Redhat EX300 : Red Hat Certified Engineer - RHCE Exam

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Exam Number : EX300
Exam Name : Red Hat Certified Engineer - RHCE
Vendor Name : Redhat
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The performance-based Red Hat Certified Engineer (RHCE) test for Red Hat Enterprise Linux 7 (EX300) tests to determine if your knowledge, skill, and ability meet those required of a senior system administrator responsible for Red Hat® Enterprise Linux® systems. Red Hat Certified System Administrator (RHCSA®) certification is required to earn RHCE® certification.

The test based on Red Hat Enterprise Linux 7 is available via on-site and individual exams until July 1, 2020.
An RHCE certification is earned by a Red Hat Certified System Administrator (RHCSA) who has demonstrated the knowledge, skill, and ability required of a senior system administrator responsible for Red Hat Enterprise Linux systems

Local and remote logins
Review methods for accessing the system and engaging Red Hat Support.
File system navigation
Copy, move, create, delete, link, and organize files while working from the Bash shell prompt.
Users and groups
Manage Linux users and groups and administer local password policies.
File permissions
Control access to files and directories using permissions and access control lists (ACLs).
SELinux permissions
Manage the SELinux behavior of a system to keep it secure in case of a network service compromise.
Process management
Evaluate and control processes running on a Red Hat Enterprise Linux system.
Updating software packages
Download, install, update, and manage software packages from Red Hat and yum package repositories.
Creating and mounting file systems
Create and manage disks, partitions, and filesystems from the command line.
Service management and boot troubleshooting
Control and monitor system daemons and troubleshoot the Red Hat Enterprise Linux boot process.
Network configuration
Configure basic IPv4 networking on Red Hat Enterprise Linux systems.
System logging and ntp
Locate and accurately interpret relevant system log files for troubleshooting purposes.
Logical volume management
Create and manage logical volumes from the command line.
Scheduled processes
Schedule tasks to automatically execute in the future.
Mounting network file systems
Use autofs and the command line to mount and unmount network storage with NFS and SMB.
Firewall configuration
Configure a basic firewall.
Virtualization and kickstart
Automate the installation of Red Hat Enterprise Linux on virtual machines with kernel-based virtual machine (KVM) and libvirt.
Managing IPv6 networking
Configure and troubleshoot basic IPv6 networking on Red Hat Enterprise Linux systems.
Configuring link aggregation and bridging
Configure and troubleshoot advanced network interface functionality including bonding, teaming, and local software bridges.
Controlling network port security
Permit and reject access to network services using advanced SELinux and firewalld filtering techniques.
Managing DNS for Servers
Set and verify correct DNS records for systems and configure secure-caching DNS.
Configuring E-mail Delivery
Relay all e-mail sent by the system to a SMTP gateway for central delivery.
Providing block-based storage
Provide and use networked iSCSI block devices as remote disks.
Providing file-based storage
Provide NFS exports and SMB file shares to specific systems and users.
Configuring MariaDB databases
Provide a MariaDB SQL database for use by programs and database administrators.
Providing Apache HTTPD Web Service
Configure Apache HTTPD to provide Transport Layer Security (TLS)-enabled websites and virtual hosts.
Writing Bash scripts
Write simple shell scripts using Bash.
Bash conditionals and control structures
Use Bash conditionals and other control structures to write more sophisticated shell commands and scripts.
Configuring the shell environment
Customize Bash startup and use environment variables, Bash aliases, and Bash functions.

Lab content summary
Managing and troubleshooting systemd services during the boot process
Network configuration and basic troubleshooting
Managing local storage, creating and using file systems
Firewall management with firewalld
Automating installation of Red Hat Enterprise Linux® using kickstart
Manage SELinux settings
Using NFS and Samba shared filesystems
iSCSI initiator and target configuration
Domain Name System (DNS) troubleshooting and caching name server
Providing Network File System (NFS) and Server Message Block (SMB) file servers
Apache HTTPD web server management
MariaDB SQL database configuration
Postfix Simple Mail Transfer Protocol (SMTP) nullclient for servers
Bash scripting for automation

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artificial Intelligence (AI) jobs: 3 in-demand abilities | EX300 test Questions and PDF Download

There are few technologies in exact years more prolific than synthetic Intelligence (AI). From self-riding automobiles to robotic surgery to the applications we use day by day, AI is all around us in each our personal and knowledgeable lives.

international revenues for AI, including application, hardware, and functions, are forecasted to grow sixteen.four p.c year over yr in 2021 to $327.5 billion, in keeping with IDC. 

With all foremost technological breakthroughs comes new challenges, and these deserve to be addressed earlier than AI can carry on its full expertise. To better take note the barriers to AI adoption, a brand new O’Reilly survey seeks to discover what these challenges are and the way businesses can overcome them. no longer pretty, lack of abilities and difficulty hiring are both greatest roadblocks.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

In contrast to O'Reilly's records remaining yr, when company way of life presented the largest bottleneck to AI adoption, this shift is colossal. This trend implies a enhanced normal acceptance of AI – however also a real and protracted AI skill gap. while it’s not excellent that demand for AI expertise has exceeded provide, it’s important to have in mind which particular potential and expert titles are most essential to AI adoption. To discover what those have been, we requested the technologists definitely the usage of AI in construction. here are the three areas know-how professionals should boost in the period of AI.

1. ML modeling and statistics science advantage

The percentage of respondents with AI products in creation over the final yr is flat when compared with 2020, and even 2019, which may also reflect the AI abilities gap. in accordance with O’Reilly’s survey, respondents feel the abilities scarcity most acutely in the areas of computer discovering (ML) modeling and statistics science (52 percent), knowing business use circumstances (forty nine percent), and records engineering (42 p.c). The emphasis on realizing use circumstances also indicates that respondents recognize AI is not a one-measurement-suits-all solution, which is essential for industries reminiscent of healthcare, the place domain advantage is vitally important.

On the other end of the spectrum, hyperparameter tuning (2 percent) wasn’t regarded an issue, which may mirror the success of computerized tools for building fashions. What’s more concerning is that workflow reproducibility (three %) was one of these low precedence. Being capable of reproduce experimental effects is important to any science, and it’s a well-recognized problem in AI. placing models into construction that work differently than meant can latest genuine issues for organizations. whereas technologists should focus on building the advantage they deserve to put themselves in the most suitable position to get hired for AI-concentrated roles, it’s critical to take note other crucial challenges they can be confronted with on the job.

[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]

2. equipment of the alternate: scikit-learn and TensorFlow

For these developing AI potential, gaining knowledge of to make use of tools with computerized add-ons is a very good jumping-off aspect.

Respondents with mature practices naturally have their favourite equipment. Scikit-be taught and TensorFlow desirable the listing (both used by way of sixty five p.c of respondents), with PyTorch not a long way at the back of. When requested which equipment they deliberate to contain over the subsequent yr, roughly half of the respondents answered with mannequin monitoring and mannequin visualization. models require consistent tweaking for explanations ranging from changing human conduct to changing datasets, and devoid of updating, can become ineffective and even detrimental. The ability to monitor performance may be increasingly essential as organizations grow more reliant on AI.

As questions of ethics and bias circulation to the forefront, modelling mannequin conduct will develop into even more essential for addressing these issues abruptly. while previous stage respondents’ answers were identical, there was one large difference, and that was a improved reliance on partnering with companies that contain AutoML. This makes loads of feel. For these developing AI expertise, learning to use tools with computerized accessories is a good leaping-off factor. 

[ Read also: 6 misconceptions about AIOps, explained. ]

three. statistics training and assortment

while it's encouraging that 18 p.c of respondents mentioned concern with information excellent, here's nowhere near high satisfactory. A excessive number of blunders exist in publicly obtainable data units, affecting  the accuracy of AI outcomes.

The title of a fresh paper says it all: “all and sundry desires to do the mannequin work, not the data work.” This paper discusses how error in statistics cascade into dangerous effects, primarily in high-chance methods, reminiscent of clinical techniques and self sustaining motors. It laments that academic classes train students how to build fashions however ignore the difficulty of excellent information, reinforcing the idea that model building is greater enjoyable and glamorous.

greater on synthetic intelligence

It’s no accident that the O'Reilly survey showed that forty six % of the respondents weren’t using edition manage tools for statistics; the capacity to track changes to statistics and reconstruct the statistics used to train any mannequin is an essential to conserving facts best. almost 10 years in the past, former Chief information Scientist of the united states office of Science and expertise coverage DJ Patil mentioned, “eighty % of the work in any information undertaking is cleaning the information.” That’s nonetheless proper, and the biggest scarcity the industry faces may be people inclined to clear the statistics.

The AI expertise gap has existed for some time, however as the most gigantic bar to wider adoption of the technology, it’s time to get fascinated about solving the difficulty. education is vigour, and arming tech experts with the advantage they need to comfy AI jobs is the most effective means to conquer this hurdle. by using specializing in probably the most in-demand knowledge, most regular tools, and valuable thoughts, we will start to close the gap and definitely delivery to form the next era of AI.

[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]

Mike Loukides is vice president of content material method for O'Reilly Media, Inc. he's edited many enormously viewed books on technical subjects that do not contain home windows programming. he is principally drawn to programming languages, Unix and what passes for Unix these days, and device and community administration.

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