danidee
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Will Robots Steal Our Jobs?

So, I just read a staggering statistic that I think the rest of you would be equally WTF?! over. According to a study sponsored by the World Economic Forum, humans will be losing an approximate 5.1 million jobs to robots within the next five years.
The study examined 15 different economies across the world, who combined make about 65% of the international workforce. Due to the upward trend of technology and service-oriented robotics, about 7 million jobs will be lost. However, 2 million will be gained due to the progress of technology in these different nations.
As of right now, robots and machinery have begun taking over more general jobs, like cashiers. However, as they become more and more widespread, the World Economic Forum believes that these robots will become smarter and more specialized.

"As entire industries adjust, most occupations are undergoing a fundamental transformation. While some jobs are threatened by redundancy and others grow rapidly, existing jobs are also going through a change in the skill sets required to do them."

A Business Insider report backed up study, adding that the jobs they believe are most at risk are ones involving data entry or more clerical-style operations, including pharmacists, lawyers and paralegals, drivers, astronauts, store clerks, soldiers, babysitters, rescuers, and sportswriters.
(The idea of astronauts being replaced by robots is seriously depressing to me. Space travel is so exciting because humans are doing it!)
CNN also cited a Bank of America sponsored study that added more jobs to the list: bakers, butchers, tour guides, tax collectors, telemarketers, insurance sales agents, retail salespeople, clerks, accountants, and pharmacy technicians. It also added that the manufacturing industry will see a huge boom in robot workforce, increasing 35% by 2025.

"To prevent a worst-case scenario – technological change accompanied by talent shortages, mass unemployment and growing inequality – reskilling and upskilling of today's workers will be critical. It is simply not possible to weather the current technological revolution by waiting for the next generation's workforce to become better prepared."

The good news is that there are still plenty of jobs that are pretty unlikely to be replaced by robots any time soon. Anything involving the arts, empathy, or intuition - such as social work, teaching, or police work - require a type of human interaction that robots just can't replicate. (So next time you're teased about your 'worthless' liberal arts degree, feel free to let them know about the impending robot revolution.)

So what do you guys think? Are robots really going to be taking over so much so quickly? Do you think the government should have any say over how much robotics is used?

Let me know in the comments below, and for more WTF news, follow the WTF Street Journal collection.
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@ShinigamiSan @ZacharyStewart @BeannachtOraibh Stephen Hawking had some pretty good thoughts on this (which I'll paraphrase) we shouldn't fear innovation and technology, we should fear the people that control it. There have been plenty of innovations in the past decades that should in theory have made human labor easier and more valuable. Automated check-out is something a machine can do, but only a human can write an amazing book for example. In theory all of these machines taking over labor that is necessary for our survival but not particularly fulfilling (like data entry, farming, retail, and driving) would mean that humans would have more time to do different work. This has been a problem for a long time: We have plenty of food- but instead of prices going down due to the surge in supply, we throw it away.
Hmmmm. I'm curious as to how babysitters & drivers are at the top of the list... I mean I personally wouldn't leave my child with one. Or trust one doing 100km on the highway. It can be quite the game of Think fast. Pothole! here. I kinda thought the entire population would think the same as me on those 2 in general though... Did a robot come up with those stats? Sounds suspiciously like one did. Lol.
You made a really awesome point @shannonl5. I actually think that it's something I fear too, but I really shouldn't. I think that while robots might take some of our jobs, that could leave more opportunity for education, innovation, and NEW job creation. There are plenty of jobs out there that require human brains, not just robotic jobs. I think this might actually leave room for us to create those jobs!
My ISP is about this. I'm currently studying the effects that artificial intelligence will have on our economy in the next 15 years. It is indeed true. @ZacharyStewart
Didn't this type of fear happened before the rise of computer and internet? Online streaming pretty much wiped out the whole video industry (blockbuster anyone?), digitalization made tape recorder and Kodak almost obsolete, but are we worse off? New technologies create new opportunities, we just adapt. There's always gonna be technology advancements therefore changes to the workforce.
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Robot là gì? Ứng dụng robot trong sản xuất tự động
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Những robot này cũng đã tạo ra một nhánh robot mới hơn: robot mềm . Lịch sử của Robot Từ rất lâu , đã có rất nhiều các thiết bị tự động có thể định cấu hình của người dùng và thậm chí là các ô tô tự động giống con người và các loài động vật khác, được thiết kế chủ yếu để giải trí. Khi kỹ thuật cơ khí phát triển qua thời đại công nghiệp , đã xuất hiện nhiều ứng dụng thực tế hơn như máy móc tự động, điều khiển từ xa và điều khiển từ xa không dây . Thuật ngữ này xuất phát từ một gốc Slavic, robot- , với các ý nghĩa liên quan đến lao động. Từ ‘robot’ lần đầu tiên được sử dụng để biểu thị một hình người hư cấu trong vở kịch tiếng Séc năm 1920 RUR (Rossumovi Univerzální Roboti – Rossum’s Universal Roboti) của Karel Čapek , mặc dù anh trai của Karel là Josef Čapek mới là người phát minh ra từ này. 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Robot cộng tác Co-bot hay robot cộng tác có độ an toàn cao hơn cánh tay robot công nghiệp, có thể hoạt động cùng lúc với người lao động và hỗ trợ trong các công đoạn lắp ráp, nâng, nhấc, dịch chuyển sản phẩm từ các vị trí trên dây chuyền lắp ráp, sản xuất. Các robot cộng tác dạng này thường là robot đánh bóng, bắn vít, cấp liệu… Robot gắp sản phẩm Với kích thước nhỏ gọn, nhanh nhẹn và thông minh hơn, robot gắp vật dùng cho ứng dụng pick and place trong dây chuyền phần loại, kiểm tra sản phẩm với các tính năng vision hiện đại, kiểm tra màu sắc, hình dáng, đo kiểm…kết hợp các đầu hút, gắp xử lý sau công đoạn kiểm tra bằng camera. Robot đóng gói hàng hóa-robotic packaging systems. Robot đang ngày càng được thiết kế, lập trình để đa dạng hóa các nhiệm vụ hỗ trợ con người với mục đích nâng cao năng suất, độ chính xác và an toàn hơn cho người sử dụng.
COVID-19 Impact on Healthcare Robots in the Healthcare Industry
COVID-19 Impact on Healthcare Robots in the Healthcare Industry As the deaths from the COVID-19 pandemic start to increase, the World Health Organization (WHO) has urged citizens to maintain specific social distances. In an attempt to avoid the spread of COVID-19 at the population level, medical robots or healthcare robots are gradually involved in the roles of sanitizing patients' quarters, distributing medications, and supplying meals to ill people. Supplying supplies to households and delivering effective services to injured patients remained a major obstacle, and this is where healthcare robotics is creating a space for them. The new pandemic is growing in demand for healthcare robots as they play a crucial role in the process of drug distribution, patient evaluation, and medical workers' infection control. COVID-19 Market Effect The 2002-2004 SARS pandemic has indelibly altered the business climate for healthcare robots. Nearly 8,000 people were affected and 700 were killed. In addition, MERS affected 200 and killed about 40 of them. They were all limited to different territories. COVID-19, on the other hand, as a pandemic, has had a larger influence on the global economy. The SARS and MERS outbreaks contributed to a new era of creativity with the advent of disinfection robots that produce UVD light to combat against infectious viruses and bacteria on hospital surfaces. If COVID-19 continued to grow globally, UVD robots makers would expand significantly, and if the pandemic was bought under control, healthcare robots would be developed exponentially to decrease the risk of infective infection in hospitals. The healthcare robots market was expected to hit with a significant growth rate in 2020-2021. Owing to production reductions and supply distresses, the healthcare robotics demand was minimally affected in 2020. That would contribute to a decline of BPS 180. But the sector is now starting to see blue sky as the main development region such as China is beating the recession and moving up. Succeeding in the Wake of the Emergency Healthcare robots can play a critical role in the current pandemic by reducing human involvement and shielding health staff from infection. This will involve measuring patients' temperatures, disinfecting equipment, measuring specimen swabs and delivering much-needed psychological assistance to patients in isolation. Researchers are now beginning to illustrate the cyclical aspect of technology right after the recession. The COVID-19 contraction would accelerate labor-replacement automation as business sales see a fall. This might have arrived during the 'cultural shock' as automation eliminates low-skilled jobs. The prospects for healthcare robotics exist in the introduction of smart navigation and the detection of high-risk and highly contaminated environments. Wireless networking systems in healthcare can include drones, telemedicine, and decontamination with AIdriven capabilities. Healthcare robotics should see early acceptance in regions first hit by the infection, offering them an advantage. The companies in China are now doubling their revenue production from the previous years. By the time the pandemic is done, robotics should be distributed through a variety of facilities and programs. The manufacturing will experience reshoring, with decreased reliance on countries such as China, and one way to do so would be by robots that would support the robotics industry. Disinfection Robots UVD Robots, a Danish corporation located in the University of Odense and Blue Ocean Robotics, is a leading manufacturer of disinfectant robots for China in the fight against the spread of the virus. The firm signed a deal with Sunay Healthcare Supplier in February and has since delivered dozens of its self-driving robots to clean hospitals and other places with ultraviolet light. The firm claimed that this reduces the transmission of coronaviruses without exposing medical workers to the possibility of infection. Since then, the firm has marketed robots to locations in more than 50 organizations, extending its services outside China to places in Europe and the United States that are facing epidemic issues. UVD Robots frequently accepts inquiries from outside the hospital and medical institutions, including prison, offices, production floors, department shops, malls, airports, hotels, and restaurants. Dimer, located in Los Angeles, provides its GermFalcon UV-C robotics equipped to clean aircraft and its UVHammer robotic systems for hospitals and complex settings. In mid-January, the organization provided its services to the first three major U.S. airports where Chinese arrivals took place. Xenex confirmed that its LightStriken germ zapping robots became the first hotel in the U.S. to sanitize or clean guest rooms and communal areas at the Westin Houston Medical Center. The technique developed by two epidemiologists in Houston will easily kill pathogens, bacteria and fungi by utilizing strong pulsed xenon ultraviolet radiation. The MTR Company, which runs the Hong Kong subway, has confirmed that it is partnering with Avalon Biomedical (Management) Limited to build the VHP Robot, which stands for the vaporized hydrogen peroxide system. The robot conducts deep cleaning and decontamination in train compartments and stations to secure passengers and workers. More than 30 disinfection robots developed and manufactured by TMiRob, a company in Shanghai, have joined major hospitals in Wuhan, the hub of the novel coronavirus outbreak, to counter the epidemic. The white robot deployed by the firm has a hydrogen peroxide sprayer on its "front" and nine ultraviolet lamps in its "belly" and can conduct various types of disinfection in areas where humans and machines coexist, navigation hardware allows the system to clear hazards independently. Beijing-based robotics firm CloudMinds sent 14 robots to Wuhan, China, to assist with medical treatment in the wake of the coronavirus pandemic. Robots, some of which are more humanoid than others, can scrub and disinfect, distribute medications to patients and check the temperature of patients. CloudMinds contributed robotics to a number of medical institutions in China, including the Wuhan Wuchang Smart Field Clinic, which was converted from the Hong Shan Sports Centre. An Israeli-made AI robotic assistant is being used in hundreds of clinics, community centres, nursing homes and industrial buildings in Asia to eliminate human-to-human interaction as millions of people take precautions for a current coronavirus epidemic worldwide. Israeli company Robotemi, a creator of the Temi robot assistant, claims the device has already been sold to hundreds of locations throughout South East Asia, including China, Japan, South Korea, and Hong Kong. Healthcare Assistance Twelve sets of robots were provided into an intelligent hospital in Wuhan, China by CloudMinds, a supplier to A3, to support the health workers overstressed and threatened. The robots carried out many important tasks including flagging patients who had a temperature, pulse rate, and blood oxygen rates and medicine at the entrance to the field hospital. Such robots have also cleaned and disinfected the hospital areas and performed fitness exercises for patients with the disease. In addition, Chinese researchers modeled the arm of a robot on wheels that can ultrasound, swab in the mouth, and hear the noises produced by a patient's organ normally with a stethoscope. The robot will conduct these activities with cameras in the same space without having to provide medical staff. Professor Zheng Gangtie from the University of Tsinghua developed the device. Unmanned Vehicles Unmanned vehicles and other autonomous robots are deployed in China's virus-affected regions. For instance, Beijing JD Logistics has sent two unmanned L4 class vehicles to Wuhan, and engineers have driven the vehicles remotely through the cloud. Another firm, Idriverplus, has donated an unmanned transportation vehicle to Shanghai and Beijing hospitals. Amazon revealed that it will recruit 100,000 workers to deliver products in vacant warehouses to purchase during the epidemic. The area of e-commerce is now growing with the usage of robots to fill orders and this increase is projected to accelerate as more customers shop digitally, while they remain home more. Teleworking Robots The use of video and audio conference software as companies including Zoom, Microsoft (Skype) and others providing interactive meetings services has expanded with millions of citizens still operating at home because of the state and nation lockdowns. However, businesses of telepresence robotics do have greater interest of their apps, but not for the same purposes. As a consequence of the latest epidemic of coronaviruses, Ava Robotics is building handheld telepresence robots for many years. According to the CEO and cofounder of the firm, the increasing number of hospitals and nursing homes are interested in the robots, which enable the family to talk to patient and older residents on video as a consequence of the policy of 'no visits' and lock-outs in these places. Owing to the need to be simple to use on one end of the line-an aged or ill user in this situation, Ava robots are distinct from the machine enabled video-conferencing device used on a phone or a computer. The company works to make it simple for robotics setup people to click on a connection and talk to the telepresence robot instantaneously. In parallel to the robot's operation, hospitals have utilized Ava robotics, primarily for triaging through the coronavirus. For starters, the robot is configured to remotely test patients with Ava robotics after the initial appointment in one of Boston's largest hospitals. It's also the machines that patients use to reach and depart a position often-rather than people, the system will do it to save the medical equipment because a person has to wear a face mask, shoes and gloves every time instead of human operation. Initially, Czartoski, a practicing neurologist, was utilizing telemedicine in the care of stroke survivors, one of the strongest early lead for telemedicine. According to him, "If I encounter anyone with stroke signs, I will test them with a camera relatively easily to inform them if there is left side fatigue to speech problems, then I will look at the CT scan and the results then make a recommendation for the ER specialist." Virtual visits are growing in the providence. The non-profit health care network conducted nearly 100,000 virtual appointments in 2019. In 2012, the providence carried out a few hundred telemedical visits a year and was rising at a fast pace — from 12,000 in 2016 to 41,000 in 2018 to more than 100,000 last year. That figure does not explicitly reflect the usage of telemedicine in the ICU. Pharmaceutical Assistance Pharmaceutical firms tend to work on viral vaccinations or therapies and automation businesses have developed electronic tools to further simplify manual and replicate processes over the years. The robotics firms also provide options for businesses seeking to combat the COVID-19 virus. The two new modular, ready-for-assay workstations, focused on the Microlab STARlet liquid handling system, were announced recently by Hamilton Group. The latest technologies will help render the SARS-CoV-2 coronavirus that triggers the current COVID-19 quick and highly effective diagnostics, and research-based testing, said the firm. The MagEx STARlet allows the extraction of biological samples from high-performance RNA-based magnetic beads, the PCR Prep STARlet workstation is pre-configured and eligible for sample deployment utilizing recent protocols from the centers. Furthermore, the robotics designed by businesses is actually being employed in the war against coronavirus. ABB robots can be seen in this ABC news story to help a medical laboratory with the development of COVID-19 research kits. Healthcare Robots Market Synopsis The new pandemic will be targeted by health-care device vendors. They will be aligned on developing, unregulated markets with tech firms, stressing the value for national emergencies of healthcare robots. Many industries such as Enterprise Resource Planning (ERP) for higher profit margins are often impacted, in combination with robotics. Medical personnel's are predicted to become a potential phenomenon in patient events, raising the incidence of disease infections. According to the International Federation of Robotics (IFR), the shipments of medical robots already have increased by 50% in 2018. The outcome of coronavirus, however, illustrates some of the essential situations in which robotic systems may be disinfected, tracked, controlled and supplied. The World Robotics study shows that Europe is the most robot-densely inhabited region in the world with an estimated size of 114 units per 10,000 employees. In Edinberg in the United Kingdom, robotic engineers are operating on what they believe is the first safety device to speak to more than one human concurrently. The initiative was planned to support disabled citizens. Scientists say that the discovery will aim to counter potential waves of diseases such as the pandemic. Throughout the United States, the COVID-19 patients are housed at the Providence Community Medical Center throughout Washington in remote locations with two rooms. A robot, which has a microphone, a stethoscope and a camera, is being used by physicians. It helps doctors to interact individually with patients without touching. Disinfection UVD robots in China has been widely searched after as a consequence of the outbreak. The robot built by Denmark's blue ocean robot is ordered by a significant number of hospitals in the world. In the epidemic epicenter of the Wuhan outbreak, these robots played an important part. As time passes, robotics plays a significant role in fighting diseases such as COVID-19, similar to other technologies. Robot technologies will play a significant role not just in aiding patients but also in maintaining the wellbeing of physicians and healthcare staff in the case of an epidemic. The crises are changing views on what is feasible in terms of innovation and strategic intervention on the part of both private and policy players. When the COVID-19 pandemic is finished, the variety of technologies and industries are built into robotics. The virus was a successful chance for businesses to show robotics for public applications. One of the most common is the installation of mobile unmanned ultraviolet (UV) light platforms to disinfect facilities. Danish business UVD Robotics is taking advantage of this potential and is increasing the application of robotics to clean hospitals. The U.S. based Germ Falcon provides identical UV disinfection approaches for airplanes, while Chinese TMiRob deploys UV disinfection robots in Wuhan. "Automation of disinfection is a vital aspect of preserving health and safety and maybe one of the big bright points in reaction to COVID-19. In the near term, the policymakers would need to improve their defense apparatuses as well as the effectiveness of their medical services in order to implement quarantine mandates. The robots should be crucial to doing this by disinfection, tracking and surveillance. Throughout the long run, COVID-19 is contributing to a major reassessment of the worldwide supply chain in production. America's reliance on Chinese imports of essential machinery and drugs is becoming a controversial problem, and policy officials are now seeing the crisis as an incentive to revitalize the drive to re-launch more manufacturing resources on the domestic sector. Whether that turns into more drastic intervention by policymakers to diversify or re-land the output of key products, it may very well bode for the robotics sector, because these reforms will entail substantial rises in CAPEX and efficiency gains in developing countries. Instead of the infectivity of COVID-19, it is better if human-to-human interaction is minimal. Since robots are free for contamination, software companies such as JD.com and others have invested to get more robots marching down the main street to provide medical equipment in healthcare settings. The robots often end up becoming critical when providing vital items to individuals who order and purchase digitally and are lonely at home. Meituan Dianping, a logistics platform, is growing the 'contactless shipping' choices by automated vehicles and robots. Shenzhen-based company Pudu Technology aimed to reduce cross-infection by introducing a robotic home distribution of medications and food. COVID-19 poses a nightmare for robotic manufacturers designing applications for emerging economies in the automotive, construction and supply chain industries. But for vendors targeting markets which that are closer to government, such as safety, education, and protection, this is a great opportunity. Whitton advises that 'the market players create tailored solutions for non-manufacturing use cases or aim to create integrated solutions to allow scale-up in the manufacture of medical supplies. To mobile robotics manufacturers and tech firms pursuing more global markets, this is a perfect chance to demonstrate the role of healthcare robotics in solving national crises as well as alleviating economic shock.
Cleanroom Technology Market to Witness Impressive Growth
The increasing demand in developing economies and the growing focus on energy-efficient cleanrooms are expected to offer significant opportunities for market growth in the coming years. However, the high operational cost associated with the cleanrooms is expected to restrain market growth to a certain extent.  The global health crisis triggered by the COVID-19 pandemic has made it imperative that the pharmaceutical industry moves at a rapid pace alongside researchers, regulators, and contract research companies to develop a diagnosis, treatment, and vaccines. Cleanroom technologies and services play an important role in this scenario to ensure that quality, safety, and efficacy are being maintained.  In the current scenario, the healthcare industry is witnessing an unparalleled demand for diagnostic tests, personal protective equipment (PPE), medical ventilators, and other critical medical supplies. Facing the potentiality of a high risk of infection, healthcare professionals (HCPs) are also facing significant challenges in providing specific and effective care (often remotely).  In Hospital systems are becoming overwhelmed with the rapidly increasing number of COVID-19 patients, which is weighing heavily on the pharmaceutical industry. With the increasing demand for certified products, various quality certifications such as ISO checks and National Safety and Quality Health Standards (NSQHS) have been made mandatory for ensuring that the standards for manufacturing processes and products are being upheld. The quality certifications require products to be processed in a cleanroom environment to ensure minimum possible contamination.   Also, the price per square foot is not the same for ISO 6 and ISO 8 cleanrooms. This is because the amount of air supplied is different in both classes of cleanrooms. The air is 100 times cleaner in an ISO 6 cleanroom than in an ISO 8 cleanroom, thereby doubling the air conditioning capacity of the HVAC systems.   For More Information Download PDF Brochure @ https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=263122482 Cleanrooms are mostly designed according to customer requirements based on product specifications and customer-specific design requirements. However, there are no specific guidelines for cleanroom designs for different application areas or product types. This leads to several challenges for cleanroom manufacturers, as they need to follow different designs every time.  The consumables segment accounted for the larger market share in 2019. The high and growing number of pharmaceutical, biotech, and medical device companies facilitating the use of disposable protective clothing has resulted in the increased adoption of the consumables in the cleanroom technologies market. Also, the large number of RD activities in the healthcare industry is resulting in a stable demand for cleanroom consumables among end-users.   The hardwall cleanrooms segment is expected to witness the highest growth during the forecast period. This is mainly due to the higher demand for hardwall cleanrooms, as they are more design-flexible than standard and softwall cleanrooms, quick and easy to install, freestanding for easy portability, and easy to expand or reconfigure.   This is due to its favorable government regulations, increasing healthcare expenditure, and the growing base of pharma companies in the country, all of which are driving adoption of cleanroom solutions in the Asia Pacific. 
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[October-2021]New Braindump2go MLS-C01 PDF and VCE Dumps[Q158-Q171]
QUESTION 158 A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management. Which combination of AWS services will meet these requirements? A.Amazon EMR for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights B.Amazon Kinesis Data Analytics for data ingestion Amazon EMR for data discovery, enrichment, and transformation Amazon Redshift for querying and analyzing the results in Amazon S3 C.AWS Glue for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights D.AWS Data Pipeline for data transfer AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights Answer: A QUESTION 159 A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers. The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset. Which solution for text extraction and entity detection will require the LEAST amount of effort? A.Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities. B.Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities. C.Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection. D.Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection. Answer: C QUESTION 160 A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow. Which set of actions should the ML specialist take to meet these requirements? A.Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook. B.Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. C.Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook. D.Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions. Answer: D QUESTION 161 A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker. How can the data scientist meet this requirements? A.Call the CreateNotebookInstanceLifecycleConfig API operation B.Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance C.Stop and then restart the SageMaker notebook instance D.Call the UpdateNotebookInstanceLifecycleConfig API operation Answer: C QUESTION 162 A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members' faces are stored in an Amazon S3 bucket. When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3. The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service. How should a machine learning specialist architect the solution to satisfy these requirements? A.Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support. B.Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation. C.Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN. D.Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN. Answer: B QUESTION 163 A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations. Which solution should a machine learning specialist implement to meet these requirements? A.Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. B.Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. C.Build a custom model in Amazon SageMaker to recognize the number of people in an image. Install cameras compatible with Amazon Kinesis Video Streams in the restaurant. Write an AWS Lambda function to take an image. Use the SageMaker endpoint to call the model to count people. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. D.Build a custom model in Amazon SageMaker to recognize the number of people in an image. Deploy AWS DeepLens cameras in the restaurant. Deploy the model to the cameras. Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. Answer: A QUESTION 164 A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible. How can the ML team solve this issue? A.Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances. B.Replace the current endpoint with a multi-model endpoint using SageMaker. C.Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint. D.Increase the cooldown period for the scale-out activity. Answer: A QUESTION 165 A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences. Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time. How can the company implement the testing model with the LEAST amount of operational overhead? A.Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version. B.Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview feature. When the new version of the model is ready for release, change the ALB's routing algorithm to weighted until all users have the updated version. C.Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version. D.Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the current version of the model. Configure the mobile app to use the endpoint URL for users who subscribed to the preview feature and to use the Route 53 record for other users. When the new version of the model is ready for release, add a new model version endpoint to Route 53, and switch the policy to weighted until all users have the updated version. Answer: D QUESTION 166 A company offers an online shopping service to its customers. The company wants to enhance the site's security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested. The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user. Which approach should an ML specialist take to implement the new security feature in the web application? A.Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm. B.Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly. C.Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm. D.Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly. Answer: C QUESTION 167 A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates. Which solution will meet these requirements? A.Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates. B.Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly. C.Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data. D.Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically. Answer: D QUESTION 168 A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network. However, the data science team realizes that individuals outside the VPC can still connect to the notebook instances across the internet. Which set of actions should the data science team take to fix the issue? A.Modify the notebook instances' security group to allow traffic only from the CIDR ranges of the VPC. Apply this security group to all of the notebook instances' VPC interfaces. B.Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:DescribeNotebooklnstance actions from only the VPC endpoints. Apply this policy to all IAM users, groups, and roles used to access the notebook instances. C.Add a NAT gateway to the VPC. Convert all of the subnets where the Amazon SageMaker notebook instances are hosted to private subnets. Stop and start all of the notebook instances to reassign only private IP addresses. D.Change the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC. Answer: B QUESTION 169 A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged. Which implementation will meet these requirements? A.Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3. B.Use SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes. C.Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3. D.Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3. Answer: C QUESTION 170 A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead? A.Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3. B.Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster. C.Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3. D.Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. Answer: C QUESTION 171 A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result. A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days. What is the MOST direct approach to solve this problem within 2 days? A.Train a custom classifier by using Amazon Comprehend. B.Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet. C.Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker. D.Use a built-in seq2seq model in Amazon SageMaker. Answer: B 2021 Latest Braindump2go MLS-C01 PDF and MLS-C01 VCE Dumps Free Share: https://drive.google.com/drive/folders/1eX--L9LzE21hzqPIkigeo1QoAGNWL4vd?usp=sharing