie Carruthers, global head of innovation and delivery at IBM X-Force, wrote in the study that the results were so remarkable that participants walked away.

“I have nearly a decade of social engineering experience, crafted hundreds of phishing emails and I even found the AI-generated phishing emails to be fairly persuasive,” Carruthers explained. “In fact, there were three organizations who originally agreed to participate in this research project and two backed out completely after reviewing both phishing emails because they expected a high success rate.”

Although humans narrowly secured victory in the experiment, the study notes that the emergence of AI in phishing cannot be underestimated. The fact that AI tools with phishing capabilities are appearing in various forums speaks volumes about the future landscape.

The study makes several recommendations that businesses should consider to Boost their digital defenses against the rise of AI-generated phishing. The first is the need for verification, especially when employees encounter suspicious or unexpected emails. Rather than relying solely on digital evidence, employees should make a direct call to the sender to clarify doubts and prevent potential breaches.

It’s recommended that businesses need to revamp their training modules. The notion that phishing emails are identifiable mainly through poor grammar and spelling errors, as they have been in the past, should be replaced with more nuanced training. Incorporating advanced techniques such as vishing — voice-based phishing — in employee training can also offer a more comprehensive defense strategy.

The study also suggests that businesses should strengthen identity and access management systems, including adopting phishing-resistant multifactor authentication mechanisms to add an additional layer of security.

“The emergence of AI in phishing attacks challenges us to reevaluate our approaches to cybersecurity,” Carruthers added. “By embracing these recommendations and staying vigilant in the face of evolving threats, we can strengthen our defenses, protect our enterprises, and ensure the security of our data and people in today’s dynamic digital age.”

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Mon, 23 Oct 2023 23:00:00 -0500 en-US text/html https://siliconangle.com/2023/10/24/ibm-study-indicates-near-parity-human-ai-phishing-attempts/
New IBM Study Explores the Changing Role of Leadership as Businesses in Europe Embrace Generative AI No result found, try new keyword!IBM launches new European study of 1600+ senior leaders and C-level executives to explore how leadership is changing in the age of AI 82% of leaders surveyed have already deployed generative AI ... Tue, 07 Nov 2023 18:50:00 -0600 https://www.nasdaq.com/press-release/new-ibm-study-explores-the-changing-role-of-leadership-as-businesses-in-europe IBM has made a new, highly efficient AI processor
Image of a series of chips on a black background, with one chip labelled

As the utility of AI systems has grown dramatically, so has their energy demand. Training new systems is extremely energy intensive, as it generally requires massive data sets and lots of processor time. Executing a trained system tends to be much less involved—smartphones can easily manage it in some cases. But, because you execute them so many times, that energy use also tends to add up.

Fortunately, there are lots of ideas on how to bring the latter energy use back down. IBM and Intel have experimented with processors designed to mimic the behavior of genuine neurons. IBM has also tested executing neural network calculations in phase change memory to avoid making repeated trips to RAM.

Now, IBM is back with yet another approach, one that's a bit of "none of the above." The company's new NorthPole processor has taken some of the ideas behind all of these approaches and merged them with a very stripped-down approach to running calculations to create a highly power-efficient chip that can efficiently execute inference-based neural networks. For things like image classification or audio transcription, the chip can be up to 35 times more efficient than relying on a GPU.

A very unusual processor

It's worth clarifying a few things early here. First, NorthPole does nothing to help the energy demand in training a neural network; it's purely designed for execution. Second, it is not a general AI processor; it's specifically designed for inference-focused neural networks. As noted above, inferences include things like figuring out the contents of an image or audio clip so they have a large range of uses, but this chip may do you any good if your needs include running a large language model because they're too large to fit in the hardware.

Finally, while NorthPole takes some ideas from neuromorphic computing chips, including IBM's earlier TrueNorth, this is not neuromorphic hardware, in that its processing units perform calculations rather than attempt to emulate the spiking communications that genuine neurons use.

That's what it's not. What actually is NorthPole? Some of the ideas do carry forward from IBM's earlier efforts. These include the recognition that a lot of the energy costs of AI come from the separation between memory and execution units. Since a key component of neural networks—the weight of connections between different layers of "neurons"—is held in memory, any execution on a traditional processor or GPU burns a lot of energy simply getting those weights from memory to where they can be used during execution.

So NorthPole, like TrueNorth before it, consists of a large array (16×16) of computational units, each of which includes both local memory and code execution capacity. So, all of the weights of various connections in the neural network can be stored exactly where they're needed.

Another feature is extensive on-chip networking, with at least four distinct networks. Some of these carry information from completed calculations to the compute units where they're needed next. Others are used to reconfigure the entire array of compute units, providing the neural weights and code needed to execute one layer of the neural network while the calculations of the previous layer are still in progress. Finally, communications among neighboring compute units is optimized. This can be useful for things like finding the edge of an object in an image. If the image is entered so that neighboring pixels go to neighboring compute units, they can more easily cooperate to identify features that extend across neighboring pixels.

The computing resources are unusual as well. Each unit is optimized for performing lower-precision calculations, ranging from two- to eight-bit precision. While higher precision is often required for training, the values needed during execution generally don't require that level of exactitude. To keep those execution units in use, they are incapable of performing conditional branches based on the value of variables—meaning your code cannot contain an "if" statement. This eliminates the need for the hardware needed for speculative branch execution, and it ensures that the wrong code will be executed whenever that speculation turns out to be wrong.

This simplicity in execution makes each compute unit capable of massively parallel execution. At two-bit precision, each unit can perform over 8,000 calculations in parallel.

Software, too

Because of all these distinctive design choices, the team behind NorthPole had to develop its own training software that figures out things like the minimum level of precision that's necessary at each layer to operate successfully.

Executing neural networks on the chip is also a relatively unusual process. Once the weights and connections of the neural network are placed in buffers on the chip, execution simply requires an external controller—typically a CPU—to upload the data it's meant to operate on (such as an image) and tell it to start. Everything else runs to completion without the CPU's involvement, which should also limit the system-level power consumption.

The NorthPole test chips were built on a 12 nm process, which is well behind the cutting edge. Still, they managed to fit 256 computational units, each with 768 kilobytes of memory, onto a 22 billion transistor chip. When the system was run against an Nvidia V100 Tensor Core GPU that was fabricated using a similar process, they found that NorthPole managed to perform 25 times the calculations for the same amount of power. And it could outperform a cutting-edge GPU by about fivefold using the same measure. Tests with the system showed it could perform a range of widely used neural network tasks efficiently, as well.

While the tests were run with the NorthPole processor installed on a PCIe card, IBM told Ars that the chip is still viewed as a research prototype, and additional work would be needed to convert it into a commercial product. The company did not indicate whether it would be pursuing commercialization, though.

One of the potential limitations of the system is that it can only run neural networks that fit within its hardware. Put too many nodes in a single layer, and NorthPole cannot deal with it. But there is the possibility of splitting up layers and executing segments of them on multiple NorthPole chips in parallel. The hardware has the capacity to handle this, but it hasn't been tested as of yet.

Perhaps the biggest limitation, however, is that this is specialized for a single category of AI task. While it's a commonly used one, the efficiency here comes largely from designing hardware that's a good match to the type of execution needed by inference tasks. So, while it's good to see the effort put into dropping the power demands of some AI workloads, we're not at the point yet where we can have a single accelerator that works for all cases.

Science, 2023. DOI: 10.1126/science.adh1174  (About DOIs).

Correction: clarified the problems with Large Language Models and NorthPole.

Thu, 19 Oct 2023 23:31:00 -0500 John Timmer en-us text/html https://arstechnica.com/science/2023/10/ibm-has-made-a-new-highly-efficient-ai-processor/
IBM Consulting to help companies get started with generative AI on AWS cloud

IBM Corp. said today its consulting division is partnering with Amazon Web Services Inc. to help customers deploy and operationalize generative artificial intelligence on its cloud computing infrastructure.

In addition to helping customers deploy some very specific use cases of generative AI, IBM will integrate its watsonx.data platform with AWS. It has also committed to training an additional 10,000 consultants on AWS’s array of generative AI services by the end of 2024.

IBM Consulting has worked with AWS for a number of years, helping clients across a range of industries implement AI and various other cloud services. With today’s news, IBM said it wants to step things up with regards to generative AI, the hot technology that powers humanlike chatbots such as ChatGPT and other services, such as image generators.

The partnership will initially focus on helping joint customers to integrate three very specific generative AI solutions. With Contact Center Modernization with Amazon Connect, IBM said it has worked with AWS to create various summarization and categorization functions that will enable customer service-focused chatbots to quickly summarize the details of their interactions with customers, enabling a seamless handoff of these calls off to human agents.

IBM is also expanding its Platform Services on AWS that first debuted in November. It has been enhanced with generative AI to better manage the entire cloud value chain, helping to automate tasks associated with information technology operations and platform engineering. IBM said the new generative AI capabilities deliver clients tools to enhance business serviceability and availability for their applications hosted on AWS through “intelligent issue resolution and observability techniques.”

As for Supply Chain Ensemble on AWS, this is being enhanced with a new virtual assistant that IB says will help to augment and accelerate the work of supply chain professionals.

In addition to those specific generative AI services, IBM Consulting said it will integrate AWS generative AI services into its IBM Consulting Cloud Accelerator to help customers accelerate their cloud transformation initiatives. The new services are said to be focused on reverse engineering, code generation and code conversion.

Expanded AI talent pool and tools

While it’s stepping up those new generative AI services, IBM plans to upskill its own teams of consultants. It’s planning to train and skill 10,000 of its consulting staff on AWS generative AI services by the end of next year. In effect, it’s building an army of AWS AI experts who will be able to engage with customers and help them identify ways to innovate and Boost business processes with the new technology.

Customers won’t have to rely on only IBM’s experts, though. They’ll also be able to use specialist tools such as watsonx.data, a purpose-built data store for generative AI workloads that will be available as a fully managed software-as-a-service offering on AWS. Before the end of 2024, IBM’s entire portfolio of watsonx.ai and watsonx.governance services is expected to be made available on AWS.

Cloud AI providers and systems integrators are racing to expand their alliances so they can cater to the enormous appetite enterprises have for AI systems, said Holger Mueller of Constellation Research Inc. Today’s partnership announcement is therefore good news for many companies, he added.

“It’s a notable coup for IBM too, as it becomes an exclusive partner for many of Amazon’s AI services, though it remains to be seen how long that will remain the case,” he added.

The availability of IBM’s watsonx offerings is also noteworthy, and perhaps even more important for many customers, the analyst continued. “IBM is making watsonx a first-class citizen on AWS, available in the AWS cloud marketplace,” he said. “This is key because data is the foundation of AI and so watsonx.data will be the place where many joint customers will start.”

French telecommunications services provider Bouygues Telecom SA said it has been one of the earliest beneficiaries of IBM’s expanded partnership with AWS. It engaged with IBM Consulting to design and implement an evolving cloud strategy that leverages the most advanced AI technologies.

Through the IBM Garage approach, Bouygues and IBM co-designed a custom data and AI reference architecture covering multiple cloud scenarios that can be extended to all of the AI and data projects spread across its cloud and on-premises platforms. With this, Bouygueus has been able to develop and scale up a variety of proof-of-concept generative AI models in rapid time, with minimal cost or risk, the company said.

“As we sought to leverage generative AI to extract insights from our engagements with clients, we were confronted with some unfamiliar issues around storage, memory size and power requirements,” said Matthieu Dupuis, head of AI at Bouygues. “IBM Consulting and AWS have been invaluable partners in identifying the right model for our needs and overcoming these technological barriers.”

Photo: IBM

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Tue, 17 Oct 2023 23:59:00 -0500 en-US text/html https://siliconangle.com/2023/10/18/ibm-consulting-help-companies-get-started-generative-ai-aws-cloud/
IBM, MeitY sign MoUs to help startups working in AI, semiconductors, quantum tech

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