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IBM C2090-101 : IBM Big Data Engineer Exam

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Test Number : C2090-101
Test Name : IBM Big Data Engineer
Vendor Name : IBM
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C2090-101 test Format | C2090-101 Course Contents | C2090-101 Course Outline | C2090-101 test Syllabus | C2090-101 test Objectives


Exam Title : IBM Certified Data Engineer - Big Data
Exam ID : C2090-101
Exam Duration : 75 mins
Questions in test : 53
Passing Score : 65%
Official Training : IBM Knowledge Center
Exam Center : Pearson VUE
Real Questions : IBM Big Data Engineer Real Questions
VCE practice test : IBM C2090-101 Certification VCE Practice Test

Data Loading
- Load unstructured data into InfoSphere BigInsights
- Import streaming data into Hadoop using InfoSphere Streams
- Create a BigSheets workbook
- Import data into Hadoop and create Big SQL table definitions
- Import data to HBase
- Import data to Hive
- Use Data Click to load from relational sources into InfoSphere BigInsights with a self-service process
- Extract data from a relational source using Sqoop
- Load log data into Hadoop using Flume
- Insert data via IBM General Parallel File System (GPFS) Posix file system API
- Load data with Hadoop command line utility 34%
Data Security
- Keep data secure within PCI standards
- Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data 8%
Architecture and Integration
- Implement MapReduce
- Evaluate use cases for selecting Hive, Big SQL, or HBase
- Create and/or query a Solr index
- Evaluate use cases for selecting potential file formats (e.g. JSON, CSV, Parquet, Sequence, etc..)
- Utilize Apache Hue for search visualization 17%
Performance and Scalability
- Use Resilient Distributed Dataset (RDD) to Strengthen MapReduce performance
- Choose file formats to optimize performance of Big SQL, JAQL, etc.
- Make specific performance tuning decisions for Hive and HBase
- Analyze performance considerations when using Apache Spark 15%
Data Preparation, Transformation, and Export
- Use Jaql query methods to transform data in InfoSphere BigInsights
- Capture and prep social data for analytics
- Integrating SPSS model scoring in InfoSphere Streams
- Implement entity resolution within a Big Data platform (e.g. Big Match)
- Utilize Pig for data transformation and data manipulation
- Use Big SQL to transform data in InfoSphere BigInsights
- Export processing results out of Hadoop (e.g. DataClick, DataStage, etc.)
- Utilize consistent regions in InfoSphere Streams to ensure at least once processing 26%



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IBM Cheatsheet

The direction to real-world artificial intelligence | C2090-101 Study Guide and PDF Dumps

consultants from MIT and IBM held a webinar this week to talk about where AI technologies are nowadays and advances that allows you to aid make their utilization more functional and common.

Artificial Intelligence project creating. Abstract concept of cyber technology, machine learning.Brain of AI. Futuristic Innovative technology in science concept

photo: Sompong Rattanakunchon / Getty images

more about artificial intelligence

synthetic intelligence has made massive strides in contemporary years, but modern AI techniques remain confined, a panel of MIT professors and IBM's director of the Watson AI Lab referred to all over a webinar this week.

Neural networks can perform selected, well-described projects however they battle in actual-world cases that go past demo awareness and current limitations like confined facts, reliance on self-working towards, and answering questions like "why" and "how" versus "what," the panel mentioned.

The way forward for AI depends on enabling AI methods to do whatever once considered not possible: study through demonstrating flexibility, some semblance of reasoning, and/or by means of transferring talents from one set of tasks to another, the neighborhood stated. 

SEE: Robotic system automation: A cheat sheet (free PDF) (TechRepublic)

The panel discussion changed into moderated with the aid of David Schubmehl, a research director at IDC, and it all started with a query he posed asking in regards to the current barriers of AI and laptop discovering.

"The brilliant success right now in specific, in desktop studying, is in problems that require interpretation of alerts—images, speech and language," referred to panelist Leslie Kaelbling, a pc science and engineering professor at MIT. 

For years, individuals have tried to resolve issues like detecting faces and pictures and without delay engineering solutions that didn't work, she spoke of.

we have become decent at engineering algorithms that take statistics and use that to derive a solution, she referred to. "it's been an amazing success." nevertheless it takes loads of facts and lots of computation so for some issues formulations aren't obtainable yet that could allow us to be taught from the amount of information attainable, Kaelbling noted.

SEE: 9 tremendous-sensible problem solvers take on bias in AI, microplastics, and language lessons for chatbots (TechRepublic)

one in all her areas of center of attention is in robotics, and or not it's tougher to get working towards examples there because robots are expensive and elements destroy, "so they basically need to be capable of be taught from smaller amounts of facts," Kaelbling observed.

Neural networks and deep learning are the "newest and superior technique to body these types of issues and the successes are many," brought Josh Tenenbaum, a professor of cognitive science and computation at MIT. 

but when speakme about universal intelligence and the way to get machines to understand the area there remains a huge gap, he pointed out.

"however on the analysis side … in reality interesting things are beginning to ensue to are trying to catch some steps to more common kinds of intelligence [in] machines," he noted. In his work, "we're seeing methods in which they will draw insights from how humans have in mind the realm and taking small steps to position them in machines."

however americans consider of AI as being synonymous with automation, it is tremendously labor intensive in a way that doesn't work for most of the problems they want to clear up, noted David Cox, IBM director of the MIT-IBM Watson AI Lab.

Echoing Kaelbling, Cox referred to that leveraging tools today like deep researching requires massive quantities of "carefully curated, bias-balanced records," to be able to use them smartly. moreover, for most problems they are trying to solve, they don't have those "enormous rivers of records" to construct a dam in entrance of to extract some cost from that river, Cox talked about.

nowadays, groups are greater concentrated on solving some class of 1-off problem and even when they have got huge information, it's hardly curated, he observed. "So many of the problems they love to clear up with AI—we should not have the right equipment for that."

it truly is as a result of they have issues with bias and interpretability with people the use of these equipment and that they ought to bear in mind why they are making these decisions, Cox mentioned. "they may be all barriers." 

despite the fact, he mentioned, there may be huge probability looking at all these different fields to chart a course ahead. 

That comprises using deep researching, which is respectable for pattern awareness, to assist clear up complicated search complications, Tenenbaum said. To Strengthen intelligent agents, scientists should use the entire available equipment, talked about Kaelbling. for instance, neural networks are necessary for belief as well as greater degree and more summary forms of reasoning to decide, as an example, what to make for dinner or to decide a way to disperse supplies.

"The important component technologically is to recognise the candy spot for every piece and determine what it's first rate at and never respectable at. Scientists deserve to remember the function each piece plays," she said.

The MIT and IBM AI experts also mentioned a new foundational method referred to as neurosymbolic AI, which is the skill to mix statistical, facts-driven learning of neural networks with the effective advantage representation and reasoning of symbolic strategies.

Moderator Schubmehl commented that having a mix of neurosymbolic AI and deep researching "might really be the holy grail" for advancing true-world AI.

Kaelbling agreed, including that it can be now not simply those two ideas but consist of others as smartly.

one of the crucial issues that emerged from the webinar is that there's a extremely effective confluence of all types of AI that are actually being used, said Cox. The next evolution of very purposeful AI goes to be realizing the science of finding issues and building a device they will cause with and grow and gain knowledge of from, and determine what goes to ensue. "That can be when AI hits its stride," he observed. 

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